Shahab Shoar

Bio: Shahab Shoar is an academic researcher. The author has contributed to research in topics: Fault tree analysis & Causal loop diagram. The author has an hindex of 6, co-authored 10 publications receiving 89 citations.

A hybrid SD-DEMATEL approach to develop a delay model for construction projects

Abstract: Purpose The purpose of this paper is to develop a model for complex interconnected structure of various factors interacting with delay in order to identify the most important factors influencing and influenced by delay based on their interrelations. Design/methodology/approach Reviewing literature and interviewing with local experts selected from the Iranian construction industry, top 58 delay factors were identified and categorized into six major groups. The interrelations among these factors were, then, modeled using the system dynamics (SD) approach. The resulting causal loop diagrams obtained from SD were used subsequently for identifying the most significant factors influencing and influenced by delay through the Decision Making Trial and Evaluation Laboratory (DEMATEL) method. The impact of factors on each other was finally determined based on the opinions of 63 experts selected from the Iranian community of consultants, contractors, and clients. Findings According to the analysis, eight out of the 58 factors were identified as the most influencing factors on delay, and nine factors were found to be the most influenced factors by delay in the field of delay analysis. The study also concluded that factors related to labors are the most important and influential factors. In addition, factors related to client were the most influencing factors and external-related factors were the least important ones. At the end, some recommendations to reduce variation of delay in the construction projects are presented as well. Originality/value Considering the interconnected structure of the factors, the paper identifies the most important factors interacting with time delay in construction projects.

Application of fuzzy fault tree analysis to identify factors influencing construction labor productivity: a high-rise building case study

Abstract: The aim of this research is to develop a systematic approach to identify and prioritize the most influencing factors on labor productivity in a construction project, with respect to their interrelations, and also investigate different scenarios which can affect it. In the first step, factors influencing construction labor productivity were identified through reviewing previous researches. Applying a group of experts, the most important factors were then determined using their relative importance index in the second step. In the third step, the interrelations among factors were determined through several sessions and interviewing those experts. Finally, the efficiency of the proposed methodology is proved by implementing in a real high rise building construction project. In this step, the selected factors from previous steps were used subsequently for analyzing their impact on labor productivity through fuzzy fault tree analysis. The probability of occurrence of events was determined according to the opinions of four members of the project management team who involved in that project. The most critical causes were also identified using importance analysis. It is believed that using the proposed methodology, appropriate response strategies could be adopted against the identified critical events to enhance the overall productivity of a construction project.

Modeling Social Impacts of High-Rise Residential Buildings during the Post-Occupancy Phase Using DEMATEL Method: A Case Study

Abstract: There are numerous risks associated with high-rise buildings, which not only affect stakeholders during the design and construction phase but also impact the occupants and the surrounding environment during the post-occupancy phase. While previous studies examined the risks of high-rise building construction, less attention has been paid to the diverse impacts of high-rise buildings on their occupants. To fill this gap, this study applied a mixed-method approach (both quantitative and qualitative) to identify and prioritize their most significant social impacts. First, the possible social impacts of these buildings were identified via a literature review. The interrelationships among the identified factors were then determined by drawing on the opinions of relevant experts. Next, through the quantitative phase, the high-rise residential buildings of District 22 of Tehran were considered as a case study, and according to the opinions of 230 chosen residents, the level of influence of factors on one another was determined. The DEMATEL approach was employed subsequently to analyze the data and identify the most important and influential factors. Finally, through the qualitative phase, in-depth interviews were conducted with residents to explain and validate the results. The most significant and influential impacts identified by this study were anti-social behavior, lack of social cohesion, and lack of social contact with neighbors. This study assists designers and policymakers to adopt strategies that could mitigate the identified impacts and improve occupants’ social wellbeing more efficiently.

Quantitative assessment of risks on construction projects using fault tree analysis with hybrid uncertainties

Abstract: The purpose of this paper is to present a fault tree (FT)-based approach for quantitative risk analysis in the construction industry that can take into account both objective and subjective uncertainties.,In this research, the identified basic events (BEs) are first categorized based on the availability of historical data into probabilistic and possibilistic. The probabilistic and possibilistic events are represented by probability distributions and fuzzy numbers, respectively. Hybrid uncertainty analysis is then performed through a combination of Monte Carlo simulation and fuzzy set theory. The probability of occurrence of the top event is finally calculated using the proposed FT-based hybrid uncertainty analysis method.,The efficiency of the proposed method is demonstrated by implementing in a real steel structure project. A quantitative risk assessment is performed for weld cracks, taking into account of both types of uncertainties. An importance analysis is finally performed to evaluate the contribution of each BE to the probability of occurrence of weld cracks and adopt appropriate response strategies.,In this research, the impact of objective (aleatory) dependence between the occurrences of different BEs and subjective (epistemic) dependence between estimates of the epistemically uncertain probabilities of some BEs are not considered. Moreover, there exist limitations to the application of fuzzy set rules, which were used for aggregating experts’ opinions and ranking purposes of the BEs in the FT model. These limitations can be investigated through further research.,It is believed that the proposed hybrid uncertainty analysis method presents a robust and powerful tool for quantitative risk analysis, as both types of uncertainties are taken into account appropriately.

