ORCID Profile
0000-0001-9149-3928
Current Organisation
University of Manchester
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Publisher: Elsevier BV
Date: 11-2022
Publisher: MDPI AG
Date: 13-01-2022
Abstract: The utilization of Internet-of-Things (IoT)-based technologies in the construction industry has recently grabbed the attention of numerous researchers and practitioners. Despite the improvements made to automate this industry using IoT-based technologies, there are several barriers to the further utilization of these leading-edge technologies. A review of the literature revealed that it lacks research focusing on the obstacles to the application of these technologies in Construction Site Safety Management (CSSM). Accordingly, the aim of this research was to identify and analyze the barriers impeding the use of such technologies in the CSSM context. To this end, initially, the extant literature was reviewed extensively and nine experts were interviewed, which led to the identification of 18 barriers. Then, the fuzzy Delphi method (FDM) was used to calculate the importance weights of the identified barriers and prioritize them through the lenses of competent experts in Hong Kong. Following this, the findings were validated using semi-structured interviews. The findings showed that the barriers related to “productivity reduction due to wearable sensors”, “the need for technical training”, and “the need for continuous monitoring” were the most significant, while “limitations on hardware and software and lack of standardization in efforts,” “the need for proper light for smooth functionality”, and “safety hazards” were the least important barriers. The obtained findings not only give new insight to academics, but also provide practical guidelines for the stakeholders at the forefront by enabling them to focus on the key barriers to the implementation of IoT-based technologies in CSSM.
Publisher: American Society of Civil Engineers (ASCE)
Date: 08-2022
Publisher: MDPI AG
Date: 30-01-2202
DOI: 10.3390/SU15032441
Abstract: Though a relatively large number of studies on sustainable project governance (SPG) have been undertaken, the existing corpus of literature is bereft of a comprehensive review paper that scientometrically analyses the materials published hitherto and puts forward the research gaps and the corresponding future works to be conducted. To fill this knowledge gap, this study undertakes a bibliometric review and scientometric analysis by meticulously delving into the relevant body of knowledge of sustainable governance reported in different databases. From the results obtained using CiteSpace software, the following conclusions can be drawn: (1) most of the keywords with high centrality rankings are related to the environment, (2) “participation” and “land use” are the most important clusters, (3) the United Kingdom and the United States are by far the most advanced countries in the concerned field, (4) the hot topics within the defined clusters are “industry”, “transition management”, “property rights”, and “natural resources”, and (5) the two salient keywords are “public participation” and “insight”. The attained findings lay out a solid foundation for researchers and practitioners towards fostering the area of SPG, by focusing on land use, community participation, politics, climate change, and the water–energy–food nexus and finding ways to tackle the elaborated shortcomings.
Publisher: Emerald
Date: 07-03-2022
DOI: 10.1108/ECAM-09-2021-0817
Abstract: In the study, a five-dimensional-safety risk assessment model (5D-SRAM) is developed to improve the construction safety risk assessment approaches available in the literature. To that purpose, a hybrid multi-dimensional fuzzy-based model is proposed, which provides a comprehensive ranking system for the safety risks existing in a project by considering the contextualization of the construction-related activities resulting in an accident. The developed 5D-SRAM is based on an amalgamation of different fuzzy-based techniques. Through the proposed fuzzy analytic hierarchy process (AHP) method, the importance weights of essential risk dimensions playing role in defining the magnitude of the construction-related risks are obtained, while a precise prioritized ranking system for the identified safety risks is acquired using the proposed fuzzy technique of order preference similarity to the ideal solution (FTOPSIS). Through the application of the proposed 5D-SRAM to a real-life case study – which is the case of green building construction projects located in Hong Kong – contributions are realized as follows: (1) determination of a more complete range of risk dimensions, (2) calculation of importance weightings for each risk dimension and (3) obtainment of a precise and inclusive ranking system for safety risks. Additionally, the supremacy of the developed 5D-SRAM against the other safety assessment approaches that are commonly adopted in the construction industry is proved. The developed 5D-SRAM provides the concerned safety decision-makers with not only all the crucial dimensions that play roles toward the magnitude of safety risks posing threats to the workers involved in construction activities, but also they are given hindsight regarding the importance weights of these dimensions. Additionally, the concerned parties are embellished with the final ranking of safety risks in a more comprehensive way than those of existing assessment methods, leading to sagacious adoption of future prudent strategies for dealing with such risks occurring on construction sites. Numerous studies have documented the safety risks faced by construction workers including proposals for risk assessment models. However, the dimensions considered by such models are limited, generally constrained to risk event probability combined with risk impact severity. Overlooking other dimensions that are essential towards the calculation of safety risks' magnitude culminates in overshadowing the further adoption of fruitful mitigative actions. To overcome this shortcoming, this study proposes a novel 5D-SRAM.
