ORCID Profile
0000-0002-6221-1175
Current Organisations
University of New South Wales
,
University of Southern Queensland
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Publisher: MDPI AG
Date: 02-2022
DOI: 10.3390/BUILDINGS12020156
Abstract: Automatic detection of corrosion and associated damages to civil infrastructures such as bridges, buildings, and roads, from aerial images captured by an Unmanned Aerial Vehicle (UAV), helps one to overcome the challenges and shortcomings (objectivity and reliability) associated with the manual inspection methods. Deep learning methods have been widely reported in the literature for civil infrastructure corrosion detection. Among them, convolutional neural networks (CNNs) display promising applicability for the automatic detection of image features less affected by image noises. Therefore, in the current study, we propose a modified version of deep hierarchical CNN architecture, based on 16 convolution layers and cycle generative adversarial network (CycleGAN), to predict pixel-wise segmentation in an end-to-end manner using the images of Bolte Bridge and sky rail areas in Victoria (Melbourne). The convolutedly designed model network proposed in the study is based on learning and aggregation of multi-scale and multilevel features while moving from the low convolutional layers to the high-level layers, thus reducing the consistency loss in images due to the inclusion of CycleGAN. The standard approaches only use the last convolutional layer, but our proposed architecture differs from these approaches and uses multiple layers. Moreover, we have used guided filtering and Conditional Random Fields (CRFs) methods to refine the prediction results. Additionally, the effectiveness of the proposed architecture was assessed using benchmarking data of 600 images of civil infrastructure. Overall, the results show that the deep hierarchical CNN architecture based on 16 convolution layers produced advanced performances when evaluated for different methods, including the baseline, PSPNet, DeepLab, and SegNet. Overall, the extended method displayed the Global Accuracy (GA) Class Average Accuracy (CAC) mean Intersection Of the Union (IOU) Precision (P) Recall (R) and F-score values of 0.989, 0.931, 0.878, 0.849, 0.818 and 0.833, respectively.
Publisher: MDPI AG
Date: 06-05-2022
DOI: 10.3390/BUILDINGS12050605
Abstract: Disposal of municipal solid waste (MSW) is one of the significant global issues that is more evident in developing nations. One of the key methods for disposing of the MSW is locating, assessing, and planning for landfill sites. Faisalabad is one of the largest industrial cities in Pakistan. It has many sustainability challenges and planning problems, including MSW management. This study uses Faisalabad as a case study area and humbly attempts to provide a framework for identifying and ranking landfill sites and addressing MSW concerns in Faisalabad. This method can be extended and applied to similar industrial cities. The landfill sites were identified using remote sensing (RS) and geographic information system (GIS). Multiple datasets, including normalized difference vegetation, water, and built-up areas indices (NDVI, NDWI, and NDBI) and physical factors including water bodies, roads, and the population that influence the landfill site selection were used to identify, rank, and select the most suitable site. The target area was distributed into 9 Thiessen polygons and ranked based on their favorability for the development and expansion of landfill sites. 70% of the area was favorable for developing and expanding landfill sites, whereas 30% was deemed unsuitable. Polygon 6, having more vegetation, a smaller population, and built-up areas was declared the best region for developing landfill sites and expansion as per rank mean indices and standard deviation (SD) of RS and vector data. The current study provides a reliable integrated mechanism based on GIS and RS that can be implemented in similar study areas and expanded to other developing countries. Accordingly, urban planning and city management can be improved, and MSW can be managed with dexterity.
Publisher: MDPI AG
Date: 29-09-2022
DOI: 10.3390/BUILDINGS12101563
Abstract: The public–private partnership (PPP) is a potential procurement strategy for delivering complex construction projects. However, implementing PPPs has not been explored extensively in developing countries like Pakistan. A performance framework is developed in this study to evaluate the application of PPP projects based on 10 key performance indicators (KPIS) and 41 performance measures (PMS). This framework was reviewed by experts for coverage and relevance, then validated through two case studies involving road construction. A triangulation approach was adopted to collect the relevant data through multiparty focus group sessions, archives, and site observations, which enhances the reliability of the data. Results showed there is a difference in performance for six KPIS, but similar practices were reported for four KPIS. The developed performance evaluation framework (PEF) for PPP projects is suitable for developing countries transitioning toward adopting this procurement strategy.
Publisher: Elsevier BV
Date: 12-2021
Publisher: MDPI AG
Date: 10-09-2021
DOI: 10.3390/SU131810164
Abstract: Concrete is an important construction material. Its characteristics depend on the environmental conditions, construction methods, and mix factors. Working with concrete is particularly tricky in a hot climate. This study predicts the properties of concrete in hot conditions using the case study of Rawalpindi, Pakistan. In this research, variable casting temperatures, design factors, and curing conditions are investigated for their effects on concrete characteristics. For this purpose, water–cement ratio (w/c), in-situ concrete temperature (T), and curing methods of the concrete are varied, and their effects on pulse velocity (PV), compressive strength (fc), depth of water penetration (WP), and split tensile strength (ft) were studied for up to 180 days. Quadratic regression and artificial neural network (ANN) models have been formulated to forecast the properties of concrete in the current study. The results show that T, curing period, and moist curing strongly influence fc, ft, and PV, while WP is adversely affected by T and moist curing. The ANN model shows better results compared to the quadratic regression model. Furthermore, a combined ANN model of fc, ft, and PV was also developed that displayed higher accuracy than the in idual ANN models. These models can help construction site engineers select the appropriate concrete parameters when concreting under hot climates to produce durable and long-lasting concrete.
Publisher: MDPI AG
Date: 30-08-2023
DOI: 10.3390/BUILDINGS13092212
Abstract: The traditional methods of marking construction site layouts using manual techniques such as chalk lines are prone to human errors, resulting in discrepancies between blueprints and actual layouts. This has serious implications for project delivery, construction, costs and, eventually, project success. However, this issue can be resolved through autonomous robots and construction automation in line with Industry 4.0 and 5.0 goals. Construction automation enables workers to concentrate on the construction phase and not worry about manual site markups. This leads to an enhancement in their productivity. This study aims to improve the floor layout printing technique by introducing a framework that integrates building information modeling (BIM) and the Internet of Things (IoT), i.e., BIM–IoT and autonomous mobile robots (AMR). The development process focuses on three key components: a marking tool, an IoT-based AMR and BIM. The BIM-based tools extract and store coordinates on the cloud platform. The AMR, developed using ESP32 and connected to the Google Firestore cloud platform, leverages IoT technology to retrieve the data and draw site layout lines accordingly. Further, this research presents a prototype of an automated robot capable of accurately printing construction site layouts. A design science research (DSR) method is employed in this study that includes a comprehensive review of the existing literature and usage of AMRs in construction layout printing. Subsequently building upon the extant literature, an AMR is developed and experiments are conducted to evaluate the system’s performance. The experiment reveals that the system’s precision falls within a range of ±15 mm and its angle accuracy is within ±4 degrees. Integrating robotic automation, IoT and BIM technologies enhances the efficiency and precision of construction layout printing. The findings provide insights into the potential benefits of deploying AMRs in construction projects, reducing site layout errors and improving construction productivity. This study also adds to the body of knowledge around construction automation in line with Industry 4.0 and 5.0 endeavors.
Publisher: MDPI AG
Date: 26-03-2020
DOI: 10.3390/BDCC4020004
Abstract: Big data is the concept of enormous amounts of data being generated daily in different fields due to the increased use of technology and internet sources. Despite the various advancements and the hopes of better understanding, big data management and analysis remain a challenge, calling for more rigorous and detailed research, as well as the identifications of methods and ways in which big data could be tackled and put to good use. The existing research lacks in discussing and evaluating the pertinent tools and technologies to analyze big data in an efficient manner which calls for a comprehensive and holistic analysis of the published articles to summarize the concept of big data and see field-specific applications. To address this gap and keep a recent focus, research articles published in last decade, belonging to top-tier and high-impact journals, were retrieved using the search engines of Google Scholar, Scopus, and Web of Science that were narrowed down to a set of 139 relevant research articles. Different analyses were conducted on the retrieved papers including bibliometric analysis, keywords analysis, big data search trends, and authors’ names, countries, and affiliated institutes contributing the most to the field of big data. The comparative analyses show that, conceptually, big data lies at the intersection of the storage, statistics, technology, and research fields and emerged as an amalgam of these four fields with interlinked aspects such as data hosting and computing, data management, data refining, data patterns, and machine learning. The results further show that major characteristics of big data can be summarized using the seven Vs, which include variety, volume, variability, value, visualization, veracity, and velocity. Furthermore, the existing methods for big data analysis, their shortcomings, and the possible directions were also explored that could be taken for harnessing technology to ensure data analysis tools could be upgraded to be fast and efficient. The major challenges in handling big data include efficient storage, retrieval, analysis, and visualization of the large heterogeneous data, which can be tackled through authentication such as Kerberos and encrypted files, logging of attacks, secure communication through Secure Sockets Layer (SSL) and Transport Layer Security (TLS), data imputation, building learning models, iding computations into sub-tasks, checkpoint applications for recursive tasks, and using Solid State Drives (SDD) and Phase Change Material (PCM) for storage. In terms of frameworks for big data management, two frameworks exist including Hadoop and Apache Spark, which must be used simultaneously to capture the holistic essence of the data and make the analyses meaningful, swift, and speedy. Further field-specific applications of big data in two promising and integrated fields, i.e., smart real estate and disaster management, were investigated, and a framework for field-specific applications, as well as a merger of the two areas through big data, was highlighted. The proposed frameworks show that big data can tackle the ever-present issues of customer regrets related to poor quality of information or lack of information in smart real estate to increase the customer satisfaction using an intermediate organization that can process and keep a check on the data being provided to the customers by the sellers and real estate managers. Similarly, for disaster and its risk management, data from social media, drones, multimedia, and search engines can be used to tackle natural disasters such as floods, bushfires, and earthquakes, as well as plan emergency responses. In addition, a merger framework for smart real estate and disaster risk management show that big data generated from the smart real estate in the form of occupant data, facilities management, and building integration and maintenance can be shared with the disaster risk management and emergency response teams to help prevent, prepare, respond to, or recover from the disasters.
