An overview of ahead geological detection technologies in tunnels

The rapid advancement of ahead geological detection technologies has been crucial to addressing the challenges posed by complex underground environments in tunnel construction. As urbanization accelerates and infrastructure projects expand, effective geological detection becomes increasingly essenti...

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Main Authors: Dingchao Chen, Xiangyu Wang, Jianbiao Bai, Yuan Chu, Xian Wang, Jiaxin Zhao, Menglong Li
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:European Journal of Remote Sensing
Subjects:
Online Access:https://www.tandfonline.com/doi/10.1080/22797254.2025.2503240
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author Dingchao Chen
Xiangyu Wang
Jianbiao Bai
Yuan Chu
Xian Wang
Jiaxin Zhao
Menglong Li
author_facet Dingchao Chen
Xiangyu Wang
Jianbiao Bai
Yuan Chu
Xian Wang
Jiaxin Zhao
Menglong Li
author_sort Dingchao Chen
collection DOAJ
description The rapid advancement of ahead geological detection technologies has been crucial to addressing the challenges posed by complex underground environments in tunnel construction. As urbanization accelerates and infrastructure projects expand, effective geological detection becomes increasingly essential for ensuring safety and operational efficiency. This paper examines the evolution of geological detection methods in tunnel construction, and categorizing them into traditional ahead geological detection methods and ahead detection while excavation (DWE) methods. It highlights the limitations of traditional methods, such as seismic, electrical, and electromagnetic techniques, which require excavation pauses, and discusses the integration of real-time detection systems, particularly those used during excavation, such as tunnel seismic while drilling (TSWD) and tunnel electrical while drilling (TEWD). These ahead geological methods enable continuous, non-invasive monitoring of subsurface conditions, providing timely data to mitigate risks such as water inrush and unstable rock formations. Furthermore, this paper explores the emerging role of intelligent technologies, including artificial intelligence (AI) and machine learning (ML), in enhancing real-time data analysis, predictive modeling, and decision-making processes in tunnel geological detection. This paper underscores the need for multi-source, cooperative detection approaches and transparent geological models to improve the safety and efficiency of tunneling operations, ultimately contributing to the optimization of tunnel design and construction strategies.
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spelling doaj-art-3f8ed2ba2a8d4058ac3daaa807fc62fa2025-08-20T01:50:00ZengTaylor & Francis GroupEuropean Journal of Remote Sensing2279-72542025-12-0158110.1080/22797254.2025.2503240An overview of ahead geological detection technologies in tunnelsDingchao Chen0Xiangyu Wang1Jianbiao Bai2Yuan Chu3Xian Wang4Jiaxin Zhao5Menglong Li6School of Mines, China University of Mining and Technology, Xuzhou, ChinaSchool of Mines, China University of Mining and Technology, Xuzhou, ChinaSchool of Mines, China University of Mining and Technology, Xuzhou, ChinaSchool of Mines, China University of Mining and Technology, Xuzhou, ChinaSchool of Mines, China University of Mining and Technology, Xuzhou, ChinaSchool of Mines, China University of Mining and Technology, Xuzhou, ChinaSchool of Mines, China University of Mining and Technology, Xuzhou, ChinaThe rapid advancement of ahead geological detection technologies has been crucial to addressing the challenges posed by complex underground environments in tunnel construction. As urbanization accelerates and infrastructure projects expand, effective geological detection becomes increasingly essential for ensuring safety and operational efficiency. This paper examines the evolution of geological detection methods in tunnel construction, and categorizing them into traditional ahead geological detection methods and ahead detection while excavation (DWE) methods. It highlights the limitations of traditional methods, such as seismic, electrical, and electromagnetic techniques, which require excavation pauses, and discusses the integration of real-time detection systems, particularly those used during excavation, such as tunnel seismic while drilling (TSWD) and tunnel electrical while drilling (TEWD). These ahead geological methods enable continuous, non-invasive monitoring of subsurface conditions, providing timely data to mitigate risks such as water inrush and unstable rock formations. Furthermore, this paper explores the emerging role of intelligent technologies, including artificial intelligence (AI) and machine learning (ML), in enhancing real-time data analysis, predictive modeling, and decision-making processes in tunnel geological detection. This paper underscores the need for multi-source, cooperative detection approaches and transparent geological models to improve the safety and efficiency of tunneling operations, ultimately contributing to the optimization of tunnel design and construction strategies.https://www.tandfonline.com/doi/10.1080/22797254.2025.2503240Tunnelahead geological detectiontunnel seismic while drillingtechnical principlesdevelopment process
spellingShingle Dingchao Chen
Xiangyu Wang
Jianbiao Bai
Yuan Chu
Xian Wang
Jiaxin Zhao
Menglong Li
An overview of ahead geological detection technologies in tunnels
European Journal of Remote Sensing
Tunnel
ahead geological detection
tunnel seismic while drilling
technical principles
development process
title An overview of ahead geological detection technologies in tunnels
title_full An overview of ahead geological detection technologies in tunnels
title_fullStr An overview of ahead geological detection technologies in tunnels
title_full_unstemmed An overview of ahead geological detection technologies in tunnels
title_short An overview of ahead geological detection technologies in tunnels
title_sort overview of ahead geological detection technologies in tunnels
topic Tunnel
ahead geological detection
tunnel seismic while drilling
technical principles
development process
url https://www.tandfonline.com/doi/10.1080/22797254.2025.2503240
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