Title: An empirical study on construction emergency disaster management and risk assessment in shield tunnel construction project with big data analysis

Authors: Liyu Lu; Meiling Ji; Xi Wen; Yong Xiang

Addresses: School of Civil Engineering, Architecture, Environment, Xihua University, Chengdu 610039, Sichuan, China ' School of Civil Engineering, Architecture, Environment, Xihua University, Chengdu 610039, Sichuan, China ' School of Civil Engineering, Architecture, Environment, Xihua University, Chengdu 610039, Sichuan, China ' School of Civil Engineering, Architecture, Environment, Xihua University, Chengdu 610039, Sichuan, China

Abstract: Emergency disaster management presents substantial risks and obstacles to shield tunnel building projects, particularly in the event of water leakage accidents. Contemporary water leak detection is critical for guaranteeing safety by reducing the likelihood of disasters and the severity of any resulting damages. However, it can be difficult. Deep learning models can analyse images taken inside the tunnel to look for signs of water damage. This study introduces a unique strategy that employs deep learning techniques, generative adversarial networks (GAN) with long short-term memory (LSTM) for water leakage detection i shield tunnel construction (WLD-STC) to conduct classification and prediction tasks on the massive image dataset. The results demonstrate that for identifying and analysing water leakage episodes during shield tunnel construction, the WLD-STC strategy using LSTM-based GAN networks outperformed other methods, particularly on huge data.

Keywords: disaster management; shield tunnel construction; STC; water leakage detection; big data; deep learning; generative adversarial networks; GAN; long short-term memory; LSTM.

DOI: 10.1504/IJDMB.2024.139465

International Journal of Data Mining and Bioinformatics, 2024 Vol.28 No.3/4, pp.406 - 425

Received: 22 Jul 2023
Accepted: 26 Oct 2023
Published online: 02 Jul 2024 *

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