Computer-Aided Civil and Infrastructure Engineering最新文献

{"title":"Multimodal Mamba with multitask learning for building flood damage assessment using synthetic aperture radar remote sensing imagery","authors":"Yu‐Hsuan Ho, Ali Mostafavi","doi":"10.1111/mice.70059","DOIUrl":"https://doi.org/10.1111/mice.70059","url":null,"abstract":"Most post‐disaster damage classifiers perform best when destructive forces leave clear spectral or structural signatures. However, these signatures are often subtle or absent after inundation, where damage may be nonstructural and difficult to detect. Consequently, existing models perform poorly at identifying flood‐related building damage. The model presented in this study, Flood‐DamageSense, addresses this gap as the first deep learning framework purpose‐built for building‐level flood‐damage assessment. The architecture fuses pre‐ and post‐event synthetic aperture radar/interferometric synthetic aperture radar (SAR/InSAR) scenes with very high‐resolution optical basemaps and an inherent flood‐risk layer that encodes long‐term exposure probabilities, guiding the network toward plausibly affected structures even when compositional change is minimal. A multimodal Mamba backbone with a semi‐Siamese encoder and task‐specific decoders jointly predicts (1) graded building‐damage states, (2) floodwater extent, and (3) building footprints. Training and evaluation on Hurricane Harvey (2017) imagery from Harris County, Texas—supported by insurance‐derived property‐damage extents—show a mean F1 improvement of up to 19 percentage points over state‐of‐the‐art baselines, with the largest gains in the frequently misclassified “minor” and “moderate” damage categories. Ablation studies identify the inherent‐risk feature as the single most significant contributor to this performance boost. An end‐to‐end post‐processing pipeline converts pixel‐level outputs to actionable, building‐scale damage maps within minutes of image acquisition. By combining risk‐aware modeling with SAR's all‐weather capability, Flood‐DamageSense delivers faster, finer‐grained, and more reliable flood‐damage intelligence to support post‐disaster decision‐making and resource allocation.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"26 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144924119","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 enhanced approach for weaving area capacity estimation combining high-fidelity simulation and interpretable machine learning","authors":"Jian Rong, Peijia Wu, Yanjie Zeng, Yacong Gao, Yi Wang","doi":"10.1111/mice.70055","DOIUrl":"10.1111/mice.70055","url":null,"abstract":"<p>Accurately assessing weaving area capacity is critical for optimizing traffic management. However, transportation agencies face a persistent challenge: Existing capacity models often inadequately characterize vehicle weaving behaviors and fail to quantify the interactions between influencing factors. This limitation hinders the development of precise traffic control strategies in weaving areas. Addressing this, we propose an integrated methodology combining enhanced microscopic simulation with interpretable machine learning. The proposed method is tested and validated on two datasets. The DIEGA calibration-improved method integrates density-based spatial clustering of applications with noise (DBSCAN) clustering, information entropy, and genetic algorithms (GA) to achieve superior modeling accuracy and 22.2% faster convergence than GA. Simulation experiments demonstrate that under a constant weaving flow of 1900 pcu/h, variations in ramp-to-freeway (<span></span><math>\u0000 <semantics>\u0000 <msub>\u0000 <mi>Q</mi>\u0000 <mi>RF</mi>\u0000 </msub>\u0000 <annotation>${Q}_{mathrm{RF}}$</annotation>\u0000 </semantics></math>)/freeway-to-ramp (<span></span><math>\u0000 <semantics>\u0000 <msub>\u0000 <mi>Q</mi>\u0000 <mi>FR</mi>\u0000 </msub>\u0000 <annotation>${Q}_{mathrm{FR}}$</annotation>\u0000 </semantics></math>) ratios induce a capacity fluctuation of approximately 15% (ranging from 4277 to 4937 pcu/h) and indicate nonlinear coupling among <span></span><math>\u0000 <semantics>\u0000 <msub>\u0000 <mi>Q</mi>\u0000 <mi>RF</mi>\u0000 </msub>\u0000 <annotation>${Q}_{mathrm{RF}}$</annotation>\u0000 </semantics></math>, <span></span><math>\u0000 <semantics>\u0000 <msub>\u0000 <mi>Q</mi>\u0000 <mi>FR</mi>\u0000 </msub>\u0000 <annotation>${Q}_{mathrm{FR}}$</annotation>\u0000 </semantics></math>, weaving length (<span></span><math>\u0000 <semantics>\u0000 <msub>\u0000 <mi>L</mi>\u0000 <mi>W</mi>\u0000 </msub>\u0000 <annotation>${L}_{mathrm{W}}$</annotation>\u0000 </semantics></math>), and capacity. The ML_RF capacity model outperforms the baseline model while providing sHapley additive exPlanations (SHAP)-based interpretability of factor interactions. The methodology's demonstrated capability to identify optimal capacity ranges (4635–4860 pcu/h at <span></span><math>\u0000 <semantics>\u0000 <msub>\u0000 <mi>Q</mi>\u0000 <mi>RF</mi>\u0000 </msub>\u0000 <annotation>${Q}_{mathrm{RF}}$</annotation>\u0000 </semantics></math>/<span></span><math>\u0000 <sem","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 25","pages":"4273-4295"},"PeriodicalIF":9.