{"id":5543,"date":"2026-05-04T11:08:45","date_gmt":"2026-05-04T03:08:45","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=5543"},"modified":"2026-05-04T23:17:04","modified_gmt":"2026-05-04T15:17:04","slug":"jase-202609-32-028","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202609-32-028","title":{"rendered":"Inverse Analysis of Internal Crack Propagation Depth in Cantilever Anti-Sliding Piles Based on Artificial Intelligence and XFEM"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 32<\/a><\/p>\n<\/div>\n\n\n\n

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Qingyang Ren1,2<\/sup>, Yanding Wang1,2<\/sup><\/i><\/a>, Songqiang Xiao1,2<\/sup>, Yanping Jia1,2<\/sup>, Senlin Gao1,2<\/sup>, and Yong Zeng1,2<\/sup><\/p>\n\n\n\n

1<\/sup>State Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing Jiaotong University, Chongqing 400074, China<\/p>\n\n\n\n

2<\/sup>School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China<\/p>\n<\/div>\n\n\n\n

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Received: October 9, 2025
Accepted: April 19, 2026
Publication Date: May 4, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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ABC algorithm flowchart.<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202609_32.028<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Cantilever anti-sliding piles are critical for slope stability, yet their subsurface health assessment remains challenged due to the invisibility of internal damage. This study proposes an on-destructive inversion framework that integrates computer vision with computational mechanics to quantify internal crack propagation depth based on surface morphological features. To address the limitations of the standard Artificial Bee Colony (ABC) algorithm, specifically its slow convergence and susceptibility to local optima\u2014an Improved ABC (I-ABC) algorithm is developed. This enhancement incorporates a neighborhood-based optimal guidance mechanism and an adaptive selection probability strategy to strike an optimal balance between global exploration and local exploitation. The proposed framework first employs an optimized YOLOv8 model, with hyper-parameters tuned via the I-ABC algorithm, to extract surface crack parameters. These parameters are subsequently mapped to internal depths using a normalized objective function derived from an Extended Finite Element Method (XFEM) forward modeling database. Experimental validation, utilizing a hybrid dataset comprising physical pile tests and numerical simulations, demonstrates that the proposed method achieves a crack depth inversion accuracy of 96%. Furthermore, statistical comparisons reveal that the I-ABC algorithm significantly outperforms Particle Swarm Optimization (PSO), Differential Evolution (DE), and Grey Wolf Optimizer (GWO) in terms of robustness and convergence speed. Ultimately, this framework provides a reliable and automated tool for the
structural health monitoring of geotechnical infrastructure<\/p>\n\n\n\n

Keywords: Cantilever anti-sliding pile; Artificial Bee Colony Algorithm; Adaptive parameters; Crack propagation depth; Extended Finite Element Method<\/em><\/p>\n\n\n\n

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