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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.
Chen et al. (Wed,) studied this question.