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70055","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144924124","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":"Virtual sensing of seismic floor responses for rapid prioritization of critical equipment inspection in nuclear power plants","authors":"Jingoo Lee, Seungjun Lee, Young‐Joo Lee, Jaebeom Lee","doi":"10.1111/mice.70051","DOIUrl":"https://doi.org/10.1111/mice.70051","url":null,"abstract":"Critical equipment in nuclear power plant auxiliary buildings such as control cabinets, panels, transformers, and diesel generators often malfunction before structural damage occurs, demanding rapid post‐earthquake inspection prioritization. However, direct walkdown inspection or dense sensor networks are impractical due to restricted accessibility in radiological zones and the high costs associated with maintenance. To address this, we propose a residual convolutional network‐based virtual sensing framework that supports urgent inspection prioritization by predicting acceleration at 139 locations from a single high‐quality seismometer. The model employs six residual blocks with progressively downsized kernels to capture multi‐scale features, while skip connections prevent vanishing gradients. Trained on artificial earthquakes with 10 dB noise and validated against unseen Next Generation Attenuation‐West 2 ground motions matched to Nuclear Regulatory Commission Regulatory Guide 1.60 and Korean uniform‐hazard spectra, the model achieves a maximum mean absolute percentage error of 0.44%–0.59% for noise‐free case and ≤4.23% at 10 dB, demonstrating robust generalization. The resulting rapid, noise‐tolerant virtual sensor network enables actionable equipment‐level decision making in nuclear facilities at a fraction of conventional monitoring cost.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"19 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144924123","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":"Environmental infringement and sag estimation for power transmission lines with unmanned aerial vehicles and multi-modal sensors","authors":"Siheon Jeong,&nbsp;Munsu Jeon,&nbsp;Jae-Kyeong Lee,&nbsp;Ki-Yong Oh","doi":"10.1111/mice.70060","DOIUrl":"10.1111/mice.70060","url":null,"abstract":"<p>This paper proposes an advanced unmanned aerial vehicle-based framework that integrates three-dimensional LiDAR and infrared camera measurements for environmental infringement assessment. The proposed framework features three characteristics. First, multi-sensor measurements are fused for environmental infringement. This approach provides useful information to detect vegetation encroachment and other hazards. Second, an extreme operational temperature is estimated by addressing the sag and estimated temperature theoretical formulation for transmission lines (TLs). This information quantifies the effect of abnormal thermal conditions on TL tension behavior. Third, uncertainty quantification is incorporated by analyzing sensor inaccuracies and repeatability errors of embedded algorithms. This characteristic enhances the reliability of the environmental infringement assessment. Each characteristic is underpinned by specific achievements, for example, real-time sensor fusion and on-board processing, dual-stage temperature modeling and critical-temperature detection, and combined uncertainty analysis with multi-site validation. Extensive field experiments conducted on multiple TLs demonstrate the effectiveness of the proposed framework and confirm that it would be effective for TL health inspection via environmental infringement and extreme operational temperature estimation under various conditions.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 25","pages":"4342-4363"},"PeriodicalIF":9.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70060","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144928068","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 data augmentation method for pavement crack detection based on super-resolution and denoising diffusion probabilistic models","authors":"Hui Yao,&nbsp;Yanhao Liu,&nbsp;Svetlana Besklubova,&nbsp;Ioannis Brilakis,&nbsp;Meng Guo,&nbsp;Jin Wang,&nbsp;Min Wang","doi":"10.1111/mice.70050","DOIUrl":"10.1111/mice.70050","url":null,"abstract":"<p>Automated detection of pavement cracks is a task of wide interest. With the improvement of industrialization, high-resolution (HR) images are increasingly favored by researchers due to their ability to provide rich information about pavements and diseases. However, the acquisition of effective training data is not easy, which affects the accuracy and robustness of the detection model. Although the recently emerged denoising diffusion probabilistic model (DDPM) overcomes the inherent pattern collapse problem of generative adversarial networks and is capable of generating more diverse and realistic pavement data, its high sampling cost hinders the generation of HR images with rich texture information. To overcome this limitation, this paper proposed a low-cost, two-step data augmentation method that combines DDPM with super-resolution. The method first generated small-sized pavement crack images using DDPM and then enhanced resolution and texture details using an improved SwinIR model. The resulting HR and diverse crack images were used to augment the dataset. The effectiveness of the proposed method was evaluated using four state-of-the-art object detection models. Experimental results showed that all models trained with the augmented training dataset exhibited better performance. Furthermore, when combined with geometric transformation techniques, the proposed method was able to improve the crack detection accuracy by up to approximately 12%.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 25","pages":"4212-4225"},"PeriodicalIF":9.1,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70050","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144928069","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 22","authors":"","doi":"10.1111/mice.70057","DOIUrl":"https://doi.org/10.1111/mice.70057","url":null,"abstract":"<p><b>The cover image</b> is based on the article <i>Hybrid physics-informed neural network with parametric identification for modeling bridge temperature distribution</i> by Yanjia Wang et al., https://doi.org/10.1111/mice.13436.