Machine learning-aided engineering services' cost overruns prediction in high-rise residential building projects: Application of random forest regression

Abstract: Current approaches to automating cost estimation mainly focus on construction costs. Yet, the two main services provided by design firms, namely ‘designing the project’, and ‘supervision of construction operations’ labelled as engineering services, despite their comparatively low cost, can significantly affect the total cost of construction projects as they can engender reworks, changes and disputes on project participants during the subsequent stages of the project. Continuous evaluation of engineering services' cost overruns (ESCO) is quintessentially important in order to prevent consequential problems later on in the project's development and use. Consequently, this research proposes a robust random forest (RF) regression model to predict ESCOs considering both project-related and organizational-related variables. A database consisting of 95 high-rise residential building projects designed during the past eight years in Iran, along with 12 related variables, were collected to develop and validate the model. The results were also compared with those of support vector regression (SVR) and multiple linear regression (MLR), which indicated that with an R2 value of 0.8680 and mean-absolute-error (MAE) of 3.88, the RF regression model performs better than those baseline models, namely SVR and MLR. This research presents two main contributions to the existing body of knowledge. From the practical point of view, it provides an efficient tool for design firms enabling them to screen and prioritize their projects from the cost overrun standpoint and to devise a contingency plan for them. From the theoretical point of view, it revealed that to mitigate ESCOs, three key factors should be given thorough consideration, namely: ‘the level of computer-aided design technologies adoption’; ‘level of communication among the project team’; and scope definition adequacy’ – cumulatively, these three factors contribute to 52.35% of ESCO variations.

Roles of artificial intelligence in construction engineering and management: A critical review and future trends

Abstract: With the extensive adoption of artificial intelligence (AI), construction engineering and management (CEM) is experiencing a rapid digital transformation. Since AI-based solutions in CEM has become the current research focus, it needs to be comprehensively understood. In this regard, this paper presents a systematic review under both scientometric and qualitative analysis to present the current state of AI adoption in the context of CEM and discuss its future research trends. To begin with, a scientometric review is performed to explore the characteristics of keywords, journals, and clusters based on 4,473 journal articles published in 1997–2020. It is found that there has been an explosion of relevant papers especially in the past 10 years along with the change in keyword popularity from expert systems to building information modeling (BIM), digital twins, and others. Then, a brief understanding of CEM is provided, which can be benefited from the emerging trend of AI in terms of automation, risk mitigation, high efficiency, digitalization, and computer vision. Special concerns have been put on six hot research topics that amply the advantage of AI in CEM, including (1) knowledge representation and reasoning, (2) information fusion, (3) computer vision, (4) natural language processing, (5) intelligence optimization, and (6) process mining. The goal of these topics is to model, predict, and optimize issues in a data-driven manner throughout the whole lifecycle of the actual complex project. To further narrow the gap between AI and CEM, six key directions of future researches, such as smart robotics, cloud virtual and augmented reality (cloud VR/AR), Artificial Intelligence of Things (AIoT), digital twins, 4D printing, and blockchains, are highlighted to constantly facilitate the automation and intelligence in CEM.

Uncertainty handling in fault tree based risk assessment: State of the art and future perspectives

Abstract: Risk assessment methods have been widely used in various industries, and they play a significant role in improving the safety performance of systems. However, the outcomes of risk assessment approaches are subject to uncertainty and ambiguity due to the complexity and variability of system behaviour, scarcity of quantitative data about different system parameters, and human involvement in the analysis, operation, and decision-making processes. The implications for improving system safety are slowly being recognised; however, research on uncertainty handling during both qualitative and quantitative risk assessment procedures is a growing field. This paper presents a review of the state of the art in this field, focusing on uncertainty handling in fault tree analysis (FTA) based risk assessment. Theoretical contributions, aleatory uncertainty, epistemic uncertainty, and integration of both epistemic and aleatory uncertainty handling in the scientific and technical literature are carefully reviewed. The emphasis is on highlighting how assessors can handle uncertainty based on the available evidence as an input to FTA.

