Computer-Aided Civil and Infrastructure Engineering最新文献

{"title":"Cost‐effective excavator pose reconstruction with physical constraints","authors":"Zongwei Yao, Chen Chen, Hongpeng Jin, Hongpu Huang, Xuefei Li, Qiushi Bi","doi":"10.1111/mice.13515","DOIUrl":"https://doi.org/10.1111/mice.13515","url":null,"abstract":"Excavator safety and efficiency are crucial for construction progress. Monitoring their 3D poses is vital but often hampered by resource and accuracy issues with traditional methods. This paper presents a method to reconstruct the 3D poses of excavators using a cost‐effective monocular camera while considering physical constraints. The approach involves two steps: deep learning to identify 2D key points, followed by using excavator kinematic models, coordinate transformation, and camera projection relationships to reconstruct 3D poses with optimization. Experimental results show the method achieves a mean joint position error of 428.58 mm and a mean cylinder length error of 5.12%, outperforming alternative methods. This method can be employed cost‐effectively for safety monitoring and productivity management of excavators on construction sites.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"5 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144192933","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":"Tunnel lining segmentation from ground-penetrating radar images using advanced single- and two-stage object detection and segmentation models","authors":"Byongkyu Bae, Yongjin Choi, Hyunjun Jung, Jaehun Ahn","doi":"10.1111/mice.13528","DOIUrl":"https://doi.org/10.1111/mice.13528","url":null,"abstract":"Recent advances in deep learning have enabled automated ground-penetrating radar (GPR) image analysis, particularly through two-stage models like mask region-based convolutional neural (Mask R-CNN) and single-stage models like you only look once (YOLO), which are two mainstream approaches for object detection and segmentation tasks. Despite their potential, the limited comparative analysis of these methods obscures the optimal model selection for practical field applications in tunnel lining inspection. This study addresses this gap by evaluating the performance of Mask R-CNN and YOLOv8 for tunnel lining detection and segmentation in GPR images. Both models are trained using the labeled GPR image datasets for tunnel lining and evaluate their prediction accuracy and consistency based on the intersection over union (IoU) metric. The results show that Mask R-CNN with ResNeXt backbone achieves superior segmentation accuracy with an average IoU of 0.973, while YOLOv8 attains an IoU of 0.894 with higher variability in prediction accuracy and occasional failures in detection. However, YOLOv8 offers faster processing times in terms of training and inference. It appears Mask R-CNN still excels in accuracy in tunnel GPR lining detection, although recent advancements of the YOLOs often outperform the accuracy of the Mask R-CNN in a few specific tasks. We also show that ResNeXt-enhanced Mask R-CNN further improves the accuracy of the traditional ResNet-based Mask R-CNN. The research finding offers useful insights into the trade-offs between the accuracy, consistency, and computational efficiency of the two mainstream models for the tunnel lining identification task in GPR images. The finding is expected to offer guidance for the future selection and development of optimal deep learning-based inspection models for practical field applications.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"8 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144202330","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":"Signal noise estimation and removal of sub‐mm 3D pavement texture data using 1D residual denoising network","authors":"Guolong Wang, Kelvin C. P. Wang, Guangwei Yang, Joshua Q. Li, Amir Golalipour","doi":"10.1111/mice.13502","DOIUrl":"https://doi.org/10.1111/mice.13502","url":null,"abstract":"Signal noise removal is an indispensable and critical procedure in obtaining clean pavement texture data for reliable pavement evaluation and management. Nevertheless, the presently established denoising approaches to pavement texture data still rely on traditional techniques that have long struggled with removing noise accurately and consistently. This paper innovatively initiates a one‐dimensional (1D) residual denoising network (R1DNet) to achieve the noise removal of 3D pavement texture data. R1DNet is proposed as a 1D architectural encoder–decoder that considers the unique characteristics of 3D texture data from 3D laser imaging technology. The encoder extracts diverse profile features of input noisy texture data through two favorably developed 1D modular structures: a cascade deep convolutional module and a parallel multi‐scale attention module. The decoder gradually parses the extracted profile features and estimates noise, with which the clean texture data are obtained based on a simple subtraction operation. The architecture of R1DNet is determined to be optimal in both accuracy and efficiency, using a customized performance‐balancing evaluation function. For model development in a supervised manner, a systematic labeling method is specifically developed, which can build the baseline clean texture data from real 0.1 mm noisy 3D texture data. The experimental results show that the proposed R1DNet can effectively eliminate noise and produce clean texture data closely matching the baseline, presenting significant improvements in accuracy and consistency, compared to the traditional denoising methods.