Publisher: Emerald
Date: 17-05-2022
Abstract: This study aims to present a comprehensive review, critical analysis and implications of the augmented reality (AR) application and implementation in the construction industry arena and demonstrate the gaps along with the future research agenda. The construction industry has been under pressure to improve its productivity, quality and sustainability. However, the conventional methods and technologies cannot respond to this industry's ever-growing demands while emerging and innovative technologies such as building information modelling, artificial intelligence (AI), virtual reality (VR) and AR have emerged and can be used to address this gap. AR application has been acknowledged as one of the most impactful technologies in the construction digitalization process. However, a comprehensive understanding of the AR application, its areas of effectiveness and overarching implications in a construction project life cycle remain vague. Therefore, this study uses an integration of systematic literature review and thematic analysis techniques to identify the phases of a construction project life cycle in which AR is the most effective, the current issues and problems of the conventional methods, the augmented parameters, the immediate effects of using AR on each phase and, eventually, the overall influence of AR on the entire project. Nvivo qualitative data analysis software was used to code, categorize and create themes from the collected data. The result of data analysis was used to develop four principal frameworks of the AR applications – design and constructability review session construction operation construction assembly and maintenance and defect inspection and management – and the gap analysis along with the future research agenda. The findings of this study indicated that the application of AR can be most effective in the following four stages of a project life cycle: design and constructability review session construction operation construction assembly and site management and maintenance, including site management and defect inspection. The results also showed that the application of AR technology in the construction industry can align and address building industry objectives by various elements such as: reducing project costs through the application of digital technologies, saving time, meeting deadlines and reduction in project delays through integrated, live scheduling and increased safety and quality of the construction work and workers. One of the main limitations of this study was the lack of materials and resources on the downfalls and shortcomings of using immersive technologies, AR, in the construction project life cycle. In addition, most of the reviewed papers were focused on the experiments with simulations and in the lab environment, rather than real experiments in real construction sites and projects. This may cause limitations and inaccuracy of the collected and reported data. The results of this study indicated that the application of AR technology in construction industry can align and address building industry objectives by various elements such as: reducing project costs through the application of digital technologies saving time meeting deadlines and reduction in project delays through integrated, live scheduling and increased safety and quality of the construction work and workers. Application of AR in the various stages of a project life cycle can increase the safety and quality of the construction work and workers. The reviewed literature indicated that substantial research and studies are yet to be done, to demonstrate the full capacity and impact of these emerging technologies in the field. The collected data and literature indicate that amongst the digital technologies, AR is one of the least researched topics in the field. Therefore, this study aims to examine the application of AR in construction projects’ life cycle to identify the stages and practices of a project life cycle where AR and its capabilities can be exploited and to identify the respective problems and issues of the conventional methods and the ways in which AR can address those shortcomings. Furthermore, this study focuses on identifying the overall outcome of AR applications in a construction project in terms of cost and time efficiency, process precision and safety.
Publisher: MDPI AG
Date: 13-11-2020
Abstract: Occupational Health and Safety (OHS)-related injuries are vexing problems for construction projects in developing countries, mostly due to poor managerial-, governmental-, and technical safety-related issues. Though some studies have been conducted on OHS-associated issues in developing countries, research on this topic remains scarce. A review of the literature shows that presenting a predictive assessment framework through machine learning techniques can add much to the field. As for Malaysia, despite the ongoing growth of the construction sector, there has not been any study focused on OHS assessment of workers involved in construction activities. To fill these gaps, an Ensemble Predictive Safety Risk Assessment Model (EPSRAM) is developed in this paper as an effective tool to assess the OHS risks related to workers on construction sites. The developed EPSRAM is based on the integration of neural networks with fuzzy inference systems. To show the effectiveness of the EPSRAM developed, it is applied to several Malaysian construction case projects. This paper contributes to the field in several ways, through: (1) identifying major potential safety risks, (2) determining crucial factors that affect the safety assessment for construction workers, (3) predicting the magnitude of identified safety risks accurately, and (4) predicting the evaluation strategies applicable to the identified risks. It is demonstrated how EPSRAM can provide safety professionals and inspectors concerned with well-being of workers with valuable information, leading to improving the working environment of construction crew members.