Publisher: MDPI AG
Date: 18-01-2023
DOI: 10.3390/SU15031866
Abstract: Artificial Intelligence (AI) and allied disruptive technologies have revolutionized the scientific world. However, civil engineering, in general, and infrastructure management, in particular, are lagging behind the technology adoption curves. Crack identification and assessment are important indicators to assess and evaluate the structural health of critical city infrastructures such as bridges. Historically, such critical infrastructure has been monitored through manual visual inspection. This process is costly, time-consuming, and prone to errors as it relies on the inspector’s knowledge and the gadgets’ precision. To save time and cost, automatic crack and damage detection in bridges and similar infrastructure is required to ensure its efficacy and reliability. However, an automated and reliable system does not exist, particularly in developing countries, presenting a gap targeted in this study. Accordingly, we proposed a two-phased deep learning-based framework for smart infrastructure management to assess the conditions of bridges in developing countries. In the first part of the study, we detected cracks in bridges using the dataset from Pakistan and the online-accessible SDNET2018 dataset. You only look once version 5 (YOLOv5) has been used to locate and classify cracks in the dataset images. To determine the main indicators (precision, recall, and mAP (0.5)), we applied each of the YOLOv5 s, m, and l models to the dataset using a ratio of 7:2:1 for training, validation, and testing, respectively. The mAP (Mean average precision) values of all the models were compared to evaluate their performance. The results show mAP values for the test set of the YOLOv5 s, m, and l as 97.8%, 99.3%, and 99.1%, respectively, indicating the superior performance of the YOLOv5 m model compared to the two counterparts. In the second portion of the study, segmentation of the crack is carried out using the U-Net model to acquire their exact pixels. Using the segmentation mask allocated to the attribute extractor, the pixel’s width, height, and area are measured and visualized on scatter plots and Boxplots to segregate different cracks. Furthermore, the segmentation part validated the output of the proposed YOLOv5 models. This study not only located and classified the cracks based on their severity level, but also segmented the crack pixels and measured their width, height, and area per pixel under different lighting conditions. It is one of the few studies targeting low-cost health assessment and damage detection in bridges of developing countries that otherwise struggle with regular maintenance and rehabilitation of such critical infrastructure. The proposed model can be used by local infrastructure monitoring and rehabilitation authorities for regular condition and health assessment of the bridges and similar infrastructure to move towards a smarter and automated damage assessment system.
Publisher: Elsevier BV
Date: 09-2021
Publisher: Hindawi Limited
Date: 28-03-2021
DOI: 10.1002/INT.22422
Publisher: Elsevier BV
Date: 08-2023
Publisher: Elsevier BV
Date: 02-2021
Publisher: MDPI AG
Date: 05-06-2022
DOI: 10.3390/BUILDINGS12060766
Abstract: Construction processes are complex and dynamic. Like its other components, the construction supply chain (CSC) involves multiple stakeholders requiring varying levels of information sharing. In addition, the intensity and ersity of information in CSCs require dexterous management. Studies reveal that information complexity can be reduced using collaborative technologies (CTs). However, the barriers to information management (IM) hinder the CTs’ adoption process and cause complexity in CSCs. This research identifies barriers to IM and factors affecting the adoption of CTs in developing countries. In order to understand and address complexity, the system dynamics (SD) approach is adopted in this study. The aim is to investigate if SD can reduce information complexity using CTs. Causal loop diagrams (CLDs) were developed to understand the relationship between the IM barriers and CT adoption factors. The SD model, when simulated, highlighted three main components, i.e., complexity, top management support, and trust and cooperation, among others, as factors affecting the adoption of CTs. Addressing these factors will reduce information complexity and result in better IM in construction projects.
Publisher: MDPI AG
Date: 20-09-2022
DOI: 10.3390/SU141911815
Abstract: In the globalized world, one significant challenge for organizations is minimizing risk by building resilient supply chains (SCs). This is important to achieve a competitive advantage in an unpredictable and ever-changing environment. However, the key enablers of such resilient and sustainable supply chain management are less explored in construction projects. Therefore, the present research aims to determine the causality among the crucial drivers of resilient and sustainable supply chain management (RSSCM) in construction projects. Based on the literature review, 12 enablers of RSSCM were shortlisted. Using the systems thinking (ST) approach, this article portrays the interrelation between the 12 shortlisted resilience enablers crucial for sustainability in construction projects. The causality and interrelationships among identified enablers in the developed causal loop diagram (CLD) show their dynamic interactions and impacts within the RSSCM system. Based on the results of this study, agility, information sharing, strategic risk planning, corporate social responsibility, and visibility are the key enablers for the RSSCM. The findings of this research will enable the construction managers to compare different SCs while understanding how supply chain characteristics increase or decrease the durability and ultimately affect the exposure to risk in the construction SCs.
Publisher: WORLD SCIENTIFIC
Date: 05-2019
Publisher: Springer Singapore
Date: 19-12-2017
Publisher: American Society of Civil Engineers (ASCE)
Date: 11-2018
Publisher: MDPI AG
Date: 23-08-2023
DOI: 10.3390/BUILDINGS13092143
Abstract: The healthcare industry significantly impacts the environment due to its high usage of energy and natural resources and the associated waste generation. This study applied a cradle-to-grave Life Cycle Sustainability Assessment (LCSA) approach to assess the environmental and social life cycles of public hospitals. One hundred twenty-four public hospitals were selected for the current study their sustainability performance was compared with those certified by Leadership in Energy and Environmental Design (LEED). The comparison revealed several factors contributing to the poor sustainability performance of public hospitals. These include inadequate management, substandard planning, political interference, insufficient staffing and funding, high energy consumption, high expenses, inconsistent healthcare policies, and conventional building designs. System thinking was leveraged, and a causal loop diagram (CLD) was developed to visualize the interdependency of the identified indicators of LCSA. Based on the findings of the study, a policy framework is proposed to guide the development of sustainable healthcare buildings. The framework includes using eco-friendly materials and techniques in construction, harnessing solar energy, improving hospital management practices, promoting public awareness about sustainability, conserving energy and water, and adopting sustainable waste management and transportation. Additionally, it emphasizes addressing social issues such as improving indoor air quality, thermal comfort, lighting, acoustics, patient safety, and security and ensuring that healthcare services are accessible and affordable. This study contributes to the literature on sustainable healthcare buildings by providing a practical policy framework for achieving sustainability in the healthcare sector.
Publisher: MDPI AG
Date: 20-03-2020
DOI: 10.3390/EN13061480
Abstract: Rising demand and limited production of electricity are instrumental in spreading the awareness of cautious energy use, leading to the global demand for energy-efficient buildings. This compels the construction industry to smartly design and effectively construct these buildings to ensure energy performance as per design expectations. However, the research tells a different tale: energy-efficient buildings have performance issues. Among several reasons behind the energy performance gap, occupant behavior is critical. The occupant behavior is dynamic and changes over time under formal and informal influences, but the traditional energy simulation programs assume it as static throughout the occupancy. Effective behavioral interventions can lead to optimized energy use. To find out the energy-saving potential based on simulated modified behavior, this study gathers primary building and occupant data from three energy-efficient office buildings in major cities of Pakistan and categorizes the occupants into high, medium, and low energy consumers. Additionally, agent-based modeling simulates the change in occupant behavior under the direct and indirect interventions over a three-year period. Finally, energy savings are quantified to highlight a 25.4% potential over the simulation period. This is a unique attempt at quantifying the potential impact on energy usage due to behavior modification which will help facility managers to plan and execute necessary interventions and software experts to develop effective tools to model the dynamic usage behavior. This will also help policymakers in devising subtle but effective behavior training strategies to reduce energy usage. Such behavioral retrofitting comes at a much lower cost than the physical or technological retrofit options to achieve the same purpose and this study establishes the foundation for it.
Publisher: SAGE Publications
Date: 10-06-2019
Abstract: Risk is inherent in construction projects and managed through contingency. Dynamic management of contingency escrow accounts during project execution poses decision-making challenges. Project managers use key performance indicators (KPIs) for contingency release decisions. However, their subjective mental models influence risk perception, exacerbating the decision-making dilemma. This research integrates project KPIs with future risk perception to develop a mathematical model for facilitating such decision making. Results suggest timely completion, project cost, stakeholder satisfaction, quality and safety as top KPIs, and the influence of managerial pressure as a significant decision contributor. The proposed model helps project managers in dynamic decision making for cost contingency.
