Computer-Aided Civil and Infrastructure Engineering最新文献

{"title":"Computer vision-based real-time cable safety assessment under vehicle-induced bridge fires","authors":"Jinglun Li,&nbsp;Binyang Wang,&nbsp;Xiaoyi Zhou,&nbsp;Raffaele Cucuzza,&nbsp;Kang Gao,&nbsp;Xiang Yun","doi":"10.1111/mice.70108","DOIUrl":"10.1111/mice.70108","url":null,"abstract":"<p>Vehicle-induced fires present a critical risk to cable-supported bridges, where the integrity of cable components is especially vulnerable. However, conventional monitoring solutions face significant limitations: infrared cameras are often economically prohibitive, and standard smoke detectors are ineffective in open bridge environments. To address these challenges, this paper proposes a multi-stage computer vision framework that utilizes existing visual surveillance infrastructure for real-time fire detection and preliminary cable safety assessment. The proposed system integrates a you only look once v11-m model for accurate vehicle detection, a BoT-SORT tracker with re-identification (Re-ID) capabilities to maintain target consistency through visual obstructions such as smoke, and a ResNet-50 classifier for vehicle-centric fire identification. The framework's novelty lies in the demonstrated synergistic operation of these components across various scenarios, particularly under actual fire conditions. The integration of the Re-ID module proves essential for eliminating false alarms by preserving target identity, while the vehicle-centric approach directly associates fire events with specific vehicles and their tracking identifiers. This linkage provides the fundamental basis for real-time safety evaluation of adjacent cables. Consequently, the framework establishes a cost-effective, readily deployable, and scalable solution for bridge monitoring, offering management authorities a practical tool for immediate fire detection and instant structural assessment.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 28","pages":"5269-5287"},"PeriodicalIF":9.1,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70108","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145382303","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":"Proactive framework for evaluating retrieval-augmented generation-based learning assistants in engineering education","authors":"Linxin Hua,&nbsp;Lirui Guo,&nbsp;Nan Zheng,&nbsp;Ye Lu,&nbsp;Jia Xu,&nbsp;Jianghua Deng","doi":"10.1111/mice.70063","DOIUrl":"https://doi.org/10.1111/mice.70063","url":null,"abstract":"<p>Retrieval-augmented generation (RAG) enabled learning assistants are promising for engineering education, given their capability to supplement domain-specific knowledge and enhance student support. However, it is also a known problem that RAG demands adequate knowledge bases and can experience unreliable retrieval generation alignment. This study proposes a proactive evaluation framework for RAG-based learning assistants, eliminating the need for student feedback in system evaluation. The framework is demonstrated using a Civil Engineering education tool, CivASK. The evaluation framework identifies the deficiencies in CivASK, including database gap, contextual misunderstanding, and incomplete retrievals, based on the performances under simulated student inquiries, automated retrieval ranking, and expert-validated evaluations. Specifically, 742 student queries are analyzed, and 374 test questions are generated for assessment, showing the practical utility of the proposed evaluation framework for real-world education assist development. The application of the proposed framework is transferable to assist other engineering courses as well.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 26","pages":"4651-4668"},"PeriodicalIF":9.1,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70063","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145375351","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":"Refined segmentation of high-resolution bridge crack images via probability map-guided point rendering technique","authors":"Honghu Chu,&nbsp;Weiwei Chen,&nbsp;Lu Deng","doi":"10.1111/mice.70088","DOIUrl":"10.1111/mice.70088","url":null,"abstract":"<p>High-resolution (HR) imaging technology is increasingly employed to capture crack images in civil infrastructure, which is vital for ensuring the safety of the bridge inspection process conducted via unmanned aerial vehicles (UAVs). Such applications require the development of advanced algorithms for the segmentation of HR images. Traditional deep learning-based segmentation methods for inferencing HR images consume considerable GPU resources, which prompts the authors to draw inspiration from the cost-effective rendering technique in computer graphics and try to apply this advanced method to the refined segmentation of HR crack images. However, the original rendering method, designed to guide rendering points by the coarse segmentation masks, often inadequately directs rendering points towards the crucial boundary areas of tiny cracks, leading to unclear or missing boundary predictions. To address this, an innovative rendering technique was proposed, utilizing