Oil and gas construction projects are complex and risky because of their dynamic environment. Furthermore, rising global energy demand has increased the need for trustworthy risk assessment models for such projects that can provide adequate and precise policy planning. Traditional risk assessments in oil and gas construction projects do not consider the interrelationships of factors in the best-fit models. The Decision Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Processes (ANP), called the DEMATEL-ANP approach, have been applied to other research disciplines to address this shortcoming. This method is able to construct a structural relationship among the different influence factors to visualize complex correlations. Thus, the purpose of this study is to showcase the DEMTAL-ANP risk assessment model to assess the overall risk factors of OGC projects. This study thus identifies the crucial risk criteria of such projects. Data were collected in 2016 through interviews with experts active in OGC projects in Iran. DEMATEL in this situation is used to determine the interdependencies’ relative strengths among the risks. The ANP method is applied to assess the relative importance of the risk factors and to determine the best strategy for implementation of a risk management program. The results presented in this study are a novel adaptation of the risk assessment methodology to OGC projects that determines the important risk factors that directly affect the project success, which in turn helps in formulation of policies for ensuring reliable energy supply planning.

Successful implementation of the lean concept as a sustainable approach in the construction industry requires the identification of critical drivers in lean construction. Despite this significance, the number of in-depth studies toward understanding the considerable drivers of lean construction implementation is quite limited. There is also a shortage of methodologies for identifying key drivers. To address these challenges, this paper presents a list of all essential drivers within three aspects of sustainability (social, economic, and environmental) and proposes a novel methodology to rank the drivers and identify the key drivers for successful and sustainable lean construction implementation. In this regard, the entropy weighted Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was employed in this research. Subsequently, an empirical study was conducted within the Malaysian construction industry to demonstrate the proposed method. Moreover, sensitivity analysis and comparison with the existing method were engaged to validate the stability and accuracy of the achieved results. The significant results obtained in this study are as follows: presenting, verifying and ranking of 63 important drivers; identifying 22 key drivers; proposing an MCDM model of key drivers. The outcomes show that the proposed method in this study is an effective and accurate tool that could help managers make better decisions.

In recent years, many researchers across the world have addressed the implementation of Building Information Modelling (BIM) in the energy assessment of the built environment. However, several potential issues still need to be resolved in order to utilise the benefits provided by BIM to a maximum degree. To fill this gap, a systematic literature review is conducted in this study to critically investigate the utilisation of BIM tools in energy assessment. To achieve the above-mentioned objective, after shortlisting the relevant papers published hitherto, using keyword searching, a systematic review was undertaken, including the application of BIM in the contexts of different countries, types of BIM tools, BIM and Life Cycle Assessment (LCA) integration, energy affiliations, stakeholders’ involvement and their roles, uncertainty, and sensitivity analysis. The outcomes show the most widely used and effective BIM tools in different types of construction projects in various countries. The review of the literature clearly shows that BIM tools can effectively be used in the assessment of energy performance of buildings. The article gives insight to engineers, architecture, and decision makers to carefully select appropriate BIM tools in terms of energy assessment.

Purpose: Proper identification of the key motivating factors (or key drivers) is needed to ensure successful adaption and implementation of the lean concept for construction projects. However, there lacks a study investigating the complex interrelationships existing among the key drivers contributing to Sustainable and Successful Lean Construction (SSLC) implementation for such projects. To address this shortcoming, this study aims to uncover the main critical key drivers towards the implementation of SSLC for the very first time by capturing the complexity of this vexing problem.Design/methodology/approach: In this study, a new hybrid framework is developed through the integration of Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Social Network Analysis (SNA). The novel developed framework is called the DSNA approach.Findings: Considering the case of Malaysian construction projects, the developed DSNA gives the following major outcomes: (1) Most important critical key drivers are seen to be optimization, continuous improvement, and, improve company culture, and (2) For SSLC adoption, the critical drivers impacting other key drivers are seen to be “improve teamwork”, “reduce leadership conflict”, and “improve company culture”, thereby demanding more attention.Practical implications: The outcomes of this study give insight for decisions and policymakers in the construction industry regarding critical key drivers and their complex interrelationships towards the further adoption of SSLC, promoting the sustainability paradigm within the respective sector.Originality/value: This paper not only presents a list of critical drivers and the corresponding association among them towards SSLC adoption, but also proposes DSNA as a novel approach for uncovering the complex interrelationship existing in an intricate problem, improving the intricate process of decision-making.