Irrigation and hydropower are among the most important sectors in the construction industry that propel the economic needs of a developing country like Vietnam. The construction of these projects often suffers from severe delays, leading to financial losses and other negative impacts on the economy. This paper aims to determine delay factors in the construction of these projects. Among many, 39 most important candidates of delay causes were identified from the literature review. Further surveys on project participants were conducted for the severity of these causes. An exploratory factor analysis was utilized to identify latent factors that cause delays in construction projects. The analysis result categorized a few groups of factors such as abnormal factors on the construction site (e.g., labor accidents, hydrology, water flow, extreme weather) and technical factors related to the construction contractor (e.g., unsuitable schedule, outdated construction technology, unprofessional workers) that have the greatest impact on the delay in construction of irrigation and hydropower projects in Vietnam. These findings contribute to the body of knowledge of project management and risk management, hence an improvement in the efficiency of the project sectors’ performance.

Labor productivity in building construction has long been a focused research topic due to the high contribution of labor cost in the building total costs. This study, among a few studies that used scaled data that were collected directly from measuring equipment and onsite activities, utilized neural networks to model the productivity of two main construction tasks and influencing factors. The neural networks show their ability to predict the behaviors of labor productivity of the formwork and rebar tasks in a test case of a high-rise building. A multilayer perceptron that had two layers and used sigmoid as its activation function provided the best effectiveness in predicting the relations among data. Among eleven independent factors, weather (e.g., temperature, precipitation, sun) generally played the most important role while crew factors were distributed in the mid of the ranking and the site factor (working floor height) played a mild role. This study confirms the robustness of neural networks in productivity research problems and the importance of working environments to labor productivity in building construction. Managerial implications, including careful environmental factors and crew structure deliberation, evolved from the study when labor productivity improvement is considered.