Publisher: MDPI AG
Date: 06-06-2022
DOI: 10.3390/W14111824
Abstract: Owing to the extensive global dependency on groundwater and associated increasing water demand, the global groundwater level is declining rapidly. In the case of Islamabad, Pakistan, the groundwater level has lowered five times over the past five years due to extensive pumping by various departments and residents to meet the local water requirements. To address this, water reservoirs and sources need to be delineated, and potential recharge zones are highlighted to assess the recharge potential. Therefore, the current study utilizes an integrated approach based on remote sensing (RS) and GIS using the influence factor (IF) technique to delineate potential groundwater recharge zones in Islamabad, Pakistan. Soil map of Pakistan, Landsat 8TM satellite data, digital elevation model (ASTER DEM), and local geological map were used in the study for the preparation of thematic maps of 15 key contributing factors considered in this study. To generate a combined groundwater recharge map, rate and weightage values were assigned to each factor representing their mutual influence and recharge capabilities. To analyze the final combined recharge map, five different assessment analogies were used in the study: poor, low, medium, high, and best. The final recharge potential map for Islamabad classifies 15% (136.8 km2) of the region as the “best” zone for extracting groundwater. Furthermore, high, medium, low, and poor ranks were assigned to 21%, 24%, 27%, and 13% of the region with respective areas of 191.52 km2, 218.88 km2, 246.24 km2, and 118.56 km2. Overall, this research outlines the best to least favorable zones in Islamabad regarding groundwater recharge potentials. This can help the authorities devise mitigation strategies and preserve the natural terrain in the regions with the best groundwater recharge potential. This is aligned with the aims of the interior ministry of Pakistan for constructing small reservoirs and ponds in the existing natural streams and installing recharging wells to maintain the groundwater level in cities. Other countries can expand upon and adapt this study to delineate local groundwater recharge potentials.
Publisher: MDPI AG
Date: 18-07-2022
DOI: 10.3390/BUILDINGS12071037
Abstract: The progress monitoring (PM) of construction projects is an essential aspect of project control that enables the stakeholders to make timely decisions to ensure successful project delivery, but ongoing practices are largely manual and document-centric. However, the integration of technologically advanced tools into construction practices has shown the potential to automate construction PM (CPM) using real-time data collection, analysis, and visualization for effective and timely decision making. In this study, we assess the level of automation achieved through various methods that enable automated computer vision (CV)-based CPM. A detailed literature review is presented, discussing the complete process of CV-based CPM based on the research conducted between 2011 and 2021. The CV-based CPM process comprises four sub-processes: data acquisition, information retrieval, progress estimation, and output visualization. Most techniques encompassing these sub-processes require human intervention to perform the desired tasks, and the inter-connectivity among them is absent. We conclude that CV-based CPM research is centric on resolving technical feasibility studies using image-based processing of site data, which are still experimental and lack connectivity to its applications for construction management. This review highlighted the most efficient techniques involved in the CV-based CPM and accentuated the need for the inter-connectivity between sub-processes for an effective alternative to traditional practices.
Publisher: MDPI AG
Date: 09-2021
DOI: 10.3390/ARCHITECTURE1010003
Abstract: With the boom of industry 4.0 technologies and their adoption in the built environment (BE), conceptual frameworks (CFs) are increasingly developed to facilitate the adoption. It is becoming increasingly important to develop a standard or guide for new BE research entrants and aspirants who want to conduct a systematic literature review and develop such CFs. However, they struggle to find a standard and reproducible procedure to conduct systematic literature reviews and develop CFs successfully. Accordingly, the current study based on requests and inspirations from nascent BE researchers presents guidelines about conducting such studies. A simplistic yet reproducible methodology is presented that can be followed by BE research aspirants to produce high-quality and well-organized review articles and develop a CF. Using an ex le of big data-based disaster management in smart cities, the current study provides a practical ex le of conducting a systematic literature review and developing a CF. It is expected that this research will serve as a baseline for conducting systematic studies in the BE field that other fields of science can adopt. Further, it is expected that this study will motivate the nascent BE researchers to conduct systematic reviews and develop associated CFs with confidence. This will pave the way for adopting disruptive technologies and innovative tools in the BE in line with industry 4.0 requirements.
Publisher: MDPI AG
Date: 26-10-2021
DOI: 10.3390/APP112110034
Abstract: Groundwater is one of the primary sources for the daily water requirements of the masses, but it is subjected to contamination due to the pollutants, such as nitrate, percolating through the soil with water. Especially in built-up areas, groundwater vulnerability and contamination are of major concern, and require appropriate consideration. The present study develops a novel framework for assessing groundwater nitrate contamination risk for the area along the Karakoram Highway, which is a part of the China Pakistan Economic Corridor (CPEC) route in northern Pakistan. A groundwater vulnerability map was prepared using the DRASTIC model. The nitrate concentration data from a previous study were used to formulate the nitrate contamination map. Three machine learning (ML) models, i.e., Support Vector Machine (SVM), Multivariate Discriminant Analysis (MDA), and Boosted Regression Trees (BRT), were used to analyze the probability of groundwater contamination incidence. Furthermore, groundwater contamination probability maps were obtained utilizing the ensemble modeling approach. The models were calibrated and validated through calibration trials, using the area under the receiver operating characteristic curve method (AUC), where a minimum AUC threshold value of 80% was achieved. Results indicated the accuracy of the models to be in the range of 0.82–0.87. The final groundwater contamination risk map highlights that 34% of the area is moderately vulnerable to groundwater contamination, and 13% of the area is exposed to high groundwater contamination risk. The findings of this study can facilitate decision-making regarding the location of future built-up areas properly in order to mitigate the nitrate contamination that can further reduce the associated health risks.
Publisher: MDPI AG
Date: 03-09-2018
DOI: 10.3390/SU10093142
Abstract: Real estate needs to improve its adoption of disruptive technologies to move from traditional to smart real estate (SRE). This study reviews the adoption of disruptive technologies in real estate. It covers the applications of nine such technologies, hereby referred to as the Big9. These are: drones, the internet of things (IoT), clouds, software as a service (SaaS), big data, 3D scanning, wearable technologies, virtual and augmented realities (VR and AR), and artificial intelligence (AI) and robotics. The Big9 are examined in terms of their application to real estate and how they can furnish consumers with the kind of information that can avert regrets. The review is based on 213 published articles. The compiled results show the state of each technology’s practice and usage in real estate. This review also surveys dissemination mechanisms, including smartphone technology, websites and social media-based online platforms, as well as the core components of SRE: sustainability, innovative technology and user centredness. It identifies four key real estate stakeholders—consumers, agents and associations, government and regulatory authorities, and complementary industries—and their needs, such as buying or selling property, profits, taxes, business and/or other factors. Interactions between these stakeholders are highlighted, and the specific needs that various technologies address are tabulated in the form of a what, who and how analysis to highlight the impact that the technologies have on key stakeholders. Finally, stakeholder needs as identified in the previous steps are matched theoretically with six extensions of the traditionally accepted technology adoption model (TAM), paving the way for a smoother transition to technology-based benefits for consumers. The findings pertinent to the Big9 technologies in the form of opportunities, potential losses and exploitation levels (OPLEL) analyses highlight the potential utilisation of each technology for addressing consumers’ needs and minimizing their regrets. Additionally, the tabulated findings in the form of what, how and who links the Big9 technologies to core consumers’ needs and provides a list of resources needed to ensure proper information dissemination to the stakeholders. Such high-quality information can bridge the gap between real estate consumers and other stakeholders and raise the state of the industry to a level where its consumers have fewer or no regrets. The study, being the first to explore real estate technologies, is limited by the number of research publications on the SRE technologies that has been compensated through incorporation of online reports.
Publisher: MDPI AG
Date: 15-11-2021
DOI: 10.3390/SU132212583
Abstract: Water scarcity has become a major problem for many countries, resulting in declining water supply and creating a need to find alternative solutions. One potential solution is rainwater harvesting (RwH), which allows rainwater to be stored for human needs. This study develops an RwH assessment system through building information modeling (BIM). For this purpose, a hydrological study of Cfa-type climate cities is conducted with the ex le of Islamabad, Pakistan. The monthly rainfall data of three sites were assessed to determine the volume of the accumulated rainwater and its potential to meet human needs. The average number of people living in a house is taken as the household number. Household number or of the number of employees working at a small enterprise, roofing material, and rooftop area are used as the key parameters for pertinent assessment in the BIM. The data simulated by BIM highlight the RwH potential using five people per house as the occupancy and a 90 m2 rooftop area for residential buildings or small enterprises as parameters. The results show that the selected sites can collect as much as 8,190 L/yr of rainwater (48 L erson/day) to 103,300 L/yr of rainwater (56 L erson/day). This much water is enough to fulfill the daily demands of up to five people. Therefore, it is established that the study area has an RwH potential that is able to meet the expected demands. This study presents a baseline approach for RwH to address water scarcity issues for residential buildings and factories of the future.
Publisher: MDPI AG
Date: 03-06-2022
DOI: 10.3390/BUILDINGS12060760
Abstract: The China Pakistan Economic Corridor (CPEC) project was signed between China and Pakistan in the year 2013. This mega project connects the two countries to enhance their economic ties and give them access to international markets. The initial investment for the project was $46 billion with a tentative duration of fifteen years. Being an extensive project in terms of cost and duration, many factors and risks affect its performance. This study aims to investigate the effects of political (PR), social safety (SR), and legal risks (LR) on the project performance (PP) of the CPEC. It further investigates the significance of the host country’s attitude towards foreigners (HCA). A research framework consisting of PR, SR, and LR as independent variables, PP as the dependent variable, and HCA as moderator is formulated and tested in the current study. In this quantitative study, the Likert scale is used to measure the impact of the assessed risks. A questionnaire survey is used as a data collection tool to collect data and test the research framework and associated hypotheses. The partial least square structural equation modeling (PLS-SEM) is used to perform the empirical test for validation of the study, with a dataset of 99 responses. The empirical investigation finds a negative relationship between PR, SR, LR, and PP. It is concluded that PR, SR, and LR negatively influence the PP of CPEC. Furthermore, HCA negatively moderates the PR, LR, and PP of CPEC. In contrast, the value of SR and PP is positive in the presence of the positive HCA.
