隧道衬砌结构开裂是隧道养护工作中需重点防治的病害,隧道智慧管养的发展急需可推广应用的隧道衬砌结构裂缝病害定量评价方法。本文引入裂缝图像自动识别技术,融合乘积标度法和规范要求分别建立了单裂缝病害和衬砌结构区间段多裂缝病害量化评价指标体系及指标阈值确定方法,其中单裂缝病害诊断方法以衬砌裂缝的长度、宽度作为主要评价指标,深度、方向和发展性作为辅助评价指标；多裂缝病害诊断方法以衬砌裂缝的长度、宽度作为主要评价指标,深度、方向、发展性和分布密度作为辅助评价指标；指标阈值确定既考虑了均匀分布函数概率分布特性又结合了隧道结构受力安全性特点,并对评价方法的分值进行了系统的无量纲标准化处理,编制了可推广应用的软件,案例分析显示本方法基本科学合理,为实现隧道开裂病害智能识别和养护方案自动决策提供了新途径。

The cracking of tunnel lining structure is a key disease that needs to be prevented and controlled in tunnel maintenance work. The development of intelligent tunnel management urgently requires a quantitative evaluation method for the cracking disease of tunnel lining structure that can be promoted and applied. In this paper, the automatic recognition technology of crack image is introduced, and the quantitative evaluation index system and threshold determination method of single crack disease and m ulti crack disease of lining structure section are established respectively by fusing the product scale method and the specification requirements. The length and width of lining crack are taken as the main evaluation indexes for the diagnosis method of single crack disease, and the depth, direction and development of lining crack are taken as the auxiliary evaluation indexes; The diagnosis method for multi crack diseases uses the length and width of lining cracks as the main evaluation indicators, and depth, direction, development, and distribution density as auxiliary evaluation indicators; The determination of indicator threshold not only considers the probability distribution characteristics of uniform distribution function but also combines the safety characteristics of tunnel structure under stress. The scores of the evaluation method have been systematically standardized without dimensionality, and software that can be promoted and applied has been developed. Case analysis shows that this method is scientifically reasonable, providing a new way to achieve intelligent identification of tunnel cracking diseases and automatic decision-making of maintenance plans.

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国家自然科学基金(52278400、41830110)；江苏省交通运输厅科技计划项目(2022Y04)；中交养护集团2020年重大科技研发项目(27100020Y251、27100020Y248)