Purpose: Construction work involves high-risk activities and requires intense focus and physical exertion. Accordingly, working conditions at construction sites contribute to physical fatigue and mental stress in workers, which is the primary cause of accidents. This study aims to examine the relation between construction accidents and physiological variables, indicative of physical fatigue and mental stress.Design/methodology/approach: Four different real-time physiological values of the construction workers were measured including blood sugar level (BSL), electrodermal activity (EDA), heart rate (HR) and skin temperature (ST). The data were collected from 21 different workers during the summer and winter seasons. Both seasonal and hourly correlation analyses were performed between the construction accidents and the four physiological variables gathered.Findings: The analysis results demonstrate that BSL values of the workers are correlated inversely with construction accidents taking place before lunch break. In addition, except BSL a significant seasonal association between the physiological variables and construction accidents was found.Originality/value: It is disclosed that variations in physiological risk factors at certain working periods pose a high risk for construction workers. Therefore, efficient work-cycle rests can be arranged to provide frequent but short breaks for workers to overcome such issues. Besides, an early warning system could be introduced to monitor the real-time physiological values of the workers.

Quality problems are crucial in construction projects since poor quality might lead to delays, low productivity, and cost overruns. In case preventive actions are absent, a lack of quality results in a chain of problems. As a solution, this study deals with non-conformities proactively by adopting an AI-based predictive model approach. The main objective of this study is to provide an automated solution structured on the data recording system for the adverse impacts of construction quality failures. For this purpose, we collected 2527 non-conformance reports from 59 diverse construction projects to develop a predictive model regarding the cost impact of the quality problems. The first of three stages forming the backbone of the study determines crucial attributes linked to quality problems through a literature survey and the Delphi method. Secondly, the Analytical Hierarchy Process (AHP) and a Genetic Algorithm (GA) were used to determine the attribute weights. In the final stage, we developed models to predict the cost impacts of non-conformities, using Case-based Reasoning (CBR). We made a comparison between the developed models to select the most precise one. The results show that the performance of CBR-GA using an automated weighting model is slightly better than CBR-AHP based on a subjective weighting system, whereas the case is the opposite in standard deviation in forecasting the cost outcome of the quality failures. Using both automated and expert systems, the study forecasts the cost impact of failures and reveals the factors linked to poor record-keeping. Ultimately, we concluded that the outcome of non-conformities can be predicted and prevented using past events via the developed AI-based predictive model.

En tr As occupational accidents usually occur due to unsafe human behaviors in the construction industry, safety training is inevitably necessary for site personnel. On construction sites, various training methods including traditional and innovative ones, have been adopted to prevent accidents. In recent years, virtual safety training has been more prevalent because of providing highly engaging practice in a risk-free environment. Although these training tools have innumerable advantages in providing safety knowledge and awareness, they can be further improved. This study introduces a virtual safety training tool, V-SAFE.v2, to provide a more reliable and effective safety training for high-risk construction works. V-SAFE.v2 consists of three main modules; i) Training Module, ii) Testing Module 1, and iii) Testing Module 2. These modules are generated firstly to provide safety training for scaffolding and formwork activities and then to evaluate the safety performance of the trainees. An experiment was conducted with fifteen construction workers and ten engineers to measure the effectiveness of the training tool. The findings showed that V-SAFE.v2 is a reliable safety training tool for high-risk construction tasks as it supports collaboration, provides individual feedback, and repeatable practice. Also, the participants stated that V-SAFE.v2 has a great potential to reduce the falling from height accidents in the construction workplaces. As occupational accidents usually occur due to unsafe human behaviors in the construction industry, safety training is inevitably necessary for site personnel. On construction sites, various training methods including traditional and innovative ones, have been adopted to prevent accidents. In recent years, virtual safety training has been more prevalent because of providing highly engaging practice in a risk-free environment. Although these training tools have innumerable advantages in providing safety knowledge and awareness, they can be further improved. This study introduces a virtual safety training tool, V-SAFE.v2, to provide a more reliable and effective safety training for high-risk construction works. V-SAFE.v2 consists of three main modules; i) Training Module, ii) Testing Module 1, and iii) Testing Module 2. These modules are generated firstly to provide safety training for scaffolding and formwork activities and then to evaluate the safety performance of the trainees. An experiment was conducted with fifteen construction workers and ten engineers to measure the effectiveness of the training tool. The findings showed that V-SAFE.v2 is a reliable safety training tool for high-risk construction tasks as it supports collaboration, provides individual feedback, and repeatable practice. Also, the participants stated that V-SAFE.v2 has a great potential to reduce the falling from height accidents in the construction workplaces.