Publisher: MDPI AG
Date: 18-08-2021
DOI: 10.3390/SU13169270
Abstract: Building Information Modeling (BIM) is recognized as one of the most significant technological breakthroughs in the Architecture, Engineering, and Construction (AEC) industry. The pace of implementation of BIM in AEC has increased during the past decade with an enhanced focus on sustainable construction. However, BIM implementation lags its potential because of several factors such as readiness issues, lack of previous experience in BIM, and lack of market demand for BIM. To evaluate and solve these issues, understanding the current BIM implementation in construction organizations is required. Motivated by this need, the main objective of this study is to propose a tool for the measurement of BIM implementation levels within an organization. Various sets of indexes are developed based on their pertinent Critical Success Factors (CSFs). A detailed literature review followed by a questionnaire survey involving 99 respondents is conducted, and results are analyzed to formulate a BIMp-Chart to calculate and visualize the BIM implementation level of an organization. Subsequently, the applicability of the BIMp-Chart is assessed by comparing and analyzing datasets of four organizations from different regions, including Qatar, Portugal, and Egypt, and a multinational organization to develop a global measurement tool. Through measuring and comparing BIM implementation levels, the BIMp-Chart can help the practitioners identify the implementation areas in an organization for proper BIM implementation. This study helps understand the fundamental elements of BIM implementation and provides a decision support system for construction organizations to devise proper strategies for the effectual management of the BIM implementation process.
Publisher: Emerald
Date: 04-07-2023
DOI: 10.1108/BEPAM-02-2023-0040
Abstract: The purpose of this study is to investigate the current construction progress monitoring (CPM) process in relation to the contractual obligations, how project management teams carry out this activity in the field and why teams continue to adopt the current method. The study aims to provide a comprehensive understanding of the current monitoring process and its effectiveness, identify any shortcomings and propose recommendations for improvements that can lead to better project outcomes. The study conducted semi-structured interviews with 28 construction management practitioners to explore their views on contractual requirements, traditional progress monitoring practices and advanced monitoring methods. Thematic analysis was used to identify existing processes, practices and incentives for advanced monitoring. Standard construction contracts mandate current progress monitoring practices, which often rely on manual, document-centric and labor-intensive methods, leading to slow and erroneous progress reporting and project delays. Key barriers to adopting advanced tools include rigid contractual clauses, lack of incentives and the absence of reliable automated tools. A holistic automated approach that covers the entire CPM process, from planning to claim management, is needed as a viable alternative to traditional practices. The study's findings can inform researchers, stakeholders and decision-makers about the existing monitoring practices and contribute to enhancing project management practices. The study identified contractually mandated progress monitoring processes, traditional methods of collecting, transferring, analyzing and dispensing progress-related information and potential incentives and points of departure towards technologically advanced methods.
Publisher: MDPI AG
Date: 06-02-2022
DOI: 10.3390/BDCC6010018
Abstract: Big data have become an integral part of various research fields due to the rapid advancements in the digital technologies available for dealing with data. The construction industry is no exception and has seen a spike in the data being generated due to the introduction of various digital disruptive technologies. However, despite the availability of data and the introduction of such technologies, the construction industry is lagging in harnessing big data. This paper critically explores literature published since 2010 to identify the data trends and how the construction industry can benefit from big data. The presence of tools such as computer-aided drawing (CAD) and building information modelling (BIM) provide a great opportunity for researchers in the construction industry to further improve how infrastructure can be developed, monitored, or improved in the future. The gaps in the existing research data have been explored and a detailed analysis was carried out to identify the different ways in which big data analysis and storage work in relevance to the construction industry. Big data engineering (BDE) and statistics are among the most crucial steps for integrating big data technology in construction. The results of this study suggest that while the existing research studies have set the stage for improving big data research, the integration of the associated digital technologies into the construction industry is not very clear. Among the future opportunities, big data research into construction safety, site management, heritage conservation, and project waste minimization and quality improvements are key areas.
Publisher: Emerald
Date: 13-03-2017
Abstract: The purpose of this paper is to investigate the level of implementation of Six Sigma (SS) in the construction industry of Pakistan along with the current state of affairs and the challenges, and opportunities for a successful implementation. The research is purely exploratory in nature. Based on published work, critical success factors are gathered, and a number of questionnaire surveys and interviews are conducted to refine and quantify their impact. A system dynamics framework to assess the SS influence on project success is developed and case study project are simulated. The construction industry of Pakistan is still functioning in a traditional way marred with low level of awareness and ad hoc approaches, the findings point to a huge improvement opportunity. Further, when under planning projects are exposed to SS, the chances of project success improve better than under execution projects. The limited level of awareness possessed by the respondents constrains the possible outreach of this work in industrially developed contexts. However, this work may become an impetus for further research in managing quality in construction industry. The findings can be used to improve the quality provision of construction projects. This work may trigger an important debate over the research and implementation of SS in the construction industry of developing countries that may greatly benefit by improving the quality of their projects and rectify their diminishing reputation for project success.
Publisher: MDPI AG
Date: 06-07-2021
DOI: 10.3390/SU13147547
Abstract: Floods have been a major cause of destruction, instigating fatalities and massive damage to the infrastructure and overall economy of the affected country. Flood-related devastation results in the loss of homes, buildings, and critical infrastructure, leaving no means of communication or travel for the people stuck in such disasters. Thus, it is essential to develop systems that can detect floods in a region to provide timely aid and relief to stranded people, save their livelihoods, homes, and buildings, and protect key city infrastructure. Flood prediction and warning systems have been implemented in developed countries, but the manufacturing cost of such systems is too high for developing countries. Remote sensing, satellite imagery, global positioning system, and geographical information systems are currently used for flood detection to assess the flood-related damages. These techniques use neural networks, machine learning, or deep learning methods. However, unmanned aerial vehicles (UAVs) coupled with convolution neural networks have not been explored in these contexts to instigate a swift disaster management response to minimize damage to infrastructure. Accordingly, this paper uses UAV-based aerial imagery as a flood detection method based on Convolutional Neural Network (CNN) to extract flood-related features from the images of the disaster zone. This method is effective in assessing the damage to local infrastructures in the disaster zones. The study area is based on a flood-prone region of the Indus River in Pakistan, where both pre-and post-disaster images are collected through UAVs. For the training phase, 2150 image patches are created by resizing and cropping the source images. These patches in the training dataset train the CNN model to detect and extract the regions where a flood-related change has occurred. The model is tested against both pre-and post-disaster images to validate it, which has positive flood detection results with an accuracy of 91%. Disaster management organizations can use this model to assess the damages to critical city infrastructure and other assets worldwide to instigate proper disaster responses and minimize the damages. This can help with the smart governance of the cities where all emergent disasters are addressed promptly.
Publisher: MDPI AG
Date: 27-05-2020
DOI: 10.3390/SU12114382
Abstract: The real estate sector is receiving mix responses throughout the world, with some countries like USA receiving lesser and European and Asia Pacific markets receiving more transactions in recent years. Among the concerning factors, post-purchase regrets by the real estate owners or renters are on the rise, which have never been assessed to date through scholarly research. These regrets can further increase in the time of lockdowns and bans on inspections due to Corona Virus Disease 2019 (COVID-19) and social distancing rules enforced by various countries such as Australia. The current study aims at investigating the key post-purchase regret factors of real estate and property owners and renters over the last decade using published literature and online threads. Based on pertinent literature, 118 systematically identified and text-mined articles, and four online threads with 135 responses, the current study develops system dynamics models to assess and predict the increase in consumers’ regrets over the last decade. Further, a user-generated thread with 23 responses involving seven real estate managers and five agents with more than 20 years of experience, 10 buyers with at least three successful rentals or purchases, and a photographer with more than 10 years of experience, is initiated on five online discussion platforms whereby the respondents are involved in a detailed discussion to highlight the regret reasons specific to real estate purchases based on online information. General architecture for text mining (GATE) software has been utilised to mine the text from both types of threads: Published and user generated. Overall, the articles and threads published over the last decade are studied under two periods: P1 (2010–2014) and P2 (2015–2019) to highlight the post-purchase or rent-related regret reasons. The results show that regret levels of the real estate consumers based on published post-purchase data are at an alarmingly high level of 88%, which compared to 2015, has increased by 18%. Among the major cited reasons, complicated buy–sell process, lack or accuracy of information, housing costs, house size, mortgages, agents, inspections, and emotional decision making are key reasons of regret. Overall, a total of 10% and 8% increases have occurred in the regrets related to the buy–sell process and lack of inspections, respectively. On the other hand, regrets related to agents and housing costs have decreased drastically by 40% mainly due to the good return on investments in the growing markets. However, based on the current trend of over reliance on online information and more powers to the agents controlling online information coupled with lack of physical inspections, the situation can change anytime. Similarly, lack of information, housing size, and mortgage-related regrets have also decreased by 7%, 5%, and 2%, respectively, since 2019. The results are expected to encourage policy level changes for addressing the regrets and uplifting the real estate industry and moving towards a smart and sustainable real estate sector. These results and pertinent discussions may help the real estate decision makers to uplift the current state, move towards a smart real estate, and avoid futuristic regrets, especially in the COVID-hit environment where most of the industries are struggling to survive. Careful attention is required to the top regret factors identified in the study by the real estate managers, investors, and agents to pave the way for a more managed real estate and property sector whereby the consumers are more satisfied with the value they receive for their money. This win–win situation will enhance the property business and remove the stigmas of intentional and deliberate withholding of information by managers and agents from the property and real estate sectors that can help boost the business through more purchases and satisfaction of its customers.
