Development of an optimization model for a monitoring point in tunnel stress deduction using a machine learning algorithm

Abstract

Monitoring of the mechanical behavior of underwater shield tunnels is vital for ensuring their long-term structural stability. Typically determined by empirical or semi-empirical methods, the limited number of monitoring points and coarse monitoring schemes pose huge challenges in terms of capturing the complete mechanical state of the entire structure. Therefore, with the aim of optimizing the monitoring scheme, this study introduces a spatial deduction model for the stress distribution of the overall structure using a machine learning algorithm. Initially, clustering experiments were performed on a numerical data set to determine the typical positions of structural mechanical responses. Subsequently, supervised learning methods were applied to derive the data information across the entire surface by using the data from these typical positions, which allows flexibility in the number and combinations of these points. According to the evaluation results of the model under various conditions, the optimized number of monitoring points and their locations are determined. Experimental findings suggest that an excessive number of monitoring points results in information redundancy, thus diminishing the deduction capability. The primary positions for monitoring points are determined as the spandrel and hance of the tunnel structure, with the arch crown and inch arch serving as additional positions to enhance the monitoring network. Compared with common methods, the proposed model shows significantly improved characterization abilities, establishing its reliability for optimizing the monitoring scheme.