Computer-Aided Civil and Infrastructure Engineering最新文献

{"title":"An unsupervised cross-domain method for bridge damage detection based on multichannel symmetric dot pattern feature alignment","authors":"Naiwei Lu,&nbsp;Xiangyuan Xiao,&nbsp;Jian Cui,&nbsp;Yiru Liu,&nbsp;Ke Huang,&nbsp;Ka-Veng Yuen","doi":"10.1111/mice.70117","DOIUrl":"10.1111/mice.70117","url":null,"abstract":"<p>A critical issue for data-driven and machine learning-based damage detection of engineering infrastructures is associated with unlabeled datasets and distribution shifts in cross-domains. To overcome this challenge, this study develops an unsupervised cross-domain method for bridge damage detection based on interclass alignment of time-frequency features extracted from multichannel sensor data. The computational framework was developed based on a deep subdomain adaptation network integrating digital and physical information. Initially, a multichannel symmetric dot pattern was utilized to transform the structural acceleration signals into a comprehensive image. Subsequently, a convolutional block attention module-enhanced ResNet34 (CBAM-ResNet34) was constructed to extract discriminative time-frequency features, where a local maximum mean discrepancy principle was introduced to perform class-conditional alignment across subdomains. Compared with traditional global domain alignment methods, the proposed approach focuses on aligning class-conditional distributions within subdomains to improve the generalization performance with unlabeled datasets. The proposed method was validated on both simulated and experimental datasets collected from a laboratory-scaled steel truss bridge. Furthermore, a case study on the Old ADA Bridge in Japan was presented to demonstrate the robustness and practical applicability of the proposed approach, serving as a benchmark against classic unsupervised methods. The results show that the proposed framework has a substantial improvement in source-to-target transfer recognition performance. Discussions were conducted on the application prospects of the proposed framework for more in-service infrastructures in complex conditions.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 29","pages":"5698-5718"},"PeriodicalIF":9.1,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145397645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cover Image, Volume 40, Issue 26","authors":"","doi":"10.1111/mice.70124","DOIUrl":"https://doi.org/10.1111/mice.70124","url":null,"abstract":"<p><b>The cover image</b> is based on the article <i>Domain-adaptive self-supervised learning for corrosion detection and 3D building information model mapping in steel tunnels</i> by Shreejan Maharjan et al., https://doi.org/10.1111/mice.70077.\u0000\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 26","pages":""},"PeriodicalIF":9.1,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70124","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145375364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cover Image, Volume 40, Issue 26","authors":"","doi":"10.1111/mice.70123","DOIUrl":"https://doi.org/10.1111/mice.70123","url":null,"abstract":"<p><b>The cover image</b> is based on the article <i>Excavator 3D pose estimation from point cloud with self-supervised deep learning</i> by Mingyu Zhang et al., https://doi.org/10.1111/mice.13500.\u0000\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 26","pages":""},"PeriodicalIF":9.1,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70123","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145375287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Single-image 3D particle reconstruction via generative AI-empowered large vision models","authors":"Zizun Zhu,&nbsp;Tongming Qu,&nbsp;Jidong Zhao","doi":"10.1111/mice.70113","DOIUrl":"10.1111/mice.70113","url":null,"abstract":"<p>This study presents a diffusion-assisted particle reconstruction model (DPRM), a novel framework for reconstructing high-fidelity 3D particle morphology from a single 2D image of granular assemblies. DPRM leverages cascaded large vision models in three stages: (1) segmentation of individual grains via a U-Net-enhanced segment anything model, (2) multi-view synthesis for each particle using denoising diffusion probabilistic models (DDPMs), and (3) 3D geometry approximation via a DDPM-assisted large reconstruction model, generating simulation-ready mesh representations. After mesh decimation and size correction, the outputs are compatible with discrete element modeling and other physics-based simulations. Quantitative validations confirm DPRM's accuracy in predicting particle size and shape distributions. Crucially, the developed method enables zero-shot generation to novel scenarios without extensive retraining, overcoming limitations of prior methods. This work establishes the first end-to-end pipeline for particle-level 3D reconstruction from monocular scene images, enabling the generation of statistically realistic particle shape for physics-based granular simulations in engineering and industry.