Year: 2025
Type: article
Abstract: this issue highlights the innovative applications of ML technique in reshaping structural safety, tunneling operations, and geotechnical investigations.As underground engineering challenges grow in complexity, ML and Big Data have become indispensable tools for improving prediction accuracy, optimizing operational efficiency, and ensuring the long-term safety and sustainability of infrastructure.By leveraging vast datasets, automating critical processes, and predicting complex engineering outcomes, these technologies are enabling smarter, more reliable engineering practices that drive both performance and resilience.The contributions to this special issue illustrate the diverse and impactful applications of ML and Big Data in deep underground engineering.One article introduces ALSTNet, an advanced data-driven model that integrates long-and short-term time-series data using autoencoders to predict tunnel structural behaviors.When applied to strain monitoring data from the Nanjing Dinghuaimen tunnel, ALSTNet outperforms traditional models, offering promising potential for early disaster prevention in real-world engineering scenarios.Another study presents two robust ML models-Gene Expression Programming (GEP) and a Decision Tree-Support Vector Machine (DT-SVM) hybrid algorithm-to assess pillar stability in deep underground mines.Validated with 236 case histories, these models demonstrate exceptional accuracy and provide valuable tools for project managers to evaluate pillar stability during both the design and operational phases of mining projects.Yet another study demonstrates the use of fuzzy C-means clustering combined with ML models in Tunnel Boring Machine (TBM) operations.This innovative approach enhances prediction accuracy, providing more reliable insights for TBM tunneling processes and boosting efficiency in underground excavation projects.
DOI: 10.1002/dug2.70004
Authors: Asoke K. Nandi, Ru Zhang, Tao Zhao, Tao Lei
Language: English
Topic: Tunneling and Rock Mechanics
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4408334273