Computer-Aided Civil and Infrastructure Engineering最新文献

{"title":"Sewer image super-resolution with depth priors and its lightweight network","authors":"Gang Pan, Chen Wang, Zhijie Sui, Shuai Guo, Yaozhi Lv, Honglie Li, Di Sun, Zixia Xia","doi":"10.1111/mice.13453","DOIUrl":"https://doi.org/10.1111/mice.13453","url":null,"abstract":"The quick-view (QV) technique serves as a primary method for detecting defects within sewerage systems. However, the effectiveness of QV is impeded by the limited visual range of its hardware, resulting in suboptimal image quality for distant portions of the sewer network. Image super-resolution is an effective way to improve image quality and has been applied in a variety of scenes. However, research on super-resolution for sewer images remains considerably unexplored. In response, this study leverages the inherent depth relationships present within QV images and introduces a novel Depth-guided, Reference-based Super-Resolution framework denoted as DSRNet. It comprises two core components: a depth extraction module and a depth information matching module (DMM). DSRNet utilizes the adjacent frames of the low-resolution image as reference images and helps them recover texture information based on the correlation. By combining these modules, the integration of depth priors significantly enhances both visual quality and performance benchmarks. Besides, in pursuit of computational efficiency and compactness, a super-resolution knowledge distillation model based on an attention mechanism is introduced. This mechanism facilitates the acquisition of feature similarity between a more complex teacher model and a streamlined student model, with the latter being a lightweight version of DSRNet. Experimental results demonstrate that DSRNet significantly improves peak signal-to-noise ratio (PSNR) and and Structural Similarity index (SSIM) compared with other methods. This study also conducts experiments on sewer defect semantic segmentation, object detection, and classification on the Pipe data set and Sewer-ML data set. Experiments show that the method can improve the performance of low-resolution sewer images in these tasks.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"48 9 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575368","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":"Automated indoor 3D scene reconstruction with decoupled mapping using quadruped robot and LiDAR sensor","authors":"Vincent J. L. Gan, Difeng Hu, Yushuo Wang, Ruoming Zhai","doi":"10.1111/mice.13450","DOIUrl":"https://doi.org/10.1111/mice.13450","url":null,"abstract":"Advancements in automated 3D scene reconstruction are essential for accurately capturing and documenting the current state of buildings and infrastructure. Traditional 3D reconstruction relies on laser scanning to obtain as-built conditions, but this process is often labor-intensive and time-consuming. This study introduces an optimization algorithm incorporating methods for viewpoint generation, occlusion detection and culling, and robot-moving trajectory identification. Additionally, the research investigates 3D reconstruction methods, comparing coupled and decoupled approaches to identify the most practical configuration for robotic scanning. Automation strategies for collision avoidance in human-centric environments are also explored, with adaptive control methods tested and validated for efficient point cloud data capture in indoor environments. This research advances the state-of-the-art in robotic scanning by providing a more precise and adaptive framework for 3D scene reconstruction. The results demonstrate the effectiveness of the proposed method in achieving high scan completeness and sufficient density in point cloud data, offering solutions for efficient robotic scanning.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"18 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143546037","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":"Development of a portable device for structural visual inspection","authors":"Jongbin Won,&nbsp;Minhyuk Song,&nbsp;Jongwoong Park","doi":"10.1111/mice.13399","DOIUrl":"10.1111/mice.13399","url":null,"abstract":"<p>Visual inspection is crucial for the maintenance of built infrastructures, facilitating early detection and quantification of damage. Traditional manual methods, however, often require inspectors to access dangerous or inaccessible areas, posing significant safety risks and inefficiencies. In response to these challenges, this paper introduces a portable visual inspection device (VID) integrated with three laser distance meters and a high-resolution camera. The VID enhances the efficiency of visual inspection by incorporating methods that accurately estimate the camera's pose relative to the target surface and determine a scale factor for precise damage quantification. The proposed methods were validated through experimental validations, demonstrating their precision and effectiveness. In lab-scale validation, the angle estimation showed accuracy with less than 3 degrees of error, and the scale factor estimation method showed discrepancies of less than 1 mm, even when the observation angle exceeded 20 degrees. Subsequent field experiments confirmed the VID's capability to detect and measure microcracks as narrow as 0.1 mm. Furthermore, the device successfully quantified non-crack damage with an error margin of 1.84%, even at challenging angles exceeding 45 degrees.