Shield Tunneling Parameter Matching Model and UI Interface

Gongyu Hou,Xiaoge Quan,Zhedong Xu,Le Li,Yajie Yang

doi:10.1155/2020/9562828

Gongyu Hou, Xiaoge Quan + Show 3 more

Open Access

https://doi.org/10.1155/2020/9562828

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Abstract

In order to improve the accuracy of shield tunneling parameter matching under the limited data, the matching model based on support vector machine (SVM) and exponential adjustment inertia weight immune particle swarm optimization (EAIW-IPSO) is proposed. The nonlinear relationship model between the tunneling parameters and the ground settlement is constructed by SVM and trained with the actual engineering sample data. Based on the trained model, EAIW-IPSO is used to optimize the tunneling parameters. At the same time, UI interface was developed based on the tunneling parameter matching model. The matching model based on BP neural network and PSO algorithm is compared in simulation experiments and engineering case. It is verified that the matching model based on SVM and EAIW-IPSO still maintains great accuracy and stability as the number of samples continues to decrease. The paper provides a better solution for the matching of tunneling parameters in actual engineering.

Highlights

E abovementioned solutions are adopted to deal with the nonlinear single-objective programming problems which are difficult to establish the objective function accurately
To solve the shield tunneling parameter matching with the limited sample data, this paper proposes a model based on support vector machine (SVM) and EAIW-IPSO. e SVM is used to construct the nonlinear relationship model between the tunneling parameters and the ground settlement
The corresponding UI interface is developed. e study provides a better solution for shield tunneling parameter matching under the limited sample data in actual engineering and realizes the interface operation to achieve the tunneling parameter matching conveniently

Summary

Basic Theory

In order to improve the performance of the PSO algorithm, Hou et al [22] improved the inertia weight adjustment equation and the population variation mechanism, proposing the EAIW-IPSO algorithm. E population variation strategy in the EAIW-IPSO algorithm is before the iteration and another particle population with size of M is regenerated. Among the original and new populations, M particles with larger fitness values are selected from the 2M particles as the group of the iteration. EAIW-IPSO algorithm specific process: Step 1: set the value of all parameters, including M, T, c1, c2, ωmin, and ωmax. Step 3: adjust the position and velocity values based on equations (1)–(3) and set the personal and global best position. M particles with larger fitness values are selected from the 2M particles as the group of the iteration, updating the global best position.

Shield Tunneling Parameter Matching Model and Its Performance Verification

Comparative Analysis for Two Models

Findings

Conclusions