Multiparameter Inversion Early Warning System of Tunnel Stress‐Seepage Coupling Based on IA‐BP Algorithm

Abstract

Under construction disturbance, the surrounding rock of a soft rock tunnel shows obvious aging characteristics. The creep characteristics of a rock mass under stress-seepage coupling greatly influence the long-term stability of a project. How to simply, quickly, and accurately determine the creep parameters of a rock mass under coupling conditions is significant to engineering structure design and construction. The optimal weights and thresholds of the BP neural network are sought through the immune algorithm to avoid the problem of slow convergence speed of the BP neural network and easy to fall into local optimum. Therefore, an intelligent back analysis method based on the IA-BP algorithm is established, which leads to the development of the corresponding intelligent back analysis program. The creep effect of the rock mass was simulated herein using the Drucker–Prager yield criterion and the time hardening creep law as the forward optimization method constitutive model. In addition, a sensitivity analysis of the parameters was performed to determine the optimal number of inversion parameters. By comparing and analyzing the residual between the inversion results of the IA-BP algorithm, PSO-BP algorithm, and the test values, the high precision of the IA-BP algorithm is proved. Taking the Lan Zhou-Hai Kou national expressway tunnel as an engineering example, a multiparameter creep inversion of the tunnel surrounding rock under the stress-seepage coupling condition was conducted using the inverse analysis method of the IA-BP algorithm. The results showed that the proposed IA-BP algorithm can effectively prevent the BP neural network from falling into a local minimum. Also, the algorithm is fast and accurate. The intelligent back analysis method based on the IA-BP algorithm is applied to the multifield coupling parameter back analysis, provides the basis and help for the structural design and construction of soft rock tunnel in water-rich stratum.

Inversion Analysis Method for Tunnel and Underground Space Engineering: A Short Review

“…Tables 5 and 6 display the application cases of the traditional inversion analysis method and classical intelligent inversion analysis algorithm in engineering. It can be seen that the improvement measures of various optimization algorithms, such as improved particle swarm optimization algorithm [87], genetic simulated annealing differential evolution algorithm [88], and immune algorithm [89], can further enhance the optimization ability and inversion quality by integrating the advantages of each algorithm and eliminating their disadvantages. These algorithms are widely used in the calculation of various tunnel characteristic parameters.…”

Back analysis of rock mechanical parameters of underground caverns based on multi-layer perceptron regression algorithm

“…The findings of the study provided compelling evidence to support the significantly higher precision achieved by the IA-BP algorithm. [8] In the subsequent sections of this paper, we will discuss the methodology employed for data collection, pre-processing, and model development. We will present the results of the deep learning-based back analysis and compare them with traditional methods.…”

Inversion of the Permeability Coefficient of a High Core Wall Dam Based on a BP Neural Network and the Marine Predator Algorithm

“…Its learning principle is using the steepest descent method to adjust thresholds and weights to make the network's sum of squares of errors the minimum. It has been proven that BP networks have great accuracy in predicting parameters on the basis of observed value in various engineering research areas [33][34][35]. The marine predator algorithm is a nature-inspired metaheuristic, applying Lévy and Brownian movements in design, and is confirmed to be a high-performance optimizer [36].…”