probability maps to precisely direct rendering points towards crack boundaries and tiny-crack branches during inference. This method enhances the accuracy of crack boundary segmentation and reduces the miss rate of tiny crack branches from HR images, all while conserving computational resources. Through model parameter experiments and ablation studies, the optimal model was obtained, and the effectiveness of the improved components was demonstrated. Furthermore, the field test has confirmed that, equipped with the proposed point rendering technique, the UAV is permitted to effectively perform crack inspection within a 3-m distance from the main beam. Compared to traditional low-resolution semantic segmentation methods, the UAV bridge inspection time is significantly reduced by 50% while maintaining the same accuracy.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 27","pages":"4946-4969"},"PeriodicalIF":9.1,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70088","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145396510","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":"Efficient bridge damage detection using a lightweight attention-based modeling framework","authors":"Junjie Huang,&nbsp;Yongzhuo Zhu,&nbsp;Mingfu Xiong,&nbsp;Javier Del Ser,&nbsp;Aziz Alotaibi,&nbsp;João Paulo Papa,&nbsp;Khan Muhammad","doi":"10.1111/mice.70098","DOIUrl":"10.1111/mice.70098","url":null,"abstract":"<p>Currently, real-time assessment of surface damage to bridges is crucial for ensuring infrastructure safety. Unfortunately, existing methods often present a challenge: overly complex computational models are incompatible with systems that have limited resources, while lightweight models struggle to achieve sufficient detection accuracy. This task is further complicated by the diverse nature of bridge damages, such as cracks, exposed reinforcement, and efflorescence, as well as the challenges of data acquisition under varied conditions from sources like unmanned aerial vehicles and specialized datasets. This work presents an efficient framework developed to improve such applications. The Lightweight Feature Enhancement and Triplet Attention Network for Bridge Damage Detection includes: (1) a multi-scale feature learning module, (2) a slim-neck-based optimized feature pyramid integration module, and (3) a triplet attention-based damage detector module; (1) extracts multi-scale representations of bridge surface features, (2) enhances multi-scale feature integration for lightweight computation, while maintaining accuracy, and (3) optimizes the framework with a three-branch structure for cross-latitude interaction, reducing the importance of irrelevant features. Extensive experiments on the MCDS and CODEBRIM datasets demonstrated its advantages: a <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>5.6</mn>\u0000 <mo>%</mo>\u0000 </mrow>\u0000 <annotation>$5.6%$</annotation>\u0000 </semantics></math> increase in Mean Average Precision, a <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>13.6</mn>\u0000 <mo>%</mo>\u0000 </mrow>\u0000 <annotation>$13.6%$</annotation>\u0000 </semantics></math> computational load reduction, and a 45 frames per second real-time performance. The model's computational complexity scales linearly with the input instances processed per unit time during inference.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 27","pages":"4758-4773"},"PeriodicalIF":9.1,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70098","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145396514","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":"Assessment of road network vulnerability using multilayer perceptron surrogates with automated closure propagation","authors":"Abdel Rahman Marian,&nbsp;Mohammad Zaher Serdar,&nbsp;Eyad Masad","doi":"10.1111/mice.70105","DOIUrl":"10.1111/mice.70105","url":null,"abstract":"<p>Road networks face increasing disruptions, yet vulnerability assessment methods either oversimplify traffic dynamics or require extensive computational simulations. This research introduces a novel approach integrating traffic simulation, graph theory, and machine learning for efficient and accurate vulnerability assessment. Analysis across numerous disruption scenarios showed that static weighting is inadequate for capturing traffic redistribution effects. In contrast, dynamic weighting aligns strongly with simulation results but was computationally infeasible. To overcome this limitation, a specialized multilayer perceptron artificial neural network (ANN) model was developed with a dual-pathway architecture and a novel automated closure propagation algorithm, separating static network attributes from spatial relationships. This surrogate model generates predictions significantly faster than traffic simulations, enabling comprehensive vulnerability analyses, previously deemed impractical. Testing across diverse disruption scales demonstrated surrogate effectiveness and limitations. This research presents a transferable and scalable methodology for constructing simulation-informed ANN surrogate models, providing practical deployment guidance for informed resilient transportation network planning.