Many human errors occur in hydraulic engineering construction, and these errors may lead to huge financial losses. A systematic and comprehensive accident analysis is required to reduce the probability of human error. Human error analysis is a lengthy and challenging process because the tendency is for accident data to be presented in text format. In addition, construction human error management requires an intelligent and efficient analysis system to ensure the timeliness of accident prevention and control. Thus, this study proposes a human error intelligent analysis system on the basis of text mining to automatically extract text knowledge and reveal the accident evolution process. Using hydraulic engineering construction text, a topic feature extraction model is built to extract words and improve the human factors analysis and classification system (HFACS) model. Then, a human error causation network that integrates text topic features, the improved HFACS model, and Bayesian theory is developed to intelligently identify human factors and quantify the human error evolution process. The analysis system proposed in this paper provides an effective way to mine and apply the experience-based knowledge available in hydraulic engineering construction text for the intelligent analysis and prediction of human error, thus improving the efficiency of human error management.

The construction industry continues to be one of the industries with the highest accident rate. Tower cranes have always been the central component of many construction operations. With recent advancements in tower cranes, more efforts have been made for their safe operation. The impact of natural environmental factors on the operation of tower cranes cannot be ignored, especially the impact of the wind load, which can be found in construction regulations worldwide. Therefore, a large number of recent studies have focused on exploring different approaches to improving the impact of loads, such as improved feedback controllers and collocated sensors. However, there are few studies on the impact of the flexible cable pendulum system under wind load. To quantify the impact in this paper, first, the swing model of the hanging and the model of sling fluctuation are proposed as the base. Second, the flexible cable pendulum model is designed for the impact of tower cranes under wind load. By field experiments, the applicability of the model in practical use is analyzed. A case of the Crane Beach Hydropower Station is investigated as a demonstration. With MATLAB programming, the variation in the hanging impulse with wind speed is shown, indicating that the maximum wind speed should be 12 m/s if the factors that people can control are considered. When considering the factors that can be harmful to humans, the maximum wind speed is 17 m/s. With the proposed model, the impacts of wind load on tower crane operation can be quantified without actual deployment, offering a more straightforward quantitative tool for safe regulations and engineering management.