Publisher: MDPI AG
Date: 18-09-2021
DOI: 10.3390/SU131810426
Abstract: Coronavirus Disease 2019 (COVID-19) has emerged as a global pandemic since late 2019 and has affected all forms of human life and economic developments. Various techniques are used to collect the infected patients’ s le, which carries risks of transferring the infection to others. The current study proposes an AI-powered UAV-based s le collection procedure through self-collection kits delivery to the potential patients and bringing the s les back for testing. Using a hypothetical case study of Islamabad, Pakistan, various test cases are run where the UAVs paths are optimized using four key algorithms, greedy, intra-route, inter-route, and tabu, to save time and reduce carbon emissions associated with alternate transportation methods. Four cases with 30, 50, 100, and 500 patients are investigated for delivering the self-testing kits to the patients. The results show that the Tabu algorithm provides the best-optimized paths covering 31.85, 51.35, 85, and 349.15 km distance for different numbers of patients. In addition, the algorithms optimize the number of UAVs to be used in each case and address the studied cases patients with 5, 8, 14, and 71 UAVs, respectively. The current study provides the first step towards the practical handling of COVID-19 and other pandemics in developing countries, where the risks of spreading the infections can be minimized by reducing person-to-person contact. Furthermore, the reduced carbon footprints of these UAVs are an added advantage for developing countries that struggle to control such emissions. The proposed system is equally applicable to both developed and developing countries and can help reduce the spread of COVID-19 through minimizing the person-to-person contact, thus helping the transformation of healthcare to smart healthcare.
Publisher: MDPI AG
Date: 13-07-2022
DOI: 10.3390/BUILDINGS12071004
Abstract: Construction is a resource-intensive industry where a circular economy (CE) is essential to minimize global impacts and conserve natural resources. A CE achieves long-term sustainability by enabling materials to circulate along the critical supply chains. Accordingly, recent research has proposed a paradigm shift towards CE-based sustainability. However, uncertainties caused by fluctuating raw material prices, scarce materials, increasing demand, consumers’ expectations, lack of proper waste infrastructure, and the use of wrong recycling technologies all lead to complexities in the construction industry (CI). This research paper aims to determine the enablers of a CE for sustainable development in the CI. The system dynamics (SD) approach is utilized for modeling and simulation purposes to address the associated process complexity. First, using content analysis of pertinent literature, ten enablers of a CE for sustainable development in CI were identified. Then, causality among these enablers was identified via interviews and questionnaire surveys, leading to the development of the causal loop diagram (CLD) using systems thinking. The CLD for the 10 shortlisted enablers shows five reinforcing loops and one balancing loop. Furthermore, the CLD was used to develop an SD model with two stocks: “Organizational Incentive Schemes” and “Policy Support.” An additional stock (“Sustainable Development”) was created to determine the combined effect of all stocks. The model was simulated for five years. The findings show that policy support and organizational incentive schemes, among other enablers, are critical in implementing a CE for sustainable development in CI. The outcomes of this study can help CI practitioners to implement a CE in a way that drives innovation, boosts economic growth, and improves competitiveness.
Publisher: MDPI AG
Date: 24-12-2021
Abstract: Manual inspection of infrastructure damages such as building cracks is difficult due to the objectivity and reliability of assessment and high demands of time and costs. This can be automated using unmanned aerial vehicles (UAVs) for aerial imagery of damages. Numerous computer vision-based approaches have been applied to address the limitations of crack detection but they have their limitations that can be overcome by using various hybrid approaches based on artificial intelligence (AI) and machine learning (ML) techniques. The convolutional neural networks (CNNs), an application of the deep learning (DL) method, display remarkable potential for automatically detecting image features such as damages and are less sensitive to image noise. A modified deep hierarchical CNN architecture has been used in this study for crack detection and damage assessment in civil infrastructures. The proposed architecture is based on 16 convolution layers and a cycle generative adversarial network (CycleGAN). For this study, the crack images were collected using UAVs and open-source images of mid to high rise buildings (five stories and above) constructed during 2000 in Sydney, Australia. Conventionally, a CNN network only utilizes the last layer of convolution. However, our proposed network is based on the utility of multiple layers. Another important component of the proposed CNN architecture is the application of guided filtering (GF) and conditional random fields (CRFs) to refine the predicted outputs to get reliable results. Benchmarking data (600 images) of Sydney-based buildings damages was used to test the proposed architecture. The proposed deep hierarchical CNN architecture produced superior performance when evaluated using five methods: GF method, Baseline (BN) method, Deep-Crack BN, Deep-Crack GF, and SegNet. Overall, the GF method outperformed all other methods as indicated by the global accuracy (0.990), class average accuracy (0.939), mean intersection of the union overall classes (IoU) (0.879), precision (0.838), recall (0.879), and F-score (0.8581) values. Overall, the proposed CNN architecture provides the advantages of reduced noise, highly integrated supervision of features, adequate learning, and aggregation of both multi-scale and multilevel features during the training procedure along with the refinement of the overall output predictions.
Publisher: University of Technology, Sydney (UTS)
Date: 03-09-2020
Abstract: The aim of this paper is to examine students’ performance in a computation-based course by evaluating the effects of key factors including sketching, visualization resources provided to them during the lectures, their attendance and tutors’ experience. A systematic review was conducted including 192 articles published during January 2010 to December 2019. Further, a case study has been conducted in which 633 students from non-engineering backgrounds were taught a core course of construction over three-yearly sessions from 2017 to 2019. The performance has been assessed through two quizzes of 10% weight each, assignment of 40% weight and a final exam with 30% weight in 2017-18 and 40% weight in 2019 were utilized with an attendance criterion of below 75% as low attendance. The statistical result highlights that a clear difference of 14% overall marks exist between the students with less than 75% attendance and the ones with 75% and above in 2017 and a 10% gap in 2018. Students with high marks in sketching secured higher overall marks as compared to others highlighting that the sketching skill is useful to construction students. The findings contribute to the body of education knowledge by evaluating key influential factors and provide a useful benchmark to other educators in the field.
Publisher: MDPI AG
Date: 27-03-2023
DOI: 10.3390/BUILDINGS13040872
Abstract: Mixed Reality (MR) that combines elements of both augmented reality (AR) and virtual reality (VR) has great potential for use in the construction industry. However, its usage in construction projects in developing countries has not been widely researched. This study aims to examine the major drivers of, and barriers to, the adoption of MR technologies (MRTs) in the construction sector of developing countries. A mixed methodology that included both qualitative and quantitative data analysis was used. The literature review revealed 37 barriers to, and 41 drivers of, MR adoption. A questionnaire was then distributed to 220 randomly selected respondents from the pertinent construction industry, representing all major stakeholders. The relative importance index (RII) was used to rank the barriers and drivers in terms of significance. The results showed that the primary barriers to MR adoption are the high cost of initial investment, public perception of the technology being immature, limited demand, and difficulty accessing relevant experts’ knowledge. The key drivers of MR adoption include improved project knowledge, reduced overall project costs, low-cost and realistic training scenarios, reduced damage and development costs, and enhanced user experience. These findings provide insights into the major barriers and drivers of MR in the construction sector of developing countries and will help pertinent companies to focus their research and development (R& D) efforts on overcoming these barriers and promote their adoption to move towards the much sought-after construction automation and digitalization.
Publisher: Springer Singapore
Date: 19-12-2018
Publisher: American Society of Civil Engineers (ASCE)
Date: 08-2017
Publisher: MDPI AG
Date: 02-07-2022
DOI: 10.3390/SU14138117
Abstract: Infrastructure, such as buildings, bridges, pavement, etc., needs to be examined periodically to maintain its reliability and structural health. Visual signs of cracks and depressions indicate stress and wear and tear over time, leading to failure/collapse if these cracks are located at critical locations, such as in load-bearing joints. Manual inspection is carried out by experienced inspectors who require long inspection times and rely on their empirical and subjective knowledge. This lengthy process results in delays that further compromise the infrastructure’s structural integrity. To address this limitation, this study proposes a deep learning (DL)-based autonomous crack detection method using the convolutional neural network (CNN) technique. To improve the CNN classification performance for enhanced pixel segmentation, 40,000 RGB images were processed before training a pretrained VGG16 architecture to create different CNN models. The chosen methods (grayscale, thresholding, and edge detection) have been used in image processing (IP) for crack detection, but not in DL. The study found that the grayscale models (F1 score for 10 epochs: 99.331%, 20 epochs: 99.549%) had a similar performance to the RGB models (F1 score for 10 epochs: 99.432%, 20 epochs: 99.533%), with the performance increasing at a greater rate with more training (grayscale: +2 TP, +11 TN images RGB: +2 TP, +4 TN images). The thresholding and edge-detection models had reduced performance compared to the RGB models (20-epoch F1 score to RGB: thresholding −0.723%, edge detection −0.402%). This suggests that DL crack detection does not rely on colour. Hence, the model has implications for the automated crack detection of concrete infrastructures and the enhanced reliability of the gathered information.
