{"title":"Multi-domain transfer generation of cavity defect data in asphalt pavements using 3D GPR and 3D forward modeling","authors":"Peng Wang , Lei Zhang , Yiqiu Tan , Zhen Leng","doi":"10.1016/j.autcon.2025.106345","DOIUrl":null,"url":null,"abstract":"<div><div>The paucity of GPR data pertaining to cavity defects significantly impedes the advancement of intelligent nondestructive testing methods in pavement engineering. This paper illustrates that heterogeneous forward models of cavity defects, constructed using pseudo-random generation algorithms, exhibit remarkable accuracy in mimicking the electromagnetic responses within asphalt pavement structures. A unified multi-domain transfer learning framework, employing StarGAN, facilitates the cross-domain generation of data representing cavity defects in asphalt pavements. The model effectively suppresses clutter interference, thereby preserving cavity defect characteristics in heterogeneous forward images, while adeptly synthesizing signals conforming to heterogeneous structural properties in homogeneous forward images. Quantitative assessments reveal an exceptionally high degree of similarity between the synthetically generated data and actual samples (LPIPS≈0). The measured cavity defect features generated by StarGAN exhibit high physical regularity and morphological diversity compared to real samples (LPIPS<0.1). This paper introduces a novel approach to data augmentation for GPR applications in asphalt roads.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"177 ","pages":"Article 106345"},"PeriodicalIF":9.6000,"publicationDate":"2025-06-16","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/S0926580525003851","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
The paucity of GPR data pertaining to cavity defects significantly impedes the advancement of intelligent nondestructive testing methods in pavement engineering. This paper illustrates that heterogeneous forward models of cavity defects, constructed using pseudo-random generation algorithms, exhibit remarkable accuracy in mimicking the electromagnetic responses within asphalt pavement structures. A unified multi-domain transfer learning framework, employing StarGAN, facilitates the cross-domain generation of data representing cavity defects in asphalt pavements. The model effectively suppresses clutter interference, thereby preserving cavity defect characteristics in heterogeneous forward images, while adeptly synthesizing signals conforming to heterogeneous structural properties in homogeneous forward images. Quantitative assessments reveal an exceptionally high degree of similarity between the synthetically generated data and actual samples (LPIPS≈0). The measured cavity defect features generated by StarGAN exhibit high physical regularity and morphological diversity compared to real samples (LPIPS<0.1). This paper introduces a novel approach to data augmentation for GPR applications in asphalt roads.
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.