</p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 8","pages":"1061-1079"},"PeriodicalIF":8.5,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13399","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143532579","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":"Crack segmentation-guided measurement with lightweight distillation network on edge device","authors":"Jianqi Zhang, Ling Ding, Wei Wang, Hainian Wang, Ioannis Brilakis, Diana Davletshina, Rauno Heikkilä, Xu Yang","doi":"10.1111/mice.13446","DOIUrl":"https://doi.org/10.1111/mice.13446","url":null,"abstract":"Pavement crack measurement (PCM) is essential for automated, precise road condition assessment. However, balancing speed and accuracy on edge artificial intelligence (AI) mobile devices remains challenging. This paper proposes a real-time PCM framework for edge deployment, incorporating a lightweight distillation network and a surface feature measurement algorithm. Specifically, the proposed instance-aware hybrid distillation module combines feature-based and relation-based knowledge distillation, leveraging crack instance-related information for efficient knowledge transfer from teacher to student networks, which results in a more accurate and lightweight segmentation model. Additionally, a real-time crack surface feature measurement algorithm, based on distance mapping relationships and crack edge coordinate extraction, addresses issues with crack edge branching and loss, enhancing measurement efficiency. Real-time measurement was performed on actual roads utilizing mobile robot equipped with an edge computing unit. The crack segmentation precision reached 84.37%, with a frame per second of 77.72. Compared to the ground truth, the relative error for average crack width ranged from 6.42% to 40.65%, while the relative error for crack length varied between 1.48% and 3.76%. These findings highlight the feasibility of real-time crack assessment and save road maintenance costs.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"46 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143539331","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":"Generative adversarial network based on domain adaptation for crack segmentation in shadow environments","authors":"Yingchao Zhang, Cheng Liu","doi":"10.1111/mice.13451","DOIUrl":"https://doi.org/10.1111/mice.13451","url":null,"abstract":"Precision segmentation of cracks is important in industrial non-destructive testing, but the presence of shadows in the actual environment can interfere with the segmentation results of cracks. To solve this problem, this study proposes a two-stage domain adaptation framework called GAN-DANet for crack segmentation in shadowed environments. In the first stage, CrackGAN uses adversarial learning to merge features from shadow-free and shadowed datasets, creating a new dataset with more domain-invariant features. In the second stage, the CrackSeg network innovatively integrates enhanced Laplacian filtering (ELF) into high-resolution net to enhance crack edges and texture features while filtering out shadow information. In this model, CrackGAN addresses domain shift by generating a new dataset with domain-invariant features, avoiding direct feature alignment between source and target domains. The ELF module in CrackSeg effectively enhances crack features and suppresses shadow interference, improving the segmentation model's robustness in shadowed environments. Experiments show that GAN-DANet improves the crack segmentation accuracy, with the mean intersection over union value increasing from 57.87 to 75.03, which surpasses the performance of existing state-of-the-art domain adaptation algorithms.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"2 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143532414","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 7","authors":"","doi":"10.1111/mice.13449","DOIUrl":"https://doi.org/10.1111/mice.13449","url":null,"abstract":"<p><b>The cover image</b> is based on the article <i>Multifidelity graph neural networks for efficient and accurate mesh-based partial differential equations surrogate modeling</i> by Negin Alemazkoor et al., https://doi.org/10.1111/mice.13312.