Publisher: MDPI AG
Date: 03-02-2022
DOI: 10.3390/BUILDINGS12020171
Abstract: The current young generation in Australia is increasingly facing issues around housing, and the demand for affordable and personalised housing alternatives to suit the needs of the younger population has given rise to a variety of housing options. The Build-to-Rent (BTR) housing supply model is one such option that was recently introduced with the aim to provide ersity and choice within the private rental sector (PRS). Although the idea of building housing infrastructure to rent is not new, the formalisation of the BTR concept is currently underway and requires a comprehensive understanding of the various factors influencing its successful adoption. With the introduction of big market players such as institutional investors, understanding the critical success factors (CSFs) for producing successful BTR projects is crucial for its adoption as a feasible option for housing provision, especially for the younger population. Through a systematic literature review approach using the Web of Science and Scopus databases, recent literature from 2011 to 2021 were reviewed to identify CSFs related to the BTR housing model. These CSFs help distinguish the BTR paradigm within the general housing market system. A total of 32 CSFs were identified through the review process. Major factors relate to investors’ interest and willingness, affordability, and housing reforms and awareness. These CSFs identify the key areas of interest within the BTR research which can help create a comprehensive understanding of the current BTR scheme, along with providing a baseline for future research.
Publisher: MDPI AG
Date: 14-06-2023
DOI: 10.3390/BUILDINGS13061528
Abstract: Job stress (JS) is a significant issue in the construction industry of developing countries. This study aims to examine the impact of error-management climate (EMC), safety climate (SC), and psychological capital (PC) (as a mediator) on employee JS in the construction industry, and establish relationships between these constructs. A questionnaire survey was conducted to gather data from 144 respondents. The study’s hypothesized relationships were tested using partial-least-squares structural-equation modeling (PLS-SEM). The analysis indicated a positive association between EMC and PC. Conversely, EMC did not have a negative impact on JS. The study also established a constructive relationship between SC and PC, and a significant negative association between SC and JS. Regarding mediation, PC was found to partially mediate the effect of EMC on JS, accounting for 55% of the variance accounted for (VAF). The study’s innovative contribution lies in exploring the limited research on PC within the construction industry, and investigating the interactions among SC, EMC, PC, and JS.
Publisher: Informa UK Limited
Date: 07-06-2018
Publisher: MDPI AG
Date: 13-10-2021
DOI: 10.3390/SU132011300
Abstract: Coronavirus Disease 2019 (COVID-19) has affected global economies due to lockdowns, business closures, and travel and other restrictions. To control the spread of the virus, several countries, including Australia, imposed strict border restrictions and lockdown measures. Accordingly, international borders have been closed, and all incoming international passengers are mandated to a 14-day hotel quarantine. Residents’ movements and businesses have been limited to essential services only. Employees have been directed to work from home while businesses moved to a remote working model. Due to such stringent measures, small and medium businesses such as cafes, restaurants, hotels, childcare centers, and tourism-based institutions incurred heavy losses, pushing a considerable portion of such small businesses to close. The airlines, education, tourism, and hospitality sector were the worst impacted among all. Due to such closures and associated effects of COVID-19, the unemployment rates are assumed to be significantly increased in countries like Australia. However, a study investigating this unemployment and reporting its status does not exist for Australia. Therefore, in this study, we investigated the effects of COVID-19 control measures such as travel restriction and lockdown on Australia’s employment status and labor markets. The data for the local transport network, unemployment rates and impacts on the tourism industry in Australia were extracted from the public data sources to assess the unemployment rates at both national and state-wide levels. Further, we also looked into the rehabilitation measures by the Australian government, such as the Job Keeper and Job Seeker programs in March 2020, that aim to provide support to people who are unable to run their businesses or have lost their jobs due to the pandemic. Overall, we observed that despite the global crisis, the Australian unemployment rate has reduced in the last year.
Publisher: Golden Light Publishing
Date: 30-09-2020
Publisher: No publisher found
Date: 2017
Publisher: MDPI AG
Date: 02-03-2023
DOI: 10.3390/BUILDINGS13030671
Abstract: Falls from height (FFH) are common safety hazards on construction sites causing monetary and human loss. Accordingly, ensuring safety at heights is a prerequisite for implementing a strong safety culture in the construction industry. However, despite multiple safety management systems, FFH are still rising, indicating that compliance with safety standards and rules remains low or neglected. Building information modelling (BIM) is used in this study to develop a safety clauses visualization system using Autodesk Revit’s application programming interface (API). The prototype digitally stores and views clauses of safety standards, such as the Operational Health and Safety Rules 2022 and Introduction to Health and Safety in Construction by NEBOSH 2008, in the BIM environment. This facilitates the safety manager’s ability to ensure that the precautionary measures needed to work at different heights are observed. The developed prototype underwent a focus group evaluation involving nine experts to assess its effectiveness in preventing FFH. It successfully created a comprehensive safety clause library that allows safety managers to provide relevant safety equipment to workers before work execution. It also enhances the awareness of construction workers of all safety requirements vis-à-vis heights. Moreover, it creates a database of safety standards that can be viewed and expanded in future by adding more safety standards to ensure wider applicability.
Publisher: MDPI AG
Date: 24-05-2022
DOI: 10.3390/BUILDINGS12060701
Abstract: This study addressed the complexity involved in integrating the causative risk factors influencing construction profitability. Most of the existing studies cover the in idual effects of profitability influencing factors. Very few focus on the systematic impact without incorporating the complexity and associated dynamics, presenting a gap targeted by the current study. The current study aimed to assess causative interrelations and interdependencies between profitability influencing risk factors (PIRF), through systems thinking (ST) and system dynamics (SD) modeling. The SD approach was used to evaluate the integrated impacts on profitability-influencing risk categories (PIRC) in construction projects. The causative influencing factors affecting construction profitability were identified through a comprehensive literature review. These were ranked using content analysis, and categorized into significant issues. Through 250 structured surveys and 15 expert opinion meetings, the path for quantitative and qualitative evaluations was prepared. Following these investigations, a causal loop diagram (CLD) was established using the ST technique, and the integrated effect was quantified using SD modeling. The study finds the rising cost of material, supply chain process, payment issues, planning and scheduling problems, financial difficulties, and effective control of manpower and equipment resources as the most critical PIRFs. The integrated effects of PIRFs on PIRC were quantified using SD modeling. This study helps field professionals with profitability-influencing factors, diagnosing issues, and integrating impacts regarding decision-making and policy formulation. For researchers, it presents a list of factors that can be investigated in detail, and the holistic interrelationships established.
Publisher: Faculty of Engineering, Chulalongkorn University
Date: 31-07-2017
Publisher: MDPI AG
Date: 11-08-2021
DOI: 10.3390/IJGI10080539
Abstract: Global climate has been radically affected by the urbanization process in recent years. Karachi, Pakistan’s economic hub, is also showing signs of swift urbanization. Owing to the construction of infrastructure projects under the China-Pakistan Economic Corridor (CPEC) and associated urbanization, Karachi’s climate has been significantly affected. The associated replacement of natural surfaces by anthropogenic materials results in urban overheating and increased local temperatures leading to serious health issues and higher air pollution. Thus, these temperature changes and urban overheating effects must be addressed to minimize their impact on the city’s population. For analyzing the urban overheating of Karachi city, LST (land surface temperature) is assessed in the current study, where data of the past 20 years (2000–2020) is used. For this purpose, remote sensing data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) and Moderate-Resolution Imaging Spectroradiometer (MODIS) sensors were utilized. The long short-term memory (LSTM) model was utilized where the road density (RD), elevation, and enhanced vegetation index (EVI) are used as input parameters. Upon comparing estimated and measured LST, the values of mean absolute error (MAE), mean square error (MSE), and mean absolute percentage error (MAPE) are 0.27 K, 0.237, and 0.15% for January, and 0.29 K, 0.261, and 0.13% for May, respectively. The low MAE, MSE, and MAPE values show a higher correlation between the predicted and observed LST values. Moreover, results show that more than 90% of the pixel data falls in the least possible error range of −1 K to +1 K. The MAE, MSE and MAPE values for Support Vector Regression (SVR) are 0.52 K, 0.453 and 0.18% and 0.76 K, 0.873, and 0.26%. The current model outperforms previous studies, shows a higher accuracy, and depicts greater reliability to predict the actual scenario. In the future, based on the accurate LST results from this model, city planners can propose mitigation strategies to reduce the harmful effects of urban overheating and associated Urban Heat Island effects (UHI).
Publisher: Elsevier BV
Date: 10-2021
Publisher: WORLD SCIENTIFIC
Date: 05-2019
Publisher: MDPI AG
Date: 13-06-2022
DOI: 10.3390/SU14127211
Abstract: Effective leadership and creative performance are the predominant factors for the success of modern projects in the global construction industry. However, rigorous research has not explored the nexus between such factors and the leader–member exchange (LMX). To address this gap, this study explores the relationship between dimensions of paternalistic leadership and employee creativity achieved through LMX in the context of the construction industry. Based on social exchange theory (SET), six relevant hypotheses were proposed in this study. The data were collected through a structured questionnaire. An online survey form was used for data collection, through which 288 responses were collected from the construction industry employees working in Pakistan. The collected data were analyzed using Smart PLS in two stages, i.e., measurement model evaluation (reliability analysis, convergent and discriminant validity) and structural model evaluation (R2, F2, and path coefficient). The findings of the current study reveal a positive association of authoritarian, benevolent, and moral leadership with employee creativity. In addition, LMX significantly mediates the relationship between the two dimensions of paternalistic leadership (benevolent and moral leadership) and creativity, except for authoritarian leadership. Based on the results, this study contributes to the body of knowledge related to the appropriate leadership style in the local construction industry that can be extended to other developing countries with similar dynamics. It also helps the managers target and develops relevant skills to acquire positive outcomes from their team members.
