Intelligent analysis method for the global vertical displacement field of foundation pits in dense karst cave areas postprint

Abstract: Prior research on excavation in dense karst cave foundation pits has primarily concentrated on evaluating the localized spatio-temporal influence and isolated geological factors. Nonetheless, this approach oversimplifies modeling conditions, thereby limiting its ability to provide a comprehensive understanding of the vertical displacement field. Consequently, this oversimplification can inflate the safety factor and increase project costs. Therefore, we propose a feedforward neural network (FNN), updated with the loop nested optimal iterative method (LNOIM), which incorporates the spatiotemporal characteristics of monitoring points and geological factors to analyze the engineering sensitivity of karst caves. Ultimately, the global foundation pit vertical displacement field was obtained. Our method has been demonstrated to be effective in a foundation pit in South China ((P value) P > 0.050, Cohen’s d < 0.200). Furthermore, it has been validated in other cases ((Root Mean Square Error) RMSE = 1.576–2.916). This work provides a new perspective on the accurate reflection of the global vertical displacement state of a foundation pit. Additionally, it enhances the ability to sensitively identify caves in dense karst cave areas, thereby improving the safety of foundation pit works.