古建筑砌体结构裂缝损伤监测和数据挖掘  

CRACK DAMAGE MONITORING AND DATA MINING OF ANCIENT BUILDING MASONRY STRUCTURES

作　　者：杨娜[1,2] 王烁 迪力达尔·迪力夏提[1,2] YANG Na;WANG Shuo;DILIDAER Diixiati(School of Civil Engineering,Beijing Jiaotong University,Beijing 100044,China;Beijing’s Key Laboratory of Structural Wind Engineering and Urban Wind Environment,Beijing Jiaotong University,Beijing 100044,China)

机构地区：[1]北京交通大学土木建筑工程学院,北京100044 [2]北京交通大学结构风工程与城市风环境北京市重点实验室,北京100044

出　　处：《工程力学》2025年第3期18-31,共14页Engineering Mechanics

基　　金：中央高校基本科研业务费专项资金项目(2021JBZ110);国家自然科学基金面上项目“基于计算机视觉的多尺度古建筑结构变形监测技术与评估方法研究”(52478119);国家自然科学基金面上项目“累积残损变形对古建筑木结构力学性能的影响”(51878034)。

摘　　要：通过接触式和非接触式的裂缝监测技术获取古建筑砌体结构健康监测数据,深入挖掘监测数据的有效信息,解析裂缝特征对古建筑结构监测具有重大意义。该文针对古建筑砌体结构健康监测数据提出了异常识别方法,研究了预测模型,并基于数理方法判断实时监测数据的异常概率;以砌体结构裂缝开合监测数据与结构内部场环境温湿度监测数据为研究对象,探究砌体结构裂缝开合监测数据的周期性特征及其影响因素。考虑到壁画裂缝在成像时相较于单一背景裂缝具有显著的干扰特性,研究了适用于藏式古建壁画裂缝的监测算法,改进U-Net形成了对环境干扰具备鲁棒性的壁画裂缝分割模型。针对大尺寸壁画裂缝,进一步研发了基于组件树SSR的图像分割算法,随后针对监测系统在图像采集过程中的环境影响因素进行了分析与试验,并通过模拟裂缝发展验证了监测系统在裂缝静止和扩张状态下应用的可行性。Obtaining health monitoring data for ancient masonry structures through both contact and non-contact crack monitoring techniques,and deeply extracting valuable information from these data to analyze crack characteristics,holds significant importance for the structural health monitoring of ancient buildings.This paper proposes an anomaly detection method for health monitoring data of ancient masonry structures,investigates predictive models,and assesses the anomaly probabilities of real-time monitoring data based on mathematical methods.The periodic characteristics and influencing factors of the monitoring data of masonry structure crack opening and closing were explored by taking the monitoring data of masonry structure crack opening and closing and the monitoring data of field environment temperature and humidity inside the structure as the research objects.Considering that mural crack imaging features have strong interference characteristics compared with a single background crack,a monitoring technology for the growth and deformation of mural wall cracks in ancient Tibetan buildings was studied.Based on U-Net semantic segmentation model,an intelligent mural crack segmentation detection model was constructed which is robust to environmental interference.For large-size mural cracks,the image segmentation algorithm based on component tree SSR was further developed,and then the environmental factors affecting the monitoring system in the process of image acquisition were analyzed and tested.The performance of the monitoring system in practical application was tested and analyzed.The feasibility of the application of the monitoring system in the static and expanding state of the fracture is verified by simulating the fracture development.

关 键 词：古建筑砌体结构 壁画 裂缝 结构健康监测 数据挖掘 计算机视觉 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]