{"title":"Graph-based change detection of pavement cracks","authors":"Yibo Zhou , Yuchun Huang , Qi Chen , Dongchen Yang","doi":"10.1016/j.autcon.2025.106110","DOIUrl":null,"url":null,"abstract":"<div><div>Pavement crack deterioration threatens road safety, but current maintenance strategies rely on composite indicators that lack crack location and attribute changes, failing to accurately track deterioration. Traditional feature-point-based methods struggle with temporal crack correspondence due to noise and shape variability. However, local structural features like intersections and inflection points are significant and extractable, providing a reliable basis for crack correspondence. This paper proposes an unsupervised graph-based framework for crack change detection. First, a curvature-first distance-optimized algorithm extracts stable keypoints to construct crack graphs, representing structural information. Second, a graph matching strategy combines Bezier curve similarity and Monte Carlo Tree Search to resolve structural correspondences, enabling accurate change detection. To address data scarcity, a Voronoi-based simulator models crack propagation through controlled stress fields. Experiments on synthetic and real-world datasets achieved crack change detection accuracies of 97.95% and 90.18%, respectively, demonstrating high accuracy without relying on learning-based components.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106110"},"PeriodicalIF":9.6000,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Automation in Construction","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0926580525001505","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Pavement crack deterioration threatens road safety, but current maintenance strategies rely on composite indicators that lack crack location and attribute changes, failing to accurately track deterioration. Traditional feature-point-based methods struggle with temporal crack correspondence due to noise and shape variability. However, local structural features like intersections and inflection points are significant and extractable, providing a reliable basis for crack correspondence. This paper proposes an unsupervised graph-based framework for crack change detection. First, a curvature-first distance-optimized algorithm extracts stable keypoints to construct crack graphs, representing structural information. Second, a graph matching strategy combines Bezier curve similarity and Monte Carlo Tree Search to resolve structural correspondences, enabling accurate change detection. To address data scarcity, a Voronoi-based simulator models crack propagation through controlled stress fields. Experiments on synthetic and real-world datasets achieved crack change detection accuracies of 97.95% and 90.18%, respectively, demonstrating high accuracy without relying on learning-based components.
期刊介绍:
Automation in Construction is an international journal that focuses on publishing original research papers related to the use of Information Technologies in various aspects of the construction industry. The journal covers topics such as design, engineering, construction technologies, and the maintenance and management of constructed facilities.
The scope of Automation in Construction is extensive and covers all stages of the construction life cycle. This includes initial planning and design, construction of the facility, operation and maintenance, as well as the eventual dismantling and recycling of buildings and engineering structures.