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This study aims to develop a predictive model using machine learning techniques to forecast construction dispute outcomes, thereby minimizing economic and social losses and promoting amicable settlements between parties.

This study develops a novel conceptual model incorporating project characteristics, root causes, and underlying causes to predict construction dispute outcomes. Utilizing a dataset of arbitration cases in Türkiye, the model was tested using five machine learning algorithms namely Logistic Regression, Support Vector Machines, Decision Trees, K-Nearest Neighbors, and Random Forest in a Python environment. The performance of each algorithm was evaluated to identify the most accurate predictive model.

The analysis revealed that the Support Vector Machine algorithm achieved the highest prediction accuracy at 71.65%. Twelve significant variables were identified for the best model namely, work type, root causes, delays from a contractor, extension of time, different site conditions, poorly written contracts, unit price determination, penalties, price adjustment, acceptances, delay of schedule, and extra payment claims. The study’s results surpass some existing models in the literature, highlighting the model’s robustness and practical applicability in forecasting construction dispute outcomes.

This study is unique in its consideration of various contract, dispute, and project attributes to predict construction dispute outcomes using machine learning techniques. It uses a fact-based dataset of arbitration cases from Türkiye, providing a robust and practical predictive model applicable across different regions and project types. It advances the literature by comparing multiple machine learning algorithms to achieve the highest prediction accuracy and offering a comprehensive tool for proactive dispute management.

The aim of this study is to measure the impact of the factors affecting construction labor productivity by focusing on different types of construction works during and after the COVID-19 pandemic in Turkey, as well as discuss solutions and immediate actions.

This research was conducted in two steps. First, a quantitative survey was carried out to determine the dimension of factors negatively affecting construction labor productivity and the loss rate of different construction works from the employee perspective. The factors were identified through a literature review. The crucial relationships were highlighted as a result of a statistical analysis. Second, a survey was performed to determine the loss rate through a comparison of man-hour values before and after the beginning of the pandemic from the employer perspective. After an analysis and comparison of the results, semi-structured interviews were performed to discuss all findings and discover ways to mitigate the impacts of COVID-19 on construction labor productivity.

The results of the study clearly show that construction labor productivity was deeply affected by the coronavirus disease (COVID-19) pandemic. Legal obligations, such as social distancing, wearing masks, and limitations on the number of workers, have been major drivers for lower labor productivity. Such obligations have a profound impact on interior construction works, especially based on teamwork. Concerning employer and labor-related factors, problems with getting payments on time, loss of income, and financial hardships are the leading factors resulting in decreased worker performance. Excavation, insulation, and plastering works were determined as the most affected construction works under the influence of the COVID-19 pandemic.

The quantitative portion of this study is limited to a sample of respondents in the Turkish construction industry. Further research is necessary to provide an in-depth review into construction labor productivity in other countries with a larger respondent sample. Another limitation is sourced by the dynamics of the COVID-19 pandemic, which may turn out that some findings are outdated. Despite these limitations, the insights from this study may enable employers to understand the major drivers and deep impacts of labor productivity loss by uncovering the main vulnerabilities during the pandemic. Recommended measures may also help policy-makers and stakeholders in the construction industry take necessary and immediate actions to ensure better construction labor productivity.

The study may contribute to a better understanding of a pandemic's impact on labor productivity by focusing on both employee and employer perspectives, especially in developing countries. The paper may help employers decide which priority measures are required for each construction work separately. The study is crucial not only for minimizing the negative effects of the COVID-19 outbreak on labor productivity but also for preparing for the post-pandemic era.