Publisher: Springer Science and Business Media LLC
Date: 30-08-2023
DOI: 10.1007/S12273-023-1045-X
Abstract: The application of machine learning (ML) modelling in daylight prediction has been a promising approach for reliable and effective visual comfort assessment. Although many advancements have been made, no standardized ML modelling framework exists in daylight assessment. In this study, 625 different building layouts were generated to model useful daylight illuminance (UDI). Two state-of-the-art ML algorithms, eXtreme Gradient Boosting (XGBoost) and random forest (RF), were employed to analyze UDI in four categories: UDI- f (fell short), UDI- s (supplementary), UDI- a (autonomous), and UDI- e (exceeded). A feature (internal finish) was introduced to the framework to better reflect real-world representation. The results show that XGBoost models predict UDI with a maximum accuracy of R 2 = 0.992. Compared to RF, the XGBoost ML models can significantly reduce prediction errors. Future research directions have been specified to advance the proposed framework by introducing new features and exploring new ML architectures to standardize ML applications in daylight prediction.
Publisher: Elsevier BV
Date: 12-2023
Publisher: Elsevier BV
Date: 08-2023
Publisher: Emerald
Date: 03-12-2021
Abstract: The purpose of this study is to identify and analyse the key measurable returning factors, value drivers and strategic benefits associated with building information modelling (BIM) return on investment (ROI). The findings of this study provide researchers and practitioners with up-to-date information in formulating appropriate strategies to quantify the monetary value of BIM. The suggested research agenda provided would also advance what is presently a limited body of knowledge relating to the evaluation of BIM ROI. To fill the identified gap, this study develops a comprehensive systematic review of mainstream studies on factors affecting BIM ROI published from 2000 to 2020. A total of 23 academic records from different sources such as journals, conference proceedings, dissertation and PhD theses were identified and thoroughly reviewed. The reported BIM ROI ranged greatly from −83.3% to 39,900%. A total of 5 returning factors, namely, schedule reduction and compliance, productivity improvement, request for information reduction, rework reduction and change orders reduction were identified as the most commonly reported factors that influence BIM ROI. Four quantification techniques including general assumptions-based theoretical model, perceived BIM ROI based on survey, factors affecting BIM ROI with no reported ROI and quantified BIM ROI based on a case study were observed and pointed out in the review, together with their limitations. Finally, three major gaps were raised as the lack of consideration on the likelihood of BIM assisting in a construction project, intangible returning factors influencing BIM-based projects and industry standards in benchmarking BIM ROI. The outcomes of this study would assist practitioners by providing the current evaluation techniques that address the limitations with BIM investment and present issues relating to the economic evaluation of BIM in the construction industry. It is also expected that presenting a deeper and wider perspective of the research work performed until now will direct a more focussed approach on productivity improvement efforts in the construction industry. This study identifies and analyses the key measurable returning factors, value drivers and strategic benefits associated with BIM ROI on an industry scale rather than a particular organisation or a project scale.
Publisher: MDPI AG
Date: 22-05-2023
DOI: 10.3390/BUILDINGS13051355
Abstract: Although many studies have focused on digital transformation and sustainability within the realm of project management, there has been a lack of research that comprehensively reviews the current state of the art of the aforementioned subject using a holistic approach. This oversight h ers the amalgamation of DT and sustainability in project management, waning the steps to be taken for the realisation of a smart and sustainable built environment. To fill the identified knowledge gap, this study presents a science mapping approach to meticulously examine the literature published on DT and sustainability within the realm of project management. In doing so, a bibliometric review together with a comprehensive Scientometric mapping analysis was carried out on the literature published from 2011 to 2022. The findings obtained in this study provide insightful accounts for both project managers and academics. Project managers are not only enlightened on rev ing their business models but are also given insights into utilising digital strategies for bringing the maximum level of sustainability into their projects. Meanwhile, researchers are given insight into the emerging trends, timelines, and emerging streams that will be explored in future endeavours.
Publisher: Emerald
Date: 20-12-2022
DOI: 10.1108/ECAM-07-2022-0665
Abstract: This research aims to develop an automated and optimization algorithms (OAs)-integrated 4D building information modeling (BIM) approach and a prototype and enable construction managers and practitioners to estimate the time of compound elements in building projects using the resource specification technique. A 4D BIM estimation process was first developed by applying the resource specification and geometric information from the BIM model. A suite of OA including particle swarm optimization, ant colony, differential evolution and genetic algorithm were developed and compared in order to facilitate and automate the estimation process. The developed processes and porotypes were linked and integrated. The OA-based automated 4D BIM estimation prototype was developed and validated through a real-life construction project. Different OAs were applied and compared, and the genetic algorithm was found as the best performing one. The prototype was successfully linked with BIM timeliner application. By using this approach, the start and finish dates of all object-based activities are developed, and the project completion time is automatically estimated. Unlike conventional construction estimation methods which need various tools and are error prone and time-consuming, the developed method bypasses the existing time estimation tools and provides the integrated and automated process with BIM and machine learning algorithms. Furthermore, this approach integrates 4D BIM applications into construction design procedures, connected with OA automation.
