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Lookup NU author(s): Xinrun Li, Dr Varun OjhaORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2026 The Author(s).In practical applications, AI-based concrete crack inspection still suffers from performance degradation in few-shot, unfamiliar scenarios and lacks the capability for high-precision, synchronized quantification of three-dimensional (3D) crack geometry and location without manual post-processing. To address these limitations, a systematic methodology for crack segmentation, 3D reconstruction, and automated measurement is proposed, grounded in computer vision and Simultaneous Localization and Mapping (SLAM) techniques. First, a novel prompt generation strategy and a tailored segmentation quality assessment module are developed to improve the performance of the Segment Anything Model (SAM), enabling few-shot crack segmentation with strong generalization across diverse and unseen scenarios. Second, a comprehensive concrete crack reconstruction within a 3D representation is achieved through a newly proposed Visual Inertial LiDAR (VIL) SLAM-based fusion approach. By integrating multi-frame RGB images, LiDAR point clouds, and inertial measurements, the method enables precise alignment of crack segmentation masks with 3D structural geometry, generating high-precise, dense, and semantically enriched point clouds that capture fine-grained crack details at real-world scale. Furthermore, an automated measurement module is introduced to directly quantify detailed crack geometrical and spatial information from the established 3D representation, eliminating manual post-processing and advancing beyond traditional image-based methods. Finally, extensive experiments are successfully conducted on diverse concrete structures validating the accuracy, robustness, and effectiveness of the proposed method in complex, non-planar, and cluttered environments.
Author(s): Deng P, Yao J, Li C, Wang S, Li X, Ojha V, He X
Publication type: Article
Publication status: Published
Journal: Computer-Aided Civil and Infrastructure Engineering
Year: 2026
Volume: 45
Print publication date: 01/05/2026
Online publication date: 22/04/2026
Acceptance date: 09/02/2026
Date deposited: 18/05/2026
ISSN (print): 1093-9687
ISSN (electronic): 1467-8667
Publisher: Elsevier Inc.
URL: https://doi.org/10.1016/j.cacaie.2026.100019
DOI: 10.1016/j.cacaie.2026.100019
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