\u0000\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure>\u0000 </p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"40 7","pages":""},"PeriodicalIF":8.5,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13449","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527737","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":"Multivariate engineering formulas discovery with knowledge-based neural network","authors":"Pei-Yao Chen, Chen Wang, Jian-Sheng Fan","doi":"10.1111/mice.13448","DOIUrl":"https://doi.org/10.1111/mice.13448","url":null,"abstract":"Multivariate engineering formulas are the foundation of various engineering standards worldwide for constructing complex systems. Traditional formula discovery methods suffer from low efficiency, the curse of dimensionality, and low physical interpretability. To address these limitations, this study proposes a knowledge-based method for efficiently generating multivariate engineering formulas directly from data. The method consists of four components: (1) a deep generative model considering dimensional homogeneity, (2) a physics-adaptive normalization method for multiple engineering variables with different units, (3) a feature merging algorithm grounded in dimensionality theory, and (4) a machine learning-based data segmentation method for piecewise formulas. Experiments on two ground-truth datasets demonstrate that our proposed method improves the accuracy of the generated formulas by 35.6% (measured by mean absolute error), compared to the <i>Eureqa</i> program. Additionally, it enhances the mechanistic interpretability of the results, compared to both <i>Eureqa</i> and the emerging physics-informed neural network-based equation discovery methods. The piecewise formulas successfully capture the implicit mechanisms in the experimental data, consistent with theoretical analysis. Overall, our knowledge-based method holds great promise for improving the efficiency of discovering interpretable and generalizable multivariate engineering formulas, facilitating the transformation of new techniques from testing to applications.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"210 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143495838","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":"Underwater bridge pier morphology measurement method via refraction correction and multi-camera calibration","authors":"Tao Wu, Shitong Hou, Zhishen Wu, Wen Xiong, Jian Zhang, Xinxing Shao, Xiaoyuan He, Gang Wu","doi":"10.1111/mice.13440","DOIUrl":"https://doi.org/10.1111/mice.13440","url":null,"abstract":"Underwater structural inspection is essential for ensuring the safety and longevity of bridges. To improve the efficiency and accuracy of these inspections, this paper presents a method for measuring the morphology of bridge piers through refraction correction and multi-camera calibration. Using an underwater visual inspection platform with appropriate lighting, the measurement equipment mitigates low visibility challenges. A coplanar camera refraction parameter calibration method based on encoded markers is proposed to reduce the effects of refraction, along with the development of a multi-refraction correction model. Additionally, a novel multi-camera extrinsic calibration method is introduced to stitch point clouds. A comparative analysis of the two extrinsic calibration methods, conducted both in air and underwater, has been performed to validate the accuracy and efficiency of the proposed approach. Finally, the circular cross-section shape of the underwater bridge pier was successfully measured, and the results of defect localization were effectively presented.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"50 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143470502","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":"Multi-hazard probabilistic risk assessment and equitable multi-objective optimization of building retrofit strategies in hurricane-vulnerable communities","authors":"Abdullah M. Braik, Himadri Sen Gupta, Maria Koliou, Andrés D. González","doi":"10.1111/mice.13445","DOIUrl":"https://doi.org/10.1111/mice.13445","url":null,"abstract":"Coastal communities are increasingly vulnerable to hurricanes, which cause billions of dollars in damage annually through wind, storm surge, and flooding. Mitigation efforts are essential to reduce these impacts but face significant challenges, including uncertainties in hazard prediction, damage estimation, and recovery costs. Resource constraints and the disproportionate burden borne by socioeconomically vulnerable groups further complicate retrofitting strategies. This study presents a probabilistic methodology to assess and mitigate hurricane risks by integrating hazard analysis, building fragility, and economic loss assessment. The methodology prioritizes retrofitting