Recently, risk assessment has become one of the most challenging issues in the areas of construction and public-private partnerships (PPPs). To address risk assessment issues, various decision-making techniques have been proposed, each with its own specific disadvantages and advantages. This paper investigates step-wise weight assessment ratio analysis (SWARA), complex proportional assessment (COPRAS), fuzzy analytic network process (FANP), fuzzy analytic hierarchy process (FAHP), fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS), simple additive weighting (SAW) and evaluation based on distance from average solution (EDAS) in order to define how various multi-attribute decision-making (MADM) methods compare when used for risk assessment in PPP projects. For this study, 5 risk assessment criteria and 10 types of risk used in Iranian highway PPP projects were selected. Four suitability and applicability tests were used to measure agreement between the rankings derived from the MADM methods. Final results show that all techniques had approximately the same rankings of risk assessment, with the SWARA, COPRAS, and EDAS methods performing slightly better. The findings of this study will help the parties in PPP and construction projects to select the best risk assessment method.

One of the major issues of the construction industry has been the “reworks” that affect the time, quality, and cost of projects. Therefore, reworks and the ineffective use of site resources and materials will always result in significant losses on projects. The development of information technology has led to the widespread use of Building Information Modelling (BIM) to enhance the delivery of more sustainable building construction projects. The purpose of this study is to combine the Step-wise Weight Assessment Ratio Analysis (SWARA) method and BIM technologies to identify and reduce time delays caused by reworks in construction projects. Firstly, 49 rework causes in residential buildings were identified and ranked. Then, BIM was generated and compared to the initial model. It was observed that working hours were reduced by 4.6%. Moreover, using an Earned Value Management (EVM) system, a 0.06 increase in Schedule Performance Index (SPI) factor was illustrated. Results obtained by this study provide an effective step in reducing a project’s time in the construction industry.

Modern, well-educated and experienced policy-makers support and promote the use of environmentally friendly materials and resources. The use of green resources is an exceptional and inevitable strategy to meet the needs of a rapidly growing Earth population. The growing population raises the need for new housing construction and urban infrastructure development. Such substances in construction refer to green building materials (GBMs). The environmental impact is lower if GBMs replace non-GBMs. Here, ranking among GBMs can facilitate and support the selection process. This study aimed to contribute to the body of knowledge to introduce a method for identifying and prioritizing GBMs in the construction industry to use in green building. The required data were collected using existing literature, interviews and questionnaires. Relevant Sustainable Development Goals (SDGs) are the first criteria for assessing GBM selection criteria. Critical weighted GBM selection criteria are the second criteria for prioritizing GBMs. The results show that “Natural, Plentiful and Renewable”, “Affordability from cradle to gate” and “Affordability during operation” are the top three GBM selection criteria. The real case study helped select “Stramit Strawboard”, “Aluminium Composite Panels (ACPs)” and “Solar Roof Tiles” as the most suitable GBMs for use in the context of the study. The model and results presented in this study will help actors of the construction industry to select and use GBMs more quickly and thus achieve a better level of construction sustainability, as well as environmental friendliness, than before.

The complexity of public–private partnership (PPP) projects ensures that risks could emerge and spread in unpredictable ways if they are not well identified and managed. The emergence of PPP projects has brought major changes in the construction industry, the most notable being in procurement methods influencing risk allocation to private parties. Thus, it is crucial to have an effective risk management for public and private partners to eliminate or minimise risks. Formulating an effective risk management system is a crucial challenge faced by both of parties in order to minimise or optimise risks. The aim of this study was to investigate the process of risk identification of private partners in Malaysian PPP projects. Data were collected throughout a 2-month period using a survey with a sample of nine Malaysian companies engaged in PPP projects, and the survey results were analysed using mean scores. The findings indicate that due to a lack of knowledge and experience of Malaysian private partners in the risk identification process, a comprehensive database for risk identification is highly necessary for the private sector. Another issue emerging from the findings is that it may be reasonable to use a combination of risk identification tools for PPP projects with a high level of complexity. The findings of the present study can greatly assist public and private partners to select the most appropriate tools for risk identification at the early stages of PPP projects.

Completing a project according to the anticipated budget is one of the most important criteria in a successful construction project. As mega hospital construction projects face various kinds of limitation which affect the cost dramatically, many developing countries such as Iran have focused in these kinds of projects. Only a few studies have been conducted which are associated with cost overrun factors in hospital construction projects in developing countries. The aim of this study is to identify and rank cost overrun factors in mega hospital construction projects of Iran. Delphi and SWARA methods were used in order to align experts’ opinions in categorizing cost overrun factors and weigh the cost overrun factors, respectively. These factors were then allocated to consultants, employers and contractors. The results illustrated that “Unacceptable quality of work which leads to rework” with score of 0.458, “Not allocating sufficient budget from the government during the project” with score of 0.429 and “Lack of supervisors’ technical knowledge” with score of 0.44 were the most crucial factors in contractors, employers and consultant groups, respectively. The identified factors inform the main literature on hospital projects and results in decreasing cost overruns in the mentioned projects.

The cost, time and scope of a construction project are key parameters for its success. Thus, predicting these indices is indispensable. Correct and accurate prediction of cost throughout the progress of a project gives project managers the chance to identify projects that need revision in their schedules in order to result in the maximum benefit. The aim of this study is to minimize the shortcomings of the Earned Value Management (EVM) method using an Artificial Neural Network (ANN) and multiple regression analysis in order to predict project cost indices more precisely. A total of 50 road construction projects in Fars Province, Iran, were selected for analysis in this research. An ANN model was used to predict the projects’ cost performance indices, thereby creating a more accurate symmetry between the predicted and actual cost by considering factors that influence project success. The input data of the ANN model were analysed in MATLAB software. A multiple regression model was also used as another analytical tool to validate the outcome of the ANN. The results showed that the ANN model resulted in a lower Mean Squared Error (MSE) and a greater correlation coefficient than both the traditional EVM model and the multiple regression model.