ORCID Profile
0000-0002-0790-1798
Current Organisations
Bond University
,
Central Queensland University
,
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: 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: 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: 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: 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: 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: Hindawi Limited
Date: 28-03-2021
DOI: 10.1002/INT.22422
Publisher: Elsevier BV
Date: 09-2021
Publisher: Golden Light Publishing
Date: 30-09-2020
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: 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: 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: 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: MDPI AG
Date: 23-11-2021
DOI: 10.3390/SU132312951
Abstract: This study examines the effects of quality of service, product awareness, and perceptions among customers of Islamic financial institutions (IFIs) on customer loyalty through technology integration using customer satisfaction as a mediator. A well-structured, comprehensive questionnaire was developed and data were collected from 203 respondents who were customers of six IFIs in Pakistan and had at least 2 years of experience in dealing confiorm this is correct with these IFIs. A total of 171 accurate responses were received from the respondents. Ten hypotheses were developed and statistically verified using regression and correlation analytical techniques. The results reveal that the quality of customer services and awareness of IFIs had a direct and positive relationship with customer loyalty, which in turn was mediated by customer satisfaction. Perceptions about IFIs had a direct positive relation with customer satisfaction. However, the relation of perceptions and quality of service with customer loyalty and satisfaction in financial institutions through technology integration was found to be insignificant, even in the presence of customer satisfaction as a mediator.
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.
Location: Australia
No related grants have been discovered for Siddra Qayyum.