Purpose: The cash flow from government agencies to contractors, called progress payment, is a critical step in public projects. The delays in progress payments significantly affect the project performance of contractors and lead to conflicts between two parties in the Turkish construction industry. Although some previous studies focused on the issues in internal cash flows (e.g. inflows and outflows) of construction companies, the context of cash flows from public agencies to contractors in public projects is still unclear. Therefore, the primary objective of this study is to develop and test diverse machine learning-based predictive models on the progress payment performance of Turkish public agencies and improve the predictive performance of these models with two different optimization algorithms (e.g. first-order and second-order). In addition, this study explored the attributes that make the most significant contribution to predicting the payment performance of Turkish public agencies. Design/methodology/approach: In total, project information of 2,319 building projects tendered by the Turkish public agencies was collected. Six different machine learning algorithms were developed and two different optimization methods were applied to achieve the best machine learning (ML) model for Turkish public agencies' cash flow performance in this study. The current research tested the effectiveness of each optimization algorithm for each ML model developed. In addition, the effect size achieved in the ML models was evaluated and ranked for each attribute, so that it is possible to observe which attributes make significant contributions to predicting the cash flow performance of Turkish public agencies. Findings: The results show that the attributes “inflation rate” (F5; 11.2%), “consumer price index” (F6; 10.55%) and “total project duration” (T1; 10.9%) are the most significant factors affecting the progress payment performance of government agencies. While decision tree (DT) shows the best performance among ML models before optimization process, the prediction performance of models support vector machine (SVM) and genetic algorithm (GA) has been significantly improved by Broyden–Fletcher–Goldfarb–Shanno (BFGS)-based Quasi-Newton optimization algorithm by 14.3% and 18.65%, respectively, based on accuracy, AUROC (Area Under the Receiver Operating Characteristics) and F1 values. Practical implications: The most effective ML model can be used and integrated into proactive systems in real Turkish public construction projects, which provides management of cash flow issues from public agencies to contractors and reduces conflicts between two parties. Originality/value: The development and comparison of various predictive ML models on the progress payment performance of Turkish public owners in construction projects will be the first empirical attempt in the body of knowledge. This study has been carried out by using a high number of project information with diverse 27 attributes, which distinguishes this study in the body of knowledge. For the optimization process, a new hyper parameter tuning strategy, the Bayesian technique, was adopted for two different optimization methods. Thus, it is available to find the best predictive model to be integrated into real proactive systems in forecasting the cash flow performance of Turkish public agencies in public works projects. This study will also make novel contributions to the body of knowledge in understanding the key parameters that have a negative impact on the payment progress of public agencies.

Objectives: Understanding the impact of job demands and mental health disorders on individual job performance is crucial to achieving a more productive workforce and should be empirically investigated. Therefore, the main purpose of this study was to assess the impact of job demands and mental health disorders on individual job performance among construction workers. In addition, we examined the interaction effect between job demands and some demographic characteristics (income, marital status, experience, and area of residence) on the job performance of construction workers in 2 dimensions. Methods: For this purpose, a new conceptual model and 3 different hypotheses were introduced. A survey was designed and administered to 513 construction workers. Whereas the measurement items regarding demographic characteristics, job demands, and mental health disorders were addressed to construction workers, the last part related to job performance of construction workers was conducted with site managers of the workers to obtain more objective results. A structural equation modeling approach was adopted to assess the validity of the model and to test the hypotheses. Results: The results of this study show that whereas job demands have a significant and high impact on individual job performance, the effect of mental health disorders on job performance is significant at a moderate level. In addition, the demographic characteristics of marital status and area of residence have a significant interaction effect on job performance when combined with job demands. Conclusion: Providing individualized support systems, resources, and opportunities for construction workers can help mitigate the negative effects of excessive demands and mental disorders on labor productivity.

Due to poor performances in construction projects, there is a necessity to apply novel project management approaches instead of traditional ones. Recently, although new project management methods have emerged and applied in different industries, the construction industry has stayed behind in implementing these techniques. The agile Project Management (APM) approach is one of these methods, and it has been highly discussed recently. As developing countries considerably suffer from poor performance in construction projects, applying new project management methods has gained more importance in these territories. Even though different studies attempted to understand the applicability of the APM, it is also necessary to explore the potential benefits of the agile methods for the construction industry. For this purpose, we surveyed 140 construction experts for the prioritization of factors and received responses on the potential advantages of the APM regarding team and project performance in construction. The results achieved from the Iraq field study demonstrate that the APM could be feasible for the construction industry and could effectively bring solutions to the project performance issues. We also concluded that the adverse effects of poor project performance, such as quality defects, schedule variances, cost overruns, and low productivity, could be eliminated especially in the construction industry of developing countries, through APM approaches.