Study on Human Factor Reliability under the Dynamic Evolution of Intelligent Lifting Construction Accidents

This research explores human factors involved in intelligent lifting construction operations risks to prevent errors and manage processes affecting risk evolution. The study employs the human factors analysis and classification system with text mining to identify risk-inducing factors from accident reports. An initial hierarchy is created from the data using the ISM technique, which is further developed into a Bayesian network diagram based on Fuzzy Dynamic Bayesian Networks. Using the approach of the fuzzy set and the developed similarity aggregation method, probabilities before or under the conditions of the network nodes are also estimated. The model’s performance is carefully verified through forward reasoning based on specific indicators, through an analysis which traces the evolution of the various paths through time, and again through sensitivity analysis. They used the FDBN-based human factor reliability risk model; the results show that the constructed risk prediction and control mechanisms help determine the significant evolutionary risk paths, optimising the management strategies for intelligent lifting construction. It presents new findings regarding outcomes of human reliability engineering applied to intelligent construction systems and presents a set of practical recommendations on safety and operations improvement.