Feedback analytical method and feedback analytical device during tunnel construction and based on extreme learning machine

Abstract

The invention discloses a feedback analytical method and a feedback analytical device during tunnel construction and based on an extreme learning machine. The feedback analytical method comprises the following steps: the optimal input layer weight and the optimal hidden layer offset are utilized to train and learn a training sample set I and a training sample set II through the extreme learning machine so as to obtain a rock classification evolution extreme learning machine model and a rock parameter identification evolution extreme learning machine model; rock classification influence factors disclosed during the tunnel construction process are obtained; the rock classification influence factors are inputted, and a rock classification result is outputted through the rock classification evolution extreme learning machine model; the rock displacement of a tunnel is monitored and obtained; according to the rock classification result, and within different rock classification ranges, the obtained rock displacement is combined, and the rock mechanical parameter is obtained by utilizing the differential evolution algorithm and the rock parameter identification evolution extreme learning machine model. According to the invention, the rock classification result and the rock mechanical parameter can be rapidly obtained, the predication is accurate, and the accuracy is high.

Description

technical field [0001] The invention relates to a feedback analysis method and device in tunnel construction, in particular to a feedback analysis method and device in tunnel construction based on an extreme learning machine. Background technique [0002] The tunnel is a concealed project built underground. The uncertainty and complexity of the geological body make the design and construction of the tunnel blind. It is of great significance to carry out feedback analysis on rocks. [0003] Feedback analysis in tunnel construction is a necessary process for the transformation of geological bodies from black box to gray box, and a prerequisite for dynamic adjustment of tunnel support schemes. Firstly, from a qualitative point of view, the type of surrounding rock can be determined through the information feedback revealed by excavation; secondly, from a quantitative point of view, surrounding rock parameters can be further obtained through mechanical back analysis, which prov...

AI Technical Summary

Problems solved by technology

Firstly, from a qualitative point of view, the type of surrounding rock can be determined through the information feedback revealed by excavation; secondly, from a quantitative point of view, surrounding rock parameters can be further obtained through mechanical back analysis, which provides a basis for numerical simulation calculations during tunnel construction. Tunnel back analysis The process is essentially a complex optimization problem. Aiming at the limitations of traditional optimization methods, researchers have introduced genetic algorithms, neural networks, Kalman filter algorithms, differential evolution algorithms, and particle swarm algorithms into tunnel engineering back analysis in the prior art. Although many achievements have been made in the realization of feedback analysis, the overall The construction analysis of the upper tunnel still mainly relies on human experience, which has a large degree of randomness

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