Publisher: Emerald
Date: 17-09-2021
DOI: 10.1108/ECAM-04-2021-0286
Abstract: Green walls (GWs), comprising living walls and green facades, have been touted as environmentally friendly products in architectural design. GWs can be viable in every aspect of sustainability they provide residents of buildings with a wide range of economic, social and environmental benefits. Despite this, the adoption rate of GW is still in its infancy stage, and the existing literature concerning the hindrances inhibiting GW adoption is very limited. To address these gaps, the aim of this paper is to identify and prioritize the hindrances to GW adoption in Hong Kong. After identifying 17 hindrances through an in-depth review of literature, the fuzzy Delphi method (FDM) is employed to refine the hindrances based on the local context with the help of 21 qualified experts in the field. Subsequently, Fuzzy Parsimonious Analytic Hierarchy Process (FPAHP) is exploited as a recently developed technique to prioritize the identified hindrances. Results reveal that the most significant hindrances to the adoption of GW are maintenance cost, high installation cost, difficulties in maintenance, sophisticated implementation and inducement to fire. Findings call for scholars to address ways to improve GW installation practices and methods in order to eradicate the hindrances and provide lessons for policymakers, assisting them in facilitating the larger-scale adoption of GW. Considering the dearth of studies on hindrances to the adoption of GWs, this paper provides a comprehensive outlook of the issue, providing knowledge that can be used as a building block for future scholars within the field. It also provides valuable insights for stakeholders within the construction industry about the hindrances to the adoption of GWs which could direct their efforts toward better implementation of it.
Publisher: Emerald
Date: 08-11-2022
DOI: 10.1108/ECAM-06-2022-0551
Abstract: To come up with a prudent decision on the installation of an appropriate green wall (GW) on buildings, this study presents a novel decision-making algorithm. The proposed algorithm considers the importance of barriers h ering GW adoption, as well as their relationships with regard to different types of GWs existing in a contextual setting. The proposed methodological approach is based on the integration of qualitative and quantitative techniques by employing focus group discussion, fuzzy-based best-worst method and fuzzy TOPSIS. Based on the experiences of qualified experts involved in related projects in Hong Kong, the following conclusions are drawn: (1) cost, installation and maintenance-related barriers are perceived to have the highest importance, (2) modular living wall system is the most suitable GW system for the context of Hong Kong and (3) existing barriers are found to have a pivotal role in the ranking of the most suitable GW systems. The findings provide valuable insight not only for policymakers and stakeholders, but also for establishing a methodological approach that can assist decision-makers in identifying the most beneficial GW system rather than the most applicable one. This could have significant implications and introduce potential changes to the common way of practice within the industry and lay the foundation for wider adoption of GW. While previous studies have investigated the sustainability-related issues of GW façade applications, the current body of knowledge is deprived of a comprehensive methodological approach for the selection of the most suitable GW systems.
Publisher: Elsevier BV
Date: 02-2022
Publisher: MDPI AG
Date: 03-04-2023
DOI: 10.3390/BUILDINGS13040952
Abstract: Sewer pipeline failures pose significant threats to the environment and public health. To tackle these repercussions, many deterioration models have been developed to predict the conditions of sewer pipes, most of which are based on CCTV inspection reports. However, these reports are prone to errors due to their subjective nature and human involvement. More importantly, there are insufficient data to develop prudent deterioration models. To address these shortcomings, this paper aims to develop a CCTV-based deterioration model for sewer pipes using Artificial Intelligence (AI). The AI-based model relies on the integration of an unsupervised, multilinear regression technique and Weibull analysis. Findings derived from the Weibull deterioration curve indicate that the useful service life for concrete and vitrified clay pipes are 79 years and 48 years, respectively. The regression models show that the R2 value for vitrified clay sewer pipes, concrete sewer pipes, and ductile iron sewer pipes are 71.18%, 71.47%, and 81.51%, respectively, and 73.69% for concrete stormwater pipes. To illustrate the impact of various factors on sewer pipes, sensitivity analyses under different scenarios are conducted. These analyses indicate that pipe diameter has a significant influence on sewer pipe deterioration, with little impact on stormwater pipes. These findings would guide decision makers in identifying critical pipes and taking necessary precautionary measures. Further, this provides a sound basis for prioritizing maintenance actions, which would pave the way for designing sustainable urban drainage systems for cities.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for SAEED REZA MOHANDES.