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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.

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.

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.

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.

Cost overruns remain a persistent problem in the construction industry. Although various cost management strategies have been implemented, innovative approaches are still required. Therefore, the authors attempted to introduce and test a new cost management strategy for the construction industry.

Zero-based budgeting (ZBB) is one such method whose effectiveness has been proven in different industries over many years. Therefore, the authors initially developed two different frameworks related to the integration of ZBB into a multinational construction contractor and the application process of ZBB for a construction project in this study. Then, the effectiveness and feasibility of the proposed frameworks are tested via an actual field study in a mega construction project.

The results show savings of 0.81% of the total project budget and 4.74% of the focused cost items by following the ZBB framework compared to the traditionally estimated project budget. The feedback received from the employees in the construction company shows that ZBB could be efficiently implemented during ongoing construction projects.

The authors believe that implementing new cost management strategies such as ZBB will open doors to deal with the complex cost overrun issues and improve construction cost performances.

This manuscript is the first actual application of the ZBB cost management approach in the construction industry.

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.

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.

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.

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.

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.