高陡岸坡溶蚀型塔柱状危岩研究现状与展望

    RESEARCH STATUS AND PROSPECTS OF THE DISSOLUTION-INDUCED UNSTABLE TOWER COLUMN ROCK MASS ON THE HIGH AND STEEP BANK SLOPE

    • 摘要: 我国西南高山峡谷灰岩区广泛发育高陡岸坡溶蚀型塔柱状危岩,对水陆交通等基础设施和人民生命财产安全构成重大威胁。基于塔柱状危岩体地貌演化特征分析,揭示了该类危岩体在全寿命周期内的形成与演化规律;并从岩体损伤演化、稳定性分析和失稳预测3个方面系统探讨其失稳机理;进一步凝练了高陡岸坡溶蚀型塔柱状危岩失稳前的关键科学问题,提出了未来需重点突破的5个研究方向。研究表明:(1)溶蚀作用、干湿循环、冻融劣化和水动力侵蚀等是该类危岩体损伤积累致变形失稳的重要影响因素,据此提出根据不同的宏观和微观形貌特征,分类评价灰岩损伤程度的研究思路;(2)合理的塔柱状危岩地质力学模型是确保稳定性分析结果准确的重要前提,底部劣化区岩体是控制塔柱状危岩失稳的关键部位;(3)危岩失稳预测涉及4个方面,即失稳模式预测、变形量预测、稳定状态预测和失稳时间预测,但针对高陡岸坡溶蚀型塔柱状危岩的失稳预测研究仍显不足,融合机器学习等人工智能算法有望显著提高其预测的可靠性与智能化水平。研究成果可为高陡岸坡崩塌灾害机理研究提供重要理论参考。

       

      Abstract: Dissolution-induced unstable tower column rock masses(DIUTCRM)are widely distributed in the limestone regions of high mountains and deep valleys in southwestern China, posing substantial risks to transportation infrastructure—including waterways and highways—and threatening human safety and property. Based on an analysis of the geomorphic evolution characteristics of DIUTCRM, this study elucidates the formation and evolutionary patterns of such rock masses throughout their life cycle. The failure mechanisms are systematically examined from three perspectives: damage evolution of the rock mass, stability analysis, and instability prediction. The study further identifies key scientific questions related to DIUTCRM instability on high, steep slopes and proposes five priority research directions for future breakthroughs. The findings indicate that: (1)Dissolution, wetting-drying cycles, freeze-thaw degradation, and hydrodynamic erosion are critical factors driving damage accumulation, deformation, and eventual failure of DIUTCRM. Accordingly, a research framework is proposed for classifying and evaluating limestone damage based on distinct macro-and micro-morphological characteristics. (2)Establishing a valid geomechanical model of DIUTCRM is essential for accurate stability analysis. The stability of the rock mass within the basal deterioration zone largely governs the overall potential for collapse. (3)Instability prediction for DIUTCRM encompasses four main aspects: failure mode, deformation progression, stability state, and failure timing. However, research in this area remains inadequate. Integrating artificial intelligence algorithms, such as machine learning, is expected to significantly enhance prediction reliability and intelligence. These findings provide an important theoretical foundation for understanding collapse disaster mechanisms on high, steep slopes in karst terrain.

       

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