This study aims to identify the causes of delays in public construction projects in Thailand, a developing country. Increasing construction durations lead to higher costs, making it essential to pinpoint the causes of these delays. The research analyzed 30 public construction projects that encountered delays. Delay causes were categorized into four groups: contractor-related, client-related, supervisor-related, and external factors. A questionnaire was used to survey these causes, and the Relative Importance Index (RII) method was employed to prioritize them. The findings revealed that the primary cause of delays was contractor-related financial issues, such as cash flow problems, with an RII of 0.777 and a weighted value of 84.44%. The second most significant cause was labor issues, such as a shortage of workers during the harvest season or festivals, with an RII of 0.773. Additionally, various algorithms were used to compare the Relative Importance Index (RII) and four machine learning methods: Decision Tree (DT), Deep Learning, Neural Network, and Naïve Bayes. The Deep Learning model proved to be the most effective baseline model, achieving a 90.79% accuracy rate in identifying contractor-related financial issues as a cause of construction delays. This was followed by the Neural Network model, which had an accuracy rate of 90.26%. The Decision Tree model had an accuracy rate of 85.26%. The RII values ranged from 68.68% for the Naïve Bayes model to 77.70% for the highest RII model. The research results indicate that contractor financial liquidity and costs significantly impact construction operations, which public agencies must consider. Additionally, the availability of contractor labor is crucial for the continuity of projects. The accuracy and reliability of the data obtained using advanced data mining techniques demonstrate the effectiveness of these results. This can be efficiently utilized by stakeholders involved in construction projects in Thailand to enhance construction project management.

The interest in using project management office (PMO) services in organizations to manage their construction projects is growing in light of rising economic, technological, and social developments based on their ability to achieve organizational goals while avoiding risks. Accordingly, organizations use PMO services to manage their technical and financial project issues to periodically evaluate PMO performance and services in a scientific, practical, and measurable way to ensure successful project path via PMO. Therefore, this research aims to develop a performance evaluation system that enables organizations to follow up and evaluate the PMO performance to ensure that PMO manages the organizations’ expectations and goals successfully according to certain quality, scope, and cost. The study builds on significant findings in PMO competence indexes as evaluation matrix, which includes five basic categories with 136 indexes covering the project life cycle. The matrix was developed based on literature analysis and supplemented with experts’ interviews in construction management. The developed robust competency-based index (RCI) for directive PMO supports the organizations to conduct client satisfaction, correction, or partial/total change of the PMO’s competence flow within five construction project life cycle and process, i.e. governance, portfolio, information, execution, and contract issues.

Sustainability in road construction projects is hindered by the extensive use of non-renewable materials, high greenhouse gas emissions, risk cost, and significant disruption to the local community. Sustainability involves economic, environmental, and social aspects (triple bottom line). However, establishing metrics to evaluate economic, environmental, and social impacts is challenging because of the different nature of these dimensions and the shortage of accepted indicators. This paper developed a comprehensive method considering all three dimensions of sustainable development: economic, environmental, and social burdens. Initially, the economic, environmental, and social impact category indicators were assessed using the Life cycle approach. After that, the Analytic Hierarchy Process (AHP) method and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) were utilized to prioritize the alternatives according to the acquired weightings and sustainable indicators. The steps of the AHP method involve forming a hierarchy, determining priorities, calculating weighting factors, examining the consistency of these assessments, and then determining global priorities/weightings. The TOPSIS method is conducted by building a normalized decision matrix, constructing the weighted normalized decision matrix, evaluating the positive and negative solutions, determining the separation measures, and calculating the relative closeness to the ideal solution. The selected alternative performs the highest Relative Closeness to the Ideal Solution. Lastly, a case study was undertaken to validate the proposed method. In three alternatives in the case study (Cement Concrete, Dense-Graded Polymer Asphalt Concrete, and Dense-Graded Asphalt Concrete), option 3 showed the most sustainable performance due to its highest Relative Closeness to the Ideal Solution. Integrating AHP and TOPSIS methods combines both strengths, including AHP’s structured approach for determining criteria weights through pairwise comparisons and TOPSIS’s ability to rank choices based on their proximity to an ideal solution.