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 28","pages":"5325-5350"},"PeriodicalIF":9.1,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70105","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145396512","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":"Machine learning-based analysis of interaction effects among influencing factors on the resilient modulus of stabilized aggregate base","authors":"Meng Guo,&nbsp;Mengmeng Zhou,&nbsp;Xiuli Du,&nbsp;Pengfei Liu","doi":"10.1111/mice.70102","DOIUrl":"https://doi.org/10.1111/mice.70102","url":null,"abstract":"<p>To overcome the limitations of conventional single-factor analysis, this study proposed a framework for investigating interaction effects of influencing factors on the resilient modulus (M_r) of stabilized aggregate base. First, cross-validation was utilized to compare the predictive accuracy and generalization capability of gradient boosting (GB) and random forest (RF) in predicting the M_r. The grid search algorithm was used to optimize hyperparameters. After optimization, the coefficient of determination for GB reached 0.99 on the training set and 0.96 on the test set, while those for RF were 0.98 and 0.94, respectively. The results indicated that GB demonstrated higher predictive accuracy for the M_r. Finally, the importance analysis, univariate sensitivity analysis, and bivariate interaction sensitivity analysis of influencing factors were systematically conducted using partial dependence plots (PDP) and Shapley additive explanations (SHAP). The research results showed that the importance of influencing factors on the M_r decreases in the order of maximum dry density to optimum moisture content ratio, wet–dry cycles (WDC), deviator stress, confining pressure, and ratio of oxide compounds in the cementitious materials. The bivariate interaction sensitivity analysis of the WDC, deviator stress, confining pressure, and ratio of oxide compounds in the cementitious materials did not disrupt their single-variable sensitivity relationships with the M_r. The variation of the WDC would destroy the single variable sensitivity relationship between the optimum moisture content ratio and M_r.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 28","pages":"5253-5268"},"PeriodicalIF":9.1,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70102","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145537827","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":"Multi-fidelity data meta-learning approach for seismic response prediction of high-rise shear wall buildings","authors":"Chenyu Zhang,&nbsp;Changhai Zhai,&nbsp;Weiping Wen,&nbsp;Guoqing Zhang","doi":"10.1111/mice.70103","DOIUrl":"https://doi.org/10.1111/mice.70103","url":null,"abstract":"<p>Rapid and accurate estimation of seismic responses in city-scale buildings is critical for post-earthquake loss assessment and pre-event identification of vulnerable buildings. However, conventional numerical simulation methods struggle to balance efficiency and accuracy when applied to large-scale buildings, while existing data-driven methods often rely on single-source datasets, limiting generalizability. Numerical simulation data of varying detail (e.g., floor- and component-based models) and field monitoring data form inherently multi-fidelity datasets, but integrating these heterogeneous sources remains challenging, particularly when different fidelities correspond to different building targets. To address this gap, we propose a multi-fidelity meta-learning algorithm that extends deep learning methods for seismic response prediction, demonstrated on multiple high-rise shear wall buildings. The proposed algorithm enables incremental data learning and model updates and is applied and validated across datasets of varying fidelities, including multiple numerical simulations and field monitoring data. Under small-sample field monitoring scenarios, the proposed method reduces overall prediction errors by 40.4%, compared to the typical transfer learning approach, demonstrating superior learning capabilities in limited-data settings. Additionally, to account for inaccuracies and potential noise in acquired structural information inputs under real-world conditions, the meta-learning model was trained and evaluated with varying levels of noise based on field monitoring data. Results indicate that the proposed meta-learning algorithm exhibits strong robustness when handling noisy inputs.