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Unsafe behavior accounts for a major part of high accident rates in construction projects. The awareness of unsafe circumstances can help modify unsafe behaviors. To improve awareness in project teams, the present study proposes a framework for predicting safety performance before the implementation of projects.

The machine learning approach was adopted in this work. The proposed framework consists of two major phases: (1) data collection and (2) model development. The first phase involved several steps, including the identification of safety performance criteria, using a questionnaire to collect data, and converting the data into useful information. The second phase, on the other hand, included the use of the decision tree algorithm coupled with the k-Nearest Neighbors algorithm as the predictive tool along with the proposing modification strategies.

A total of nine safety performance criteria were identified. The results showed that safety employees, training, rule adherence and management commitment were key criteria for safety performance prediction. It was also found that the decision tree algorithm is capable of predicting safety performance.

The main novelty of the present study is developing an integrated model to propose strategies for the safety enhancement of projects in the case of incorrect predictions.

This research aims to measure the public sector comparator (PSC) to reach public–private partnership (PPP) projects' negotiable price range for water and sewage companies in Iran. PSC measurement drives the public sector to make valid decisions about costs.

Around 170 risks were primarily determined through studying numerous articles. Then, risk effects were specified by distributing questionnaires in two steps. The questionnaires are distributed among experts on PPP-related projects and the Monte Carlo simulation method is used for confidence factors of 70, 80 and 90%. PSC is measured based on these results to study cases of Sirjan’s sewerage and sewage purification systems.

11 risks were identified as the main risks that are effective on PSC, and project implementation costs were specified based on the modeling. The corruption of the private and public sectors was identified as the most effective risk in this research. It can affect a project’s cost up to 158% in the construction period and up to 134% in the operation period. Based on the obtained results, 63% of this risk’s cost goes to the public sector.

The originality of this research is the PSC measurement method and appointing the risk share of each private and public sector. The results of this research can be applied to all the infrastructure and PPP projects in Iran and other developing countries as a way for employers to estimate accurate negotiable price ranges.

Extended reality (XR) is an emerging technology, with its popularity rising in different industry sectors, where its application has been recently considered in construction safety. This study aims to investigate the applications of XR technologies in the safety of construction through projects lifecycle perspective.

Scientometric analysis was conducted to discover trends, keywords, contribution of countries and publication outlets in the literature. The content analysis was applied to categorize previous studies into three groups concerning the phase of lifecycle in which they used XR.

Results of the content analysis showed that the application of XR in the construction safety is mostly covered in two areas, namely, safety training and risk management. It was found that virtual reality was the most used XR tool with most of its application dedicated to safety training in the design phase. The amount of research on the application of augmented reality and mixed reality in safety training, and risk management in all phases of lifecycle is still insignificant. Finally, this study proposed three main areas for using the XR technologies regarding the safety issues in future research, namely, control of safety regulations and safety coordination in construction phase, and safety reports in the operation phase.

This paper inspected the utilization of all types of XR for safety in each phase of construction lifecycle and proposed future directions for research by addressing the safety challenges in each phase.

Demolition is a high-risk operation in construction projects that may lead to serious accidents. Risk assessment is a fundamental step in managing demolition risk and preventing casualties and financial losses. The present study aims to provide a framework to identify, analyse and evaluate the risks in building demolition operations.

According to previous studies and the use of expert knowledge, 10 possible risks of the building demolition operation were identified. Subsequently, these risks were assessed using a combination of fuzzy logic with fault tree analysis (FTA), criteria importance through inter-criteria correlation (CRITIC) and technique for order preference by similarity to ideal solution (TOPSIS). Then, the risks were classified with the help of a risk decision matrix (RDM), and appropriate treatment strategies were presented according to the level of importance of each risk.

Considering the obtained magnitude for each risk and its rating, building collapse and noise pollution were identified as the most and least significant risks, respectively. The results of this study were in good agreement with the data provided by the Iranian Ministry of Cooperatives, Labour and Social Welfare, as well as obtained results of the previous studies on demolition.

This paper provides a novel framework for assessing the risks in building demolition operations. The findings of this study help demolition project managers to manage the risks in their projects properly.