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 28","pages":"5288-5306"},"PeriodicalIF":9.1,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145382231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Inverted Hermite Kolmogorov–Arnold Network Transformer for multi-point settlement prediction in high-speed railway bridge piers","authors":"Xunqiang Gong,&nbsp;Qi Liang,&nbsp;Hongyu Wang,&nbsp;Tieding Lu,&nbsp;Junjie Liu,&nbsp;Zhiping Chen,&nbsp;Rui Zhang","doi":"10.1111/mice.70109","DOIUrl":"10.1111/mice.70109","url":null,"abstract":"<p>Settlement monitoring of high-speed railway bridge piers (HSR-BPs) is critical for ensuring construction and operational safety. However, existing methods for BP settlement prediction face three challenges: limited dataset size, inconsistent observation periods across measuring points, and difficulty in synchronizing spatiotemporal correlations between points. To address these issues, this paper proposes a multi-point prediction method for HSR-BPs based on the Inverted Hermite Kolmogorov–Arnold Network (KAN) Transformer. First, construct a dataset with consistent observation days through interpolation and measuring points screening; Second, integrate a HermiteKANLinear Module into the Key-Query-Value attention mechanism to capture spatial–temporal dependencies; Then, replace the traditional multilayer perceptron with a HermiteKAN to improve prediction accuracy. Experimental validation using 34 sets of augmented settlement data demonstrates the proposed method's superiority over 14 baseline methods. Experiments of both single and multiple measurement points show significant performance gains: Compared to the single measurement point, the multiple measurement points using the proposed method reduce mean absolute error, root mean square error, and MAPE by 21.16%, 20.57%, and 21.11%, respectively. Furthermore, the proposed method outperforms eight deep learning models across varying prediction lengths. Ablation studies confirm that each proposed component contributes to the overall optimal performance in all evaluation metrics, validating the proposed method's effectiveness and precision. The dataset and codes are available at https://github.com/RSIDEA-ECUT/IHKTransformer.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 29","pages":"5576-5595"},"PeriodicalIF":9.1,"publicationDate":"2025-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70109","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145382299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating triple attention convolutional network with multi-objective optimization for excavation-induced deformation prediction","authors":"Ping He,&nbsp;Honggui Di,&nbsp;Zhiyao Tian,&nbsp;Zhanlin Cao,&nbsp;Shunhua Zhou","doi":"10.1111/mice.70116","DOIUrl":"10.1111/mice.70116","url":null,"abstract":"<p>Accurate and rapid prediction of deep excavation deformation is crucial for construction safety and environmental protection. Traditional finite element analysis is time-consuming, while single-objective optimization (SOO) tends to cause parameter compensation effects. This paper proposes a deep learning and multi-objective optimization (MOO) approach for excavation deformation prediction. A triple-attention convolutional network (TACN) is constructed to capture the complex interactions among soil parameters, deformation locations, and excavation stages. Integrating the proposed TACN, a TACN-MOO optimization framework is established to perform rapid parameter identification and deformation prediction by simultaneously considering wall deflection and ground settlement. Validation through a Shanghai excavation project shows: (1) TACN effectively captures nonlinear soil-deformation relationships with higher accuracy than convolutional neural network models; (2) the MOO framework effectively mitigates parameter compensation effects while reducing computation time from 8+ h to 1–2 min; (3) engineering applications demonstrate that the method achieves high accuracy in wall deflection prediction and good agreement in settlement estimation with excellent transferability. This research provides an efficient and reliable technical framework for intelligent prediction and dynamic control of deep excavation deformation.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 29","pages":"5534-5553"},"PeriodicalIF":9.1,"publicationDate":"2025-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70116","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145382302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ontology-driven intelligent assessment system for dam structural safety based on spatiotemporal anomaly detection framework","authors":"Xiaosong Shu,&nbsp;HaiBo Yang,&nbsp;Yuhang Zhou,&nbsp;Peng Xu,&nbsp;Xinyi Liu,&nbsp;Jinjun Guo,&nbsp;Yingchun Cai,&nbsp;Jingyu Fan,&nbsp;Fan Wu","doi":"10.1111/mice.70115","DOIUrl":"10.1111/mice.70115","url":null,"abstract":"<p>Dam safety assessment systems play a pivotal role in evaluating the structural integrity of critical hydraulic infrastructures. Current implementations frequently exhibit limitations in critical functionalities including multi-source data integration and automated anomaly detection. This study proposes an ontology-enhanced intelligent assessment system featuring three technical innovations. A multi-level semantic representation framework is proposed to formally model structural components, sensor networks, and their spatiotemporal relationships through domain-specific ontology engineering. A hybrid anomaly detection architecture employs spatiotemporal variational autoencoder to enable unsupervised identification of abnormal signals. A knowledge-informed reasoning framework integrates empirical safety rules and detection results through Semantic Web Rule Language and SPARQL Protocol and Resource Description Framework Query Language query. Experimental validation on a double-curvature arch dam demonstrated superior performance. The proposed system achieves 89.1% anomaly detection accuracy, simplifies the semantic query through ontology-driven knowledge indexing, and enables automated diagnostic reasoning that identifies the causal relationships between abnormal signals and environmental triggers.