Intelligent Recognition of Safety Risk in Metro Engineering Construction Based on BP Neural Network

Mengchu Li,Jingchun Wang,Ming Bao Cheng

doi:10.1155/2021/5587027

Mengchu Li, Jingchun Wang + Show 1 more

Open Access

https://doi.org/10.1155/2021/5587027

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Abstract

With the rapid development of urban economy, the development of urban rail transit is becoming more and more rapid. As an energy-saving, land-saving, and environment-friendly green travel mode, the subway provides realistic and feasible solutions to the increasingly prominent traffic environment and other urban diseases in our country and brings a booming development in the subway construction industry with efforts to promote and build in many large cities. For a large number of subway constructions, it is particularly important to judge the construction safety status in time during the entire safety management process. Regularly conducting safety risk assessments on subway construction status can accurately predict and judge the types of accidents that occur. In order to solve the current safety risk assessment problems in the process of subway construction in our country, this paper is based on the BP neural network to intelligently identify the safety risks of subway construction, choosing from three aspects: human factors, management factors, and risk factors. We evaluate the construction safety of subway projects under construction through the model, predict the types of accidents that may occur, so that the construction unit can take corresponding preventive and improvement measures, improve the relevant safety technology of subway construction in a targeted manner, and propose corresponding reductions. We provide suggestions and measures for risk probability, to ensure that the construction unit discovers the danger in time and takes safety measures. The rectification measures provided theoretical basis and guidance.

Highlights

IntroductionDue to the rapid development of subway projects, the large scale of construction, and the lack of sufficient technology and management capabilities, the safe construction of subway projects has potential high safety hazards [3]
Based on the system theory, Zeng et al used dynamic control principles to study the theoretical framework of the subway project risk assessment system from the perspective of dynamic system risk assessment [17,18,19]. ere are some shortcomings in the experimental research of the abovementioned scholars, so this paper studies the intelligent identification of safety risks in subway construction based on the BP neural network
On the basis of summarizing the existing research results and combining the construction characteristics of subway projects, this study constructed a set of index systems consistent with the risk analysis of subway construction progress and introduced the improved BP neural network algorithm into the risk preassessment

Summary

Introduction

Due to the rapid development of subway projects, the large scale of construction, and the lack of sufficient technology and management capabilities, the safe construction of subway projects has potential high safety hazards [3]. It has devastating effects on victims, property damage, and social environment. Erefore, it is very urgent to determine the dangerous factors in subway construction and formulate appropriate countermeasures to reduce or even eliminate possible safety accidents. It has devastating effects on victims, property damage, and social environment. erefore, it is very urgent to determine the dangerous factors in subway construction and formulate appropriate countermeasures to reduce or even eliminate possible safety accidents. e purpose of this article is to establish a safety assessment prediction model, through which the safety of underground projects under construction can be evaluated and the types of possible accidents can be predicted, so that the construction unit can take appropriate preventive and remedial measures [4]

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Conclusion