Publisher: Springer Science and Business Media LLC
Date: 19-05-2021
Publisher: MDPI AG
Date: 18-09-2021
DOI: 10.3390/SMARTCITIES4030065
Abstract: Floods are one of the most fatal and devastating disasters, instigating an immense loss of human lives and damage to property, infrastructure, and agricultural lands. To cater to this, there is a need to develop and implement real-time flood management systems that could instantly detect flooded regions to initiate relief activities as early as possible. Current imaging systems, relying on satellites, have demonstrated low accuracy and delayed response, making them unreliable and impractical to be used in emergency responses to natural disasters such as flooding. This research employs Unmanned Aerial Vehicles (UAVs) to develop an automated imaging system that can identify inundated areas from aerial images. The Haar cascade classifier was explored in the case study to detect landmarks such as roads and buildings from the aerial images captured by UAVs and identify flooded areas. The extracted landmarks are added to the training dataset that is used to train a deep learning algorithm. Experimental results show that buildings and roads can be detected from the images with 91% and 94% accuracy, respectively. The overall accuracy of 91% is recorded in classifying flooded and non-flooded regions from the input case study images. The system has shown promising results on test images belonging to both pre- and post-flood classes. The flood relief and rescue workers can quickly locate flooded regions and rescue stranded people using this system. Such real-time flood inundation systems will help transform the disaster management systems in line with modern smart cities initiatives.
Publisher: Elsevier BV
Date: 06-2021
Publisher: MDPI AG
Date: 29-08-2022
DOI: 10.3390/BUILDINGS12091322
Abstract: Due to the increased globalization and the disruptions caused by pandemics, supply chains (SCs) are becoming more complex in all industries. Such increased complexities of the SCs bring in more risks. The construction industry is no exception its SC has been disrupted in line with its industrial counterparts. Therefore, it is important to manage the complexities in integrating SC risks and resilient capabilities (RCs) to enable a resilient SC in construction. This study investigated the complexity involved in the dynamics of effects between organizations’ SC risks and RCs to overcome disruptive events. Past researchers investigated how to improve the performance of construction projects, regardless of the complexities and interdependencies associated with the risks across the entire SC. However, the system dynamics (SD) approach to describe the ersity of construction SCs under risks has received limited attention indicating a research gap pursued by this study. This work aimed to analyze and establish interconnectivity and functionality amongst the construction SC risks and RCs using systems thinking (ST) and SD modeling approach. The SD technique is used to assess the complexity and integrated effect of SC risks on construction projects to enhance their resilience. The risks and RCs were identified by critically scrutinizing the literature and were then ranked through content analysis. Questionnaire surveys and expert opinions (involving 10 experts) helped develop causal loop diagrams (CLDs) and SD models with simulations to assess complexity qualitatively and quantitatively within the system. Research reveals that construction organizations are more vulnerable to health pandemics, budget overruns, poor information coordination, insufficient management oversight, and error visibility to stakeholders. Further, the most effective RCs include assets visibility, collaborative information exchange, business intelligence gatherings, alternative suppliers, and inventory management systems. This research helps industry practitioners identify and plan for various risks and RCs within their organizations and SCs. Furthermore, it helps understand trade-offs between suitable RCs to abate essential risks and develop preparedness against disruptions to improve organizational policymaking, project efficiency, and performance.
Publisher: Springer Science and Business Media LLC
Date: 02-02-2022
Publisher: MDPI AG
Date: 14-04-2023
DOI: 10.3390/BUILDINGS13041036
Abstract: Non-renewable resources have been becoming scarcer on a global scale by the day, while energy demand has been rising exponentially. To tackle this problem, organizations worldwide have been striving to learn and adopt green practices to sustain themselves and benefit society. In this context, the current study aims to identify and understand the critical factors that encourage in iduals working in construction organizations to adopt green behavior. The current study surveyed 121 top managers working in 150 construction firms deployed across Pakistan. It was found that knowledge and awareness significantly contributed to green behavioral adoption. Additionally, behavioral intention, motivation, and environmental consciousness have been found to positively mediate the impact of knowledge and awareness on green behavior adoption. The findings of this study highlight the important factors to consider when developing future policies. Moreover, the research provides a stepping stone for future researchers to evaluate other sectors and regions in similar contexts to draw comparisons and identify areas for improvement.
Publisher: Emerald
Date: 07-11-2016
DOI: 10.1108/JFMPC-02-2016-0011
Abstract: The purpose of this paper is to investigate the critical decision factors of public–private partnership (PPP) concession which is complex due to a number of uncertain and random variables. To identify critical factors contributing to determination of concession period, this study reviews the published literature. It also identifies countries contributing most in PPP research. As a whole, it provides a mutually beneficial scenario by formulating a decision-making matrix. This paper reviews the literature published during the period 2005-2015. A two-staged methodology is followed on retrieved scholarly papers: first, countries contributing to PPP are identified along with authors and affiliated institutions. Second, using frequency analysis of shortlisted critical factors, yearly appearance and stakeholders affected, a decision matrix is formulated. The most contributing country toward PPP research is China, followed by the USA both in terms of country- and author-based contribution. In total, 63 factors are identified that affect PPP concession out of which, 8 per cent are highly critical and 21 per cent are marginally critical for decision-making. Critical factors of PPP concession period will be identified with the help of decision-making matrix. This will help in adequate resource allocation for handling critical factors ensuring project success. Researchers may also understand the research trends in the past decade to usher ways for future improvements. This paper reports findings of an original and innovative study, which identifies critical success factors of PPP concession period and synthesizes them into a decision-making matrix. Many of the previous studies have identified and ranked the critical factors but such a synthesis has not been reported.
Publisher: Emerald
Date: 06-06-2016
DOI: 10.1108/IJLSS-11-2015-0045
Abstract: This paper aims at collecting and reviewing the published literature on the Six Sigma in construction along with its critical success factors (CSFs). The research is based on literature review. Based on the keyword and semantic search techniques, papers published on the topic of Six Sigma during 2000-2015 are retrieved. Frequency analysis is performed to find out significance of identified CSFs, and zoning is performed based on the product of frequency of appearance and parties affected by the CSFs. A total of 69 CSFs are identified as published in the literature. Based on an inclusion criterion of minimum 15 appearances, 22 CSFs are shortlisted for further analysis. Of these CSFs, around 32 per cent fall into red zone (most critical), 50 per cent into yellow and 18 per cent into green zone (least critical). This work is limited by partial identification of CSFs. Though based on an extensive search, the retrieved CSFs may not be all the published ones. However, more thorough search techniques can be applied to improve upon this work. The findings can be used to facilitate the decision-making in the context of project success. This work is an original attempt at gathering Six Sigma CSFs applicable to construction projects. It may be used for further research and development to help ensure project quality and success.
Publisher: MDPI AG
Date: 21-03-2022
DOI: 10.3390/ARCHITECTURE2010010
Abstract: The identification of significant areas impacting safety performance has always been a key concern for construction management researchers. This paper aims to examine the ersified influence of sensitive sub-categories of demographic variables on construction safety climate (SC). The data relating to fourteen demographic variables and twenty-four formerly validated SC statements were collected from forty-one under-construction high-rise buildings in Pakistan. The variances in respondents’ distribution among various sub-categories of demographic variables, and influence of each sub-category of demographic variables on SC statements were analyzed using cross-tabulation, Spearman’s rho correlation coefficients, independent s le Kruskal-Wallis and Mann-Whitney U tests. The study comprehends that the employees in the age group of 20 years or below and between 41 and 50 years, engaged for over 48 h per week, having 4 dependent family members, primary education, and/or lesser working experience, attained a comparatively lower SC level. Likewise, frontline workers and foremen are observed to be employed for extended working hours, causing them fatigue. It also discovers that safety alertness level steadily declines once employees get acquainted with their tasks, thus necessitating to arrange periodic refresher safety training sessions. The study recommends concentrating on frontline workers and foremen who are less educated and fall in the age group of 41–50 years by resolving their safety concerns and providing them adequate safety training, promptly replacing their defective equipment, improving worksite conditions, and counselling them about the significance of wearing PPE and adhering to all the safety rules regardless of the difficulty in their enactment. A joint focus on the heightened personal attributes of employees and risky SC statements is expected to enhance safety performance on under-construction building projects. Moreover, the study’s results can be cautiously generalized and applied to other countries having similar work environment.
Publisher: MDPI AG
Date: 20-10-2022
DOI: 10.3390/BUILDINGS12101752
Abstract: Building information modeling (BIM) through data-rich digital representation has revolutionized the architecture, engineering, and construction (AEC) industry. BIM implementation in the AEC industry has noticeably increased over the last decade. Various BIM roles have been discussed in the literature to ease the process of BIM implementation, but the BIM roles related to project delivery methods have not been standardized. Stimulated by this need, this study develops a BIM roles and responsibilities matrix (BIM-R& R) in the context of the design−bid−build (DBB) projects for developing countries. A comprehensive literature review has been conducted, followed by a questionnaire survey comprising 105 responses. The results were analyzed to formulate a BIM-R& R matrix, on which the expert opinion was obtained from the BIM experts. The proposed BIM-R& R matrix describes all the roles and their corresponding responsibilities required along the project life cycle phases of DBB projects. The incorporation of BIM roles in the DBB procurement process will aid in the efficient management of all information and data that may be lost due to the fragmented nature of DBB. BIM roles with enhanced communication and coordination will also help in reducing time and cost overruns while maintaining a high-quality product. This study helps the associated construction industry in its efforts to implement BIM on their projects by providing a method by which to assess which BIM roles are necessary. Moreover, it will provide project and construction managers with a clear understanding of the BIM roles in DBB projects.