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 29","pages":"5649-5671"},"PeriodicalIF":9.1,"publicationDate":"2025-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70115","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145382300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hierarchical nondestructive detection of full-scene suspended ceiling systems using point cloud","authors":"Qinghua Guo,&nbsp;Weihang Gao,&nbsp;T. Y. Yang,&nbsp;Xilin Lu","doi":"10.1111/mice.70111","DOIUrl":"10.1111/mice.70111","url":null,"abstract":"<p>Suspended ceiling (SC) systems constitute a critical nonstructural building component. Excessive deformation of the ceiling surface can cause life-threatening falling debris during earthquakes and create voids that may expose occupants to hazardous materials concealed above the ceiling. To address limitations of the in-service detection of SC deformation, this paper presents a point cloud–based full-scene SC detection method, integrating region growing, Hough Transform, a customized Set2Seq network, and robust principal component analysis to achieve a complete workflow from ceiling segmentation, panel extraction to deformation quantification. Point cloud data with color information acquired from two precision-differentiated devices are used in substage tests and holistic evaluation. The substage tests demonstrate that the local panel deformation quantitative accuracy of the proposed method is generally over 80%, and the holistic experiments show the feasibility of full-scenario practical application.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 29","pages":"5380-5395"},"PeriodicalIF":9.1,"publicationDate":"2025-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70111","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145382301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A deep learning methodology for rapid identification of slab damage in concrete face rockfill dams","authors":"Jianghan Xue,&nbsp;Pengtao Zhang,&nbsp;Junru Li,&nbsp;Xiang Lu,&nbsp;Zefa Li,&nbsp;Yanling Li,&nbsp;Jiankang Chen,&nbsp;Chufeng Kuang","doi":"10.1111/mice.70110","DOIUrl":"10.1111/mice.70110","url":null,"abstract":"<p>Accurate identification of slab conditions is critical for ensuring the seepage safety of concrete face rockfill dams (CFRDs). However, existing methods for monitoring slab damage are limited and inconvenient. There is an urgent need to utilize available monitoring data to rapidly and accurately assess the condition of slab damage, and to implement responsive decision-making and early warning measures to prevent dam failure. To address this issue, this study proposes a deep learning (DL) methodology for the rapid identification of slab damage. A multi-objective optimization algorithm, Non-dominated Sorting Genetic Algorithm ll (NSGA-II), is employed to carry out inversion analysis and obtain the actual permeability coefficients, which are then used as inputs in numerical seepage simulations of dam behavior after slab damage. Based on the simulation results, a DL model is constructed to establish an accurate mapping relationship between the information of slab damage and the corresponding monitoring data. Given that current DL models often fail to explicitly and effectively capture the importance of features across sequences—leading to redundancy, reduced accuracy, and poor interpretability, a hierarchical optimization structure based on multi-head attention mechanisms is proposed. Specifically, two multi-head attention modules controlling input weights are innovatively integrated into both the input and hidden layers of the DL model, forming a dual multi-head attention enhanced (DMAE) architecture. This structure can be embedded within basic DL models for training and prediction. A case study of the cracked Sanbanxi CFRD project shows that the DMAE-Bi-directional Long Short-Term Memory (BiLSTM) model outperforms other DL models in terms of prediction accuracy and robustness, suggesting it is the most suitable for the identification and prediction of slab damage. Furthermore, the visualization of input attention weights reveals that the key factors in identifying slab damage and should be prioritized in future seepage pressure monitoring. This study fills a critical gap in the field of slab damage identification, provides both technical support and theoretical foundations for intelligent diagnosis and interpretability analysis of slab damage in CFRDs.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 29","pages":"5596-5624"},"PeriodicalIF":9.1,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145396613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An integrated spatiotemporal trust and consensus fusion framework for dam safety assessment with multi-sensor anomaly detection","authors":"Xiaosong Shu,&nbsp;HaiBo Yang,&nbsp;Fan Wu,&nbsp;Yuhang Zhou,&nbsp;Peng Xu,&nbsp;Xinyi Liu,&nbsp;Jinjun Guo,&nbsp;Yingchun Cai,&nbsp;Jinyu Fan","doi":"10.1111/mice.70112","DOIUrl":"10.1111/mice.70112","url":null,"abstract":"<p>Multiple sensors are strategically deployed within concrete dams to monitor structural behavior under intricate environmental conditions. The diverse monitoring parameters, spatial configurations, and temporal variations across these sensors often engender performance conflicts. It is different to obtain the comprehensive dam safety status under the intricate relations and behavior conflicts. To solve the problem, the proposed model integrates anomaly detection, trust propagation, consensus measurement, and information fusion for dam safety assessment. The multi-expert variational autoencoder facilitates anomaly score computations. The social trust network delineates spatiotemporal relationships among sensors. The consensus measurement mitigates information conflicts for data integration using the interval-valued fusion strategy. Empirical validation through a case study involving an arch dam underscores the model's efficacy in identifying anomalies. Through the results analysis, the spatial relationships exhibit divergent attributes in response to changes in water levels. It indicates that the spatial relations are necessary factors in the dam safety assessment.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 29","pages":"5625-5648"},"PeriodicalIF":9.1,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70112","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145396511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}