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.

Purpose: To reveal the impact mechanism of rural museum intervention on the construction of local identity of rural community residents, and provide practical reference for the protection and utilization of rural cultural identity. Methods: This study takes the Weijiapo Rural Museum in Luoyang, China as the research object, uses participatory observation and in-depth interview methods, and explains the specific characteristics of rural community resident identity construction through identity process theory (IPT). Results: (1) The impact of the intervention of rural museums on rural areas is reflected in four aspects: local spatial reconstruction, transformation of livelihood methods, reconstruction of social relationships, and evolution of cultural customs; (2) under the influence of rural museum construction, the representation of community residents’ identity has shown complex characteristics, with both positive and negative impacts coexisting; (3) the local identity of community residents affects their perception and attitude towards the construction of rural museums.