strategies using a risk-informed, equity-focused approach. Multi-objective optimization balances cost-effectiveness and risk reduction while promoting fair resource allocation among socioeconomic groups. The novelty of this study lies in its direct integration of equity as an objective in resource allocation through multi-objective optimization, its comprehensive consideration of multi-hazard risks, its inclusion of both direct and indirect losses in cost assessments, and its use of probabilistic hazard analysis to incorporate varying time horizons. A case study of the Galveston testbed demonstrates the methodology's potential to minimize damage and foster equitable resilience. Analysis of budget scenarios and trade-offs between cost and equity underscores the importance of comprehensive loss assessments and equity considerations in mitigation and resilience planning. Key findings highlight the varied effectiveness of retrofitting strategies across different budgets and time horizons, the necessity of addressing both direct and indirect losses, and the importance of multi-hazard considerations for accurate risk assessments. Multi-objective optimization underscores that equitable solutions are achievable even under constrained budgets. Beyond a certain point, achieving equity does not necessarily increase expected losses, demonstrating that more equitable solutions can be implemented without compromising overall cost-effectiveness.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"12 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143463166","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":"Hybrid deep learning model for predicting failure properties of asphalt binder from fracture surface images","authors":"Babak Asadi, Viraj Shah, Abhilash Vyas, Mani Golparvar-Fard, Ramez Hajj","doi":"10.1111/mice.13447","DOIUrl":"https://doi.org/10.1111/mice.13447","url":null,"abstract":"Cracking impacts asphalt concrete durability primarily due to cohesive asphalt binder failures. The poker chip test has recently been introduced to better characterize the cracking potential of asphalt binders by fracturing a specimen in a realistic stress state to a thin binder film. However, broader adoption faces challenges due to high instrumentation costs for measuring load and displacement. This paper presents and validates a deep learning model that predicts ductility and tensile strength from posttest images of fractured binder surfaces, with potential extensions to simplified instrumentation. The hybrid model, named PCNet, integrates a custom lightweight convolutional neural network (CNN) developed to capture local features (e.g., edges, boundaries, contours) within fracture cavities with a Swin Transformer that models global contextual dependencies. A bidirectional cross-attention fusion module is designed to facilitate mutual information exchange between CNN and transformer branches. The fused features are then processed by a fully connected network (FCN) to predict indices derived from the test. The proposed model demonstrates high predictive accuracy across a range of binders and test configurations, achieving an &lt;span data-altimg=\"/cms/asset/e9fbf493-59db-40fa-93fd-5f973187445b/mice13447-math-0001.png\"&gt;&lt;/span&gt;&lt;mjx-container ctxtmenu_counter=\"150\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"&gt;&lt;mjx-math aria-hidden=\"true\" location=\"graphic/mice13447-math-0001.png\"&gt;&lt;mjx-semantics&gt;&lt;mjx-msup data-semantic-children=\"0,1\" data-semantic- data-semantic-role=\"latinletter\" data-semantic-speech=\"upper R squared\" data-semantic-type=\"superscript\"&gt;&lt;mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mi&gt;&lt;mjx-script style=\"vertical-align: 0.363em;\"&gt;&lt;mjx-mn data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"integer\" data-semantic-type=\"number\" size=\"s\"&gt;&lt;mjx-c&gt;&lt;/mjx-c&gt;&lt;/mjx-mn&gt;&lt;/mjx-script&gt;&lt;/mjx-msup&gt;&lt;/mjx-semantics&gt;&lt;/mjx-math&gt;&lt;mjx-assistive-mml display=\"inline\" unselectable=\"on\"&gt;&lt;math altimg=\"urn:x-wiley:10939687:media:mice13447:mice13447-math-0001\" display=\"inline\" location=\"graphic/mice13447-math-0001.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"&gt;&lt;semantics&gt;&lt;msup data-semantic-=\"\" data-semantic-children=\"0,1\" data-semantic-role=\"latinletter\" data-semantic-speech=\"upper R squared\" data-semantic-type=\"superscript\"&gt;&lt;mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic-parent=\"2\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"&gt;R&lt;/mi&gt;&lt;mn data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic-parent=\"2\" data-semantic-rol","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"22 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143463167","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}