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 29","pages":"5512-5533"},"PeriodicalIF":9.1,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70103","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145659633","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":"Probabilistic risk assessment framework for cost overruns predictions in infrastructure projects using randomized simulations","authors":"Rubina Canesi,&nbsp;Laura Gabrielli,&nbsp;Giuliano Marella,&nbsp;Aurora Greta Ruggeri","doi":"10.1111/mice.70100","DOIUrl":"https://doi.org/10.1111/mice.70100","url":null,"abstract":"<p>This paper introduces PRIMoS (Probabilistic Risk matrix Integration with MOnte carlo Simulation), an advanced computational framework that enhances cost overrun risk assessment and uncertainty quantification in infrastructure project management. PRIMoS is an innovative Bayesian Monte Carlo simulation framework integrated with a probabilistic risk matrix, providing comprehensive cost risk analysis. The proposed framework simultaneously addresses both cost uncertainties and time uncertainties, the latter through discount rate assessment, extending beyond traditional cost-focused approaches. PRIMoS employs a novel method to define risk magnitude (RM) levels for all project components, enabling adaptive probability distributions for Monte Carlo inputs. This approach allows for the capture of specific cost-related interdependencies and evolving risk patterns within the financial aspects of the project lifecycle. The framework's efficacy was demonstrated through application to a large infrastructure project, showcasing its ability to provide more accurate and detailed cost overrun forecasts compared to conventional methods. The proposed model improved cost estimation accuracy by predicting an increase in contingencies, thereby reducing the estimation error to less than 5%. PRIMoS offers a powerful tool for proactive risk management and informed decision-making in large-scale infrastructure development.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 27","pages":"4774-4796"},"PeriodicalIF":9.1,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70100","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145442977","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":"Automated path-planning strategy for robotic inspection of underground utilities based on building information model","authors":"Zihan Yang,&nbsp;Jiangpeng Shu,&nbsp;Jishuang Jiang,&nbsp;Wentao Han,&nbsp;Yichang Wang,&nbsp;Liang Zhao,&nbsp;Yong Bai","doi":"10.1111/mice.70107","DOIUrl":"10.1111/mice.70107","url":null,"abstract":"<p>This paper proposes a fully automated end-to-end inspection-path-planning strategy for underground utilities, such as pipelines, based on building information modeling (BIM). An automatic extraction method is developed to process utility information from BIM models, using a registration step that pairs each pipeline with its corresponding utility branch. This is followed by geometric modification via offset algorithms that account for obstacle dimensions to generate safe navigation paths. A novel inspection algorithm, the utility-Chinese postman problem (U-CPP), is introduced to generate a topological map and ensure full-coverage inspection. A Dynamo prototype integrates all these algorithms, minimizing manual intervention and achieving full-process automation. The method is validated with three real-world utility BIM models featuring diverse cross-sectional configurations. The U-CPP algorithm achieves 100% coverage with minimal repetition rates and computes optimized inspection paths in 24, 23, and 23 ms. Results demonstrate that the proposed strategy efficiently automates both information extraction and full-coverage path planning. The U-CPP algorithm proves to be robust, computationally efficient, and effective in handling diverse utility configurations.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 29","pages":"5554-5575"},"PeriodicalIF":9.1,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70107","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145295094","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":"Hypothesis generation from pragmatic causal relationships for latent knowledge reasoning in the civil engineering domain","authors":"Sangbin Lee,&nbsp;Robin Eunju Kim","doi":"10.1111/mice.70101","DOIUrl":"10.1111/mice.70101","url":null,"abstract":"<p>Structural health monitoring (SHM) research generates vast amount of information, especially as unstructured data formats. To date, most natural language processing (NLP) applications focus on extracting information (syntactic or semantic level) rather than providing latent knowledge and generating newer information (pragmatic level). Thus, this study proposes a pragmatic NLP framework integrating named entity recognition (NER) model (BERT–BiLSTM–CRF), domain-specific knowledge graph (KG), and hypothesis generation. Using a labeled dataset, the semantic-aware NER model achieved 0.8998 accuracy and 0.8705 F1 score, allowing precise label prediction for unseen texts. Then, domain-specific KG constructed interrelations across diverse literature, blending insights. From this enriched KG, the framework generated candidate hypotheses to provide latent knowledge. In this work, the generated hypothesis is validated by showing a strong correlation to the literature. The results of this study showed the potential of pragmatic NLP on SHM, offering pathways for latent knowledge reasoning and cross-disciplinary research insight discovery.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 29","pages":"5447-5473"},"PeriodicalIF":9.1,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70101","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145295095","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}