基于AI驱动的边坡地质灾害智能识别与巡检养护研究进展  

Research progress on AI driven intelligent recognition,inspection and maintenance of slope geological hazards

作　　者：胡盛明[1,2] 汪舟 卢永飞 马贵平 毛志斌 王清华 HU Shengming;WANG Zhou;LU Yongfei;MA Guiping;MAO Zhibin;WANG Qinghua(National and Provincial Joint Engineering Laboratory for Hydraulic Engineering Safety and Efficient Utilization of Water Resources of Poyang Lake Basin,Nanchang Institute of Technology,Nanchang 330099,China;Jiangxi Academy of Water Science and Engineering,Nanchang 330029,China)

机构地区：[1]南昌工程学院鄱阳湖流域水工程安全与资源高效利用国家地方联合工程实验室,江西南昌330099 [2]江西省水利科学院,江西南昌330029

出　　处：《南昌工程学院学报》2025年第1期37-47,共11页Journal of Nanchang Institute of Technology

基　　金：国家自然科学基金资助项目(42162023,42162025);江西省教育厅科学技术研究项目(GJJ201904);江西省水利科技项目(202124ZDKT15,202223YBKT03);江西省科技厅重点研发计划项目(20203BBGL73220);江西省高等学校教学改革研究省级课题(JXJG-23-18-23)。

摘　　要：为有效应对边坡地质灾害带来的严峻挑战,如何借助先进技术提升边坡地质灾害的监测与防治水平成为关键。回顾了人工智能(AI)技术在边坡地质灾害智能识别与巡检养护领域的最新研究进展;系统梳理了传统人工巡检方法的效率低下和安全隐患问题。综述了新型监测技术,如无人机、遥感和深度学习技术在提高边坡监测准确性和效率方面的发展;展望了未来研究方向,主要包括技术融合、自动化、实时监测与预警能力提升,以及数据驱动的决策支持等。同时,分析探讨了技术进步过程中面临的挑战,包括数据质量、算法过拟合和计算资源等问题,并强调建立边坡地质灾害数据库和分类标准的重要性,以实现快速识别和应对边坡地质灾害,降低社会经济损失和人员伤亡风险。研究结果可为构建更加完善和高效的地质灾害防控体系提供有益参考。Slope geohazards have become a severe threat to human society due to their considerable destructive capacity,necessitating the development of effective monitoring and prevention strategies,utilizing advanced technological solutions.This paper reviews the latest research progress in the field of intelligent recognition and inspection and maintenance of slope geohazards with artificial intelligence(AI)technology,systematically examines the inefficiency and safety hazards of traditional manual inspection methods,and reviews the development of new monitoring technologies.These include the use of drones,remote sensing,and deep learning technologies to enhance the accuracy and efficiency of slope monitoring.This paper also summarizes future research directions,including technological convergence,automation,improvement of real-time monitoring and early warning capabilities,and data-driven decision support.The analysis further discusses the challenges encountered during the technological advancement,including data quality,algorithm overfitting and computational resources,and highlights the importance of establishing a slope geohazard database and classification standards to achieve rapid identification of and response to slope geohazards and to reduce the risk of economic losses and human casualties.The research in this paper provides a reference for building a more complete and efficient geological hazard prevention and control system.

关 键 词：AI驱动 公路边坡 地质灾害 智能识别 巡检养护 

分 类 号：TU455[建筑科学—岩土工程]