Transit Route Optimization for Cable Crane Exposure Reduction Using NSGA-II Algorithm Based on Neural Networks

Abstract

Owing to frequent man-machine interaction, cable crane operation during the dam construction becomes an intensive dangerous special equipment and easily enlarge exposure. In particular, high wind weather increases the risk of falling objects at high altitude and enhance the impact space of cable crane. These factors lead to space conflicts in the limited space, and prone to accidents. However, previous studies mainly focused on safety monitoring through various technologies, but lack of sufficient prior control, and current methods for searching the optimal transit route of cable crane are insecure and insufficient since the wind environment, complex construction process, and dense man-machine interaction are not simultaneously considered. Thus, in this article, a transit route optimization method for cable crane exposure considering dynamic wind loads during the dam construction is proposed to reduce hazard exposure risk. A comprehensive evaluation function is constructed to assess the cable crane transit route with taking the hazard exposure, efficiency, and operability and collision risk into consideration. The objective is to make use of the NSGA-II algorithm to optimize the transit route under different working situations based on neural networks. The model feasibility is validated by applying it to the Dagangshan hydropower station. A set of results show that the novel methodology is able to reduce exposure and assess the comprehensive performance of different transit route. This method provides an important objective and precise evaluation tool for the cable crane optimal transit route, and also promotes safety management in the project.