Causes of delays on construction projects: a comprehensive list

Abstract: The purpose of this paper is to present a systematic review of studies on CPD published between 1985 and 2018.,Before identifying common CPD, research trends were examined in terms of the number of publications in selected journals, as well as the contributions made by countries, institutions and researchers.,The findings reveal that researchers from developing countries have contributed the most to identifying the causes of CPD. A total of 149 causes of CPD were identified in a thorough review of 97 selected studies. Weather/climate conditions, poor communication, lack of coordination and conflicts between stakeholders, ineffective or improper planning, material shortages, financial problems, payment delays, equipment/plant shortage, lack of experience/qualification/competence among project stakeholders, labour shortages and poor site management were identified as the ten most common CPDs.,Being the first study of its type, this study provides insight into the research output related to this area and identifies a common set of CPDs, which may provide a better understanding of the key areas requiring attention where steps should be taken to minimise or control factors causing delays in construction projects.

Delay Causes and Emerging Digital Tools: A Novel Model of Delay Analysis, Including Integrated Project Delivery and PMBOK

Abstract: Delay is one of the main challenges of construction projects, and there is still much to overcome in order to reach near zero delay in all construction projects. This project aims to conduct a systematic critical review including a bibliography analysis on delay literature in construction. The main questions consider what has been learnt from a decade investigating delay causes and effects in the construction literature and what factors have been missed in the literature. This paper also presents a new and challenging question regarding how digital tools and associated technologies may prevent any delay in construction projects, which can change the research direction from delay investigations to identifying prevention factors. The paper identifies the delay dataset, including 493 papers investigating delay in construction, and establishes a specific dataset of papers focusing on delay effects and causes (DEC), including 94 selected papers covering different factors examined in over 29 countries such as Iran, India, Turkey, Bangladesh, Saudi Arabia, the United Arab Emirates (UAE), Cambodia, Oman, Malaysia, Taiwan, China, Vietnam, the US, the UK, and Egypt. In addition, the paper identifies 30 critical factors with the frequency of occurrences over three times in the DEC dataset and computes their medians of ranking. This paper also discusses digital tools and methods that can be used for delay analysis and preventions, including MS Project, Oracle Primavera P6, and Open Plan by Deltek. The paper discusses the project schedule delay analysis from project management methodology perspectives. It also discusses the current method’s limitations and future directions, which are based on the identification of the deficiency areas. In total, four overlooked factors are identified and suggested, including faulty data analysis, unmatched structure of the research questionnaires with new knowledge and standards [e.g., Project Management Body of Knowledge (PMBOK)], overlooked effects of digital technologies [e.g., Digital twin, Navisworks, Building Information Model (BIM), Geographic Information System (GIS), and Integrated Project Delivery (IPD)], and ignored job-site technologies. In addition, the paper presents the DEC model for future studies, including four main key factors. These factors are resources (e.g., project budgets, labour, material, equipment, and digital tool), project context, stakeholders performance (e.g., owner/client, consultant/designer, contractor, vendor/supplier), and external factors (e.g., ground condition, site location, regulation, natural disaster), which may significantly affect delay prevention and should be concurrently considered in the future delay investigations, since they may be required for designing an effective mitigation strategy when these proof points are identified. This would significantly help to utilise digital systems to prevent time overruns in different construction contexts.

Barriers to the digitalisation and innovation of Australian Smart Real Estate: A managerial perspective on the technology non-adoption

Abstract: The real estate sector brings a fortune to the global economy. But, presently, this sector is regressive and uses traditional methods and approaches. Therefore, it needs a technological transformation and innovation in line with the Industry 4.0 requirements to transform into smart real estate. However, it faces the barriers of disruptive digital technology (DDT) adoption and innovation that need effective management to enable such transformation. These barriers present managerial challenges that affect DDT adoption and innovation in smart real estate. The current study assesses these DDTs adoption and innovation barriers facing the Australian real estate sector from a managerial perspective. Based on a comprehensive review of 72 systematically retrieved and shortlisted articles, we identify 21 key barriers to digitalisation and innovation. The barriers are grouped into the technology-organisation-external environment (TOE) categories using a Fault tree. Data is collected from 102 real estate and property managers to rate and rank the identified barriers. The results show that most of the respondents are aware of the DDTs and reported AI (22.5% of respondents), big data (12.75%) and VR (12.75%) as the most critical technologies not adopted so far due to costs, organisation policies, awareness, reluctance, user demand, tech integration, government support and funding. Overall, the highest barrier (risk) scores are observed for high costs of software and hardware (T1), high complexity of the selected technology dissemination system (T2) and lack of government incentives, R&D support, policies, regulations and standards (E1). Among the TOE categories, as evident from the fault tree analysis, the highest percentage of failure to adopt the DDT is attributed to E1 in the environmental group. For the technological group, the highest failure reason is attributed to T2. And for the organisational group, the barrier with the highest failure chances for DDT adoption is the lack of organisational willingness to invest in digital marketing (O4). These barriers must be addressed to pave the way for DDT adoption and innovation in the Australian real estate sector and move towards smart real estate.