Safety Risk Assessment Using a BP Neural Network of High Cutting Slope Construction in High-Speed Railway

High-speed railway construction is extending to mountainous areas, and the harsh environment complex climate pose various risks slope construction. This seriously threatens human lives causes huge economic losses. The existing research results on safety of high cutting in HSRs are limited, a complete set risk assessment processes methods has not yet been formed. Therefore, this study, we aimed develop model, including factor identification classification data processing, help project managers evaluate In comprehensive was carried out from three dimensions, technical specification, literature analysis, case statistical list risk-influencing factors Based historical data, side evaluation model established using BP neural network algorithm. applied HF slopes. show that level II; main earthwork excavation method, scaffolding equipment, height, rate, groundwater, personnel awareness, management system. Finally, study used verify proposed control measures for were proposed. Our findings will conduct effective management, add knowledge terms implementation, offer lessons references future HSR.