Publisher: Elsevier BV
Date: 05-2021
Publisher: MDPI AG
Date: 23-09-2022
DOI: 10.3390/BUILDINGS12101516
Abstract: Since the beginning of industrialization, there have been several paradigm shifts initiated through technological revolutions, inventions, and leaps [...]
Publisher: Springer Science and Business Media LLC
Date: 13-03-2017
Publisher: MDPI AG
Date: 09-03-2022
DOI: 10.3390/BDCC6010030
Abstract: The detection and classification of drug–drug interactions (DDI) from existing data are of high importance because recent reports show that DDIs are among the major causes of hospital-acquired conditions and readmissions and are also necessary for smart healthcare. Therefore, to avoid adverse drug interactions, it is necessary to have an up-to-date knowledge of DDIs. This knowledge could be extracted by applying text-processing techniques to the medical literature published in the form of ‘Big Data’ because, whenever a drug interaction is investigated, it is typically reported and published in healthcare and clinical pharmacology journals. However, it is crucial to automate the extraction of the interactions taking place between drugs because the medical literature is being published in immense volumes, and it is impossible for healthcare professionals to read and collect all of the investigated DDI reports from these Big Data. To avoid this time-consuming procedure, the Information Extraction (IE) and Relationship Extraction (RE) techniques that have been studied in depth in Natural Language Processing (NLP) could be very promising. Since 2011, a lot of research has been reported in this particular area, and there are many approaches that have been implemented that can also be applied to biomedical texts to extract DDI-related information. A benchmark corpus is also publicly available for the advancement of DDI extraction tasks. The current state-of-the-art implementations for extracting DDIs from biomedical texts has employed Support Vector Machines (SVM) or other machine learning methods that work on manually defined features and that might be the cause of the low precision and recall that have been achieved in this domain so far. Modern deep learning techniques have also been applied for the automatic extraction of DDIs from the scientific literature and have proven to be very promising for the advancement of DDI extraction tasks. As such, it is pertinent to investigate deep learning techniques for the extraction and classification of DDIs in order for them to be used in the smart healthcare domain. We proposed a deep neural network-based method (SEV-DDI: Severity-Drug–Drug Interaction) with some further-integrated units/layers to achieve higher precision and accuracy. After successfully outperforming other methods in the DDI classification task, we moved a step further and utilized the methods in a sentiment analysis task to investigate the severity of an interaction. The ability to determine the severity of a DDI will be very helpful for clinical decision support systems in making more accurate and informed decisions, ensuring the safety of the patients.
Publisher: MDPI AG
Date: 26-07-2021
DOI: 10.3390/FIRE4030040
Abstract: Australia is a regular recipient of devastating bushfires that severely impacts its economy, landscape, forests, and wild animals. These bushfires must be managed to save a fortune, wildlife, and vegetation and reduce fatalities and harmful environmental impacts. The current study proposes a holistic model that uses a mixed-method approach of Geographical Information System (GIS), remote sensing, and Unmanned Aerial Vehicles (UAV)-based bushfire assessment and mitigation. The fire products of Visible Infrared Imager Radiometer Suite (VIIRS) and Moderate-resolution Imaging Spectroradiometer (MODIS) are used for monitoring the burnt areas within the Victorian Region due to the 2020 bushfires. The results show that the aggregate of 1500 m produces the best output for estimating the burnt areas. The identified hotspots are in the eastern belt of the state that progressed north towards New South Wales. The R2 values between 0.91–0.99 indicate the fitness of methods used in the current study. A healthy z-value index between 0.03 to 2.9 shows the statistical significance of the hotspots. Additional analysis of the 2019–20 Victorian bushfires shows a widespread radius of the fires associated with the climate change and Indian Ocean Dipole (IOD) phenomenon. The UAV paths are optimized using five algorithms: greedy, intra route, inter route, tabu, and particle swarm optimization (PSO), where PSO search surpassed all the tested methods in terms of faster run time and lesser costs to manage the bushfires disasters. The average improvement demonstrated by the PSO algorithm over the greedy method is approximately 2% and 1.2% as compared with the intra route. Further, the cost reduction is 1.5% compared with the inter-route scheme and 1.2% compared with the intra route algorithm. The local disaster management authorities can instantly adopt the proposed system to assess the bushfires disasters and instigate an immediate response plan.
Publisher: WORLD SCIENTIFIC
Date: 05-2019
Publisher: MDPI AG
Date: 11-10-2023
DOI: 10.3390/SOC13100219
Publisher: MDPI AG
Date: 03-07-2020
DOI: 10.3390/SU12135402
Abstract: Digital tools and marketing have been widely adopted in various industries throughout the world. These tools have enabled companies to obtain real-time customer insights and create and communicate value to customers more effectively. This study aims at understanding the principles and practices of sustainable digital marketing in the Malaysian property development industry by investigating the extent to which digital marketing has been adopted, the impediments to its adoption, and the strategies to improve digital capabilities for the local context. Digital marketing theories, practices, and models from other industries are adopted and applied to the local property development industry to lay the foundation for making it smart and sustainable. This paper proposes a marketing technology acceptance model (MTAM) for digital marketing strategy and capability development. The key factors used in the model are ease of use, perceived usefulness, perceived cost, higher return, efficiency, digital service quality, digital information quality, digital system quality, attitude towards use, and actual use. The model and hypothetical relationships of critical factors are tested using structural modeling, reliability, and validity techniques using a s le of 279 Malaysian property development sector representatives. A quantitative approach is adopted, using an online questionnaire tool to investigate the behavior of respondents on the current digital marketing practices and capabilities of Malaysian property development companies. The results show that the s le property development companies are driven by the benefit of easily obtaining real-time customer information for creating and communicating value to customers more effectively through the company brand. Further strategies, such as creating real-time interactions, creating key performance indicators to measure digital marketing, personalization, and encouraging innovation in digital marketing are most preferred by local professionals. An adoption framework is provided based on the reviewed models and results of the current study to help transform the Malaysian property development sector into a smart and sustainable property development sector by facilitating the adoption of digital technologies. The results, based on real-time data and pertinent strategies for improvement of the local property sector, are expected to pave the way for inducing sustainable digital marketing trends, enhancing capabilities, and uplifting the state of the property development sector in developing countries.
Publisher: Springer Science and Business Media LLC
Date: 22-02-2021
Publisher: MDPI AG
Date: 13-09-2021
DOI: 10.3390/SU131810207
Abstract: Bushfires have been a key concern for countries such as Australia for a long time. These must be mitigated to eradicate the associated harmful effects on the climate and to have a sustainable and healthy environment for wildlife. The current study investigates the 2019–2020 bushfires in New South Wales (NSW) Australia. The bush fires are mapped using Geographical Information Systems (GIS) and remote sensing, the hotpots are monitored, and damage is assessed. Further, an Unmanned Aerial Vehicles (UAV)-based bushfire mitigation framework is presented where the bushfires can be mapped and monitored instantly using UAV swarms. For the GIS and remote sensing, datasets of the Australian Bureau of Meteorology and VIIRS fire data products are used, whereas the paths of UAVs are optimized using the Particle Swarm Optimization (PSO) algorithm. The mapping results of 2019–2020 NSW bushfires show that 50% of the national parks of NSW were impacted by the fires, resulting in damage to 2.5 million hectares of land. The fires are highly clustered towards the north and southeastern cities of NSW and its border region with Victoria. The hotspots are in the Deua, Kosciu Sako, Wollemi, and Yengo National Parks. The current study is the first step towards addressing a key issue of bushfire disasters, in the Australian context, that can be adopted by its Rural Fire Service (RFS), before the next fire season, to instantly map, assess, and subsequently mitigate the bushfire disasters. This will help move towards a smart and sustainable environment.
Publisher: MDPI AG
Date: 14-12-2021
DOI: 10.3390/ARCHITECTURE1020012
Abstract: Sustainable supply chain management (SSCM) involves the managing of information, materials, cash flows, and collaboration among enterprises along the supply chain, integrating sustainable development goals. This research paper aims to determine challenges in SSCM adoption and to address related complexity using the system dynamics (SD) approach utilizing modeling and simulation techniques. This research identified challenges from the literature using content analysis. Causality among these identified challenges was determined using interviews and questionnaire surveys that led to the development of a causal loop diagram (CLD), which was used in the development of the SD model. Among the 19 shortlisted variables, CLD had IV reinforcing and II balancing loops. Moreover, CLD was used to build an SD model with two stocks, and a new stock named ‘project performance’ was added to envisage the cumulative impact of all stocks. The model was simulated for five years, and the results predict that the lack of top management commitment and corporate social responsibility adversely affects project performance. This implies that there is a need to improve numerous factors, in particular corporate social responsibility and top management commitment, which would lead to the adoption of SSCM, thus leading to a performance improvement for the construction industry (CI). The model was validated using boundary adequacy, structure, and parametric verification tests, which showed that the developed model is logical and approximately replicates the industry’s actual system. The research findings will help the CI practitioners to adopt sustainability principles in terms of the supply chain and will not only enhance productivity and performance but will also help in the minimization of delays, promote long-term relations, and reduce communication gaps and project complexities.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Location: Australia
No related grants have been discovered for Fahim Ullah.