Predicting Rock Failure in Wet Environments Using Nonlinear Energy Signal Fusion for Sustainable Infrastructure Design

Tong Wang, Bin Zhi, Xiaoxu Tian, Yun Cheng (), Changwei Li and Zhanping Song ()
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Tong Wang: College of Pipeline Engineering, Xi’an Shiyou University, Xi’an 710065, China
Bin Zhi: China Road and Bridge Corporation, Beijing 100011, China
Xiaoxu Tian: School of Civil Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
Yun Cheng: School of Civil Engineering, Yancheng Institute of Technology, Yancheng 224051, China
Changwei Li: China Road and Bridge Corporation, Beijing 100011, China
Zhanping Song: School of Civil Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China

Sustainability, 2025, vol. 17, issue 16, 1-22

Abstract: Moisture-induced instability in rock masses presents a significant threat to the safety and sustainability of underground infrastructure. This study proposes a nonlinear energy signal fusion framework to predict failure in moisture-affected limestone by integrating acoustic emission data with energy dissipation metrics. Uniaxial compression tests were carried out under controlled moisture conditions, with real-time monitoring of AE signals and strain energy evolution. The results reveal that increasing moisture content reduces the compressive strength and elastic modulus, prolongs the compaction phase, and induces a transition in failure mode from brittle shear to ductile tensile–shear behavior. An energy partitioning analysis shows a clear shift from storage-dominated to dissipation-dominated failure. A dissipation factor ( η ) is introduced to characterize the failure process, with critical thresholds η min and η f identified. A nonlinear AE-energy coupling model incorporating water-sensitive parameters is proposed. Furthermore, an energy-based instability criterion integrating multiple indicators is established to quantify failure transitions. The proposed method offers a robust tool for intelligent monitoring and predictive stability assessment. By integrating data-driven indicators with environmental sensitivity, the study provides engineering insights that support adaptive support design, long-term resilience, and sustainable decision making in groundwater-rich rock environments.

Keywords: moisture-induced rock instability; energy dissipation; acoustic emission monitoring; instability prediction; sustainable underground engineering (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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