\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 22","pages":""},"PeriodicalIF":9.1,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70057","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144918838","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 22","authors":"","doi":"10.1111/mice.70058","DOIUrl":"https://doi.org/10.1111/mice.70058","url":null,"abstract":"<p><b>The cover image</b> is based on the article <i>Sensitivity of inflow turbulence uncertainty on wind pressure on high-rise buildings using large eddy simulations</i> by L. W. Chew et al., https://doi.org/10.1111/mice.70016.\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 22","pages":""},"PeriodicalIF":9.1,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70058","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144918839","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":"Leveraging national datasets for systematic socio-physical coastal flood risk assessment at the community level","authors":"Ahmed A. Ewis,&nbsp;Omar Nofal","doi":"10.1111/mice.70052","DOIUrl":"10.1111/mice.70052","url":null,"abstract":"<p>The increasing frequency and intensity of hurricanes, coupled with rapid urban development in Florida, have heightened the vulnerability of coastal areas, leading to significant social and economic challenges and prolonged recovery periods. While numerous techniques have been used to evaluate the effects of hurricane-driven storm surges along the coast, limited research has focused on developing a generalized hurricane risk analysis framework that integrates readily available national datasets with detailed information from local communities. In this paper, a novel approach has been developed to generalize the coastal flood risk analysis at the national level by leveraging the National Structure Inventory developed by the Army Corps of Engineers; the Sea, Lake, and Overland Surges from Hurricanes coastal flood hazard model developed by the National Oceanic and Atmospheric Administration; Climate Risk &amp; Resilience climate change data provided by Argonne National Laboratory; and fragility-based flood vulnerability functions. All these data have been used to develop a community-level coastal flood risk assessment approach using three main algorithms in order to be a systematic and adaptable procedure. Although Miami-Dade County was utilized as an example to highlight the established approach's applicability, the suggested technique is generally applicable and can be implemented in any coastal region. The analysis results are in terms of exceedance probabilities of a set of prescribed damage states, percentage of monetary loss, and potential population dislocation. This method is considered as the initial step for a generalized community resilience evaluation against flood hazards. This can assist policymakers and stakeholders to make better risk- and resilience-informed decisions.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 25","pages":"4132-4148"},"PeriodicalIF":9.1,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144905931","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":"Novel architecture based on dual-view fusion for underground hidden distresses detection","authors":"Lili Pei,&nbsp;Danni Mao,&nbsp;Jiangang Ding,&nbsp;Qinrui Tang,&nbsp;Di Wang,&nbsp;Daijie He","doi":"10.1111/mice.70053","DOIUrl":"10.1111/mice.70053","url":null,"abstract":"<p>The detection of underground hidden distresses aims to locate the anomaly areas beneath the surface. This task faces significant challenges, including the scarcity of distress samples and the complex three-dimensional hidden structures of the distresses. Traditional approaches typically employ generative adversarial networks and manually designed object detectors to address these issues. Nonetheless, the current methods face challenges in maintaining semantic consistency between the generated samples and the real-world samples, and they can only detect anomalies based on features from a single image. To overcome these limitations, this paper proposes an innovative detection architecture that significantly enhances the performance of multi-object hidden distress detection by introducing a dual-view (horizontal and longitudinal) correlated generation model and a dual-stream detection mechanism. The approach offers a comprehensive subsurface analysis: The horizontal view captures large-scale anomalies, while the longitudinal view reveals vertical structural details, boosting multi-object distress detection. Specifically, a ground-penetrating radar image diffusion model (GPRDiff) is proposed to generate hidden distress images with dual-view correlation. Furthermore, this study designs a novel dual-view cross-information fusion transformer to achieve efficient fusion of dual-stream information. Experimental results demonstrate that using a combination of GPRDiff-generated images and real images as input, along with a joint-view guided dual-stream detector, significantly improves the detection accuracy of multi-object hidden distresses. This research not only fills the technical gap in multi-object generation within the field of 3D radar road detection but also provides new research insights and technical pathways for other detection industries.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 25","pages":"4253-4272"},"PeriodicalIF":9.1,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144905934","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 21","authors":"","doi":"10.1111/mice.70048","DOIUrl":"https://doi.org/10.1111/mice.70048","url":null,"abstract":"<p><b>The cover image</b> is based on the article <i>Integrating a mortar model into discrete element simulation for enhanced understanding of asphalt mixture cracking</i> by Gyalwang Dhundup et al., https://doi.org/10.1111/mice.13425.\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 21","pages":""},"PeriodicalIF":9.1,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.70048","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144892486","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}