This study develops an integrated model of the main contextual factors that affect sustainable delivery of megaprojects in Iran. The inputs to the model are based on a comprehensive literature review affecting the “triple bottom line” of sustainability, as measured in economic, environmental, and social costs. Innovation diffusion theory and extralogical laws of imitation inform the theoretical points of departure. The model inputs were customized for the context of Iran, and a structural equation model was developed, using data collected from 101 survey questionnaires. The findings identify a wide range of factors that directly impact megaproject sustainability, and these may be used by policymakers and practitioners within the Iranian construction industry to better manage sustainability outcomes. Principally, megaprojects would benefit from (1) the generation of sustainability awareness at the evaluation phase, (2) tackling corruption at the preparation phase, and (3) consolidating responsible project management practices at the usage phase. Moreover, these lessons may be more broadly applicable across the entire construction supply chain in Iran, as well as being transferable to other developing countries.

Extant literature extensively articulates the advantages of using remotely piloted aircrafts (RPAs) in a myriad of construction activities. Yet, the barriers that hinder their wider adoption on construction projects have received scant academic attention. This study aims at addressing this gap in the literature. This study reviews 59 papers published on the use of RPAs for construction activities and offers an evaluation of barriers to widespread adoption throughout the sector. Barriers are identified, collated and categorized into five thematic groups, namely, technical difficulties, restrictive regulatory environment, site-related problems, weather and organizational barriers. The paper contributes to knowledge by: signposting a need for reordering priorities when defining future research on RPAs, suggesting measures to address the barriers identified and providing pragmatic guidance for construction companies intending to use RPAs on their projects. Using the task–technology fit theory, the study uncovers that current RPA technology is an under-fit match for construction activities and represents a prominent barrier to adoption. This is a dissenting finding, given that past studies on RPAs have primarily focused upon addressing public acceptance, concerns and societal consequences. Enablers of the identified barriers are also collated from extant literature and contemporary practice and encapsulated in a conceptual model.

Purpose: This research aims to develop an approach to assess the reliability of integrated construction supply chainsviaan integrated model of building information modelling (BIM) and the lean supply chain (LSC). It reflects the synergistic workflow between BIM and LSC as a novel approach to improve the reliability of construction projects.Design/methodology/approach: This research evaluates the reliability of the BIM-LSC approach through a combination of entropy theory, set pair analysis (SPA), and Markov chains (EESM). An exploratory survey was conducted to collect data from 316 industry professionals experienced in BIM and LSC. Subsequently, multiple cycles of calculations were performed with indirect data inputs. Finally, a reliability evaluation index is established for the BIM-LSC approach and potential applications are identified.Findings: The results show that the EESM model of BIM-LSC developed in this study can handle not only supply chain reliability evaluation at a given state but also the prediction of reliability in supply chain state transitions due to changing project conditions. This is particularly relevant to the current environment of the construction project, which is characterised by an increasing level of complexity in terms of labour, technology, and resource interactions.Research limitations/implications: Future research could consider the accuracy and validity of the proposed model in real-life scenarios with by considering both quantitative and qualitative data across the entire lifecycle of projects.Practical implications: The research offers a model to evaluate the reliability of the BIM-LSC approach. The accuracy of BIM supply chain reliability analysis and prediction in an uncertain environment is improved.Originality/value: The BIM-LSC reliability evaluation and prediction presented in this study provides a theoretical foundation to enhance understanding of the BIM-LSC in the construction project context.

An accurate estimation of construction duration for the successful completion of infrastructure projects is as important a task as keeping a project within the stipulated budget and quality. This research aims at developing an automated approach for estimating the duration of construction for dam projects by virtue of artificial neural network (ANN). Through literature searches and expert interviews, variables that critically influence the construction of dam projects were identified. A wide range of data on Iranian dam projects was used to develop seven ANN models. Different datasets were used to achieve the best performance by assessing the RMSE and R² values as the reliability and validity indicators. Finally, a web-based automated prototype was developed and validated in order for stakeholders to estimate the duration of dam projects based on the ANN method and benefit from better infrastructure project management practices.