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"13 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144188899","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":"End‐to‐end frequency enhancement framework for GPR images using domain‐adaptive generative adversarial networks","authors":"Hancheng Zhang, Yuanyuan Hu, Qiang Wang, Zhendong Qian, Pengfei Liu","doi":"10.1111/mice.13525","DOIUrl":"https://doi.org/10.1111/mice.13525","url":null,"abstract":"Ground‐penetrating radar (GPR) offers nondestructive subsurface imaging but suffers from a trade‐off between frequency and penetration depth: High frequencies yield better resolution with limited depth, while low frequencies penetrate deeper with reduced detail. This paper introduces a novel frequency enhancement method for GPR images using domain‐adaptive generative adversarial networks. The proposed end‐to‐end framework integrates a Domain Adaptation Module (DAM) and a Frequency Enhancement Module (FEM) to address frequency‐resolution trade‐offs and domain discrepancies. The DAM aligns simulated and real low‐frequency GPR data, enabling effective frequency enhancement by the FEM. Due to inherent differences in signal characteristics between simulated and real‐world GPR data, directly applying models trained on simulated data to real‐world scenarios often results in performance degradation and loss of physical consistency, making domain adaptation essential for bridging this gap. By reducing domain discrepancies and ensuring feature consistency, the framework generates high‐frequency GPR images with enhanced clarity and detail. Extensive experiments show that the method significantly improves image quality, target detection, and localization accuracy, outperforming state‐of‐the‐art approaches and demonstrating strong potential for subsurface imaging applications.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"49 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144176883","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":"Early detection and location of unexpected events in buried pipelines under unseen conditions using the two‐stream global fusion classifier model","authors":"Sun‐Ho Lee, Choon‐Su Park, Dong‐Jin Yoon","doi":"10.1111/mice.13507","DOIUrl":"https://doi.org/10.1111/mice.13507","url":null,"abstract":"Failure of buried pipelines can result in serious impacts, such as explosions, environmental contamination, and economic losses. Early detection and location of unexpected events is crucial to prevent such events. However, conventional monitoring methods exhibit limited generalization performance under varying environmental and operational conditions. Furthermore, the cross‐correlation‐based time difference of arrival approach, which is widely used for source localization, also lacks the capability to identify anomalous events. This study introduces what is termed as the two‐stream global fusion classifier (TSGFC), a novel multitask deep‐learning model designed to early detection and location of unexpected events in buried pipelines, even under previously unseen conditions. TSGFC combines spatial and temporal features from accelerometer data using a global fusion mechanism, and uniquely performs both event classification and source localization through a unified multitask framework. To ensure generalization across diverse environments, we employed a unique data acquisition strategy that was specifically designed to evaluate the model's performance under domain shift by using training data from controlled experiments and test data from real‐world excavation activities conducted on a completely different pipeline. Our results confirm that TSGFC can identify unexpected excavation activity with 95.45% accuracy and minimal false alarms, even when evaluated on data collected from a completely different buried pipeline under real‐world excavation scenarios unseen during training.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"57 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144176884","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":"A surface electromyography–based deep learning model for guiding semi‐autonomous drones in road infrastructure inspection","authors":"Yu Li, David Zhang, Penghao Dong, Shanshan Yao, Ruwen Qin","doi":"10.1111/mice.13520","DOIUrl":"https://doi.org/10.1111/mice.13520","url":null,"abstract":"While semi‐autonomous drones are increasingly used for road infrastructure inspection, their insufficient ability to independently handle complex scenarios beyond initial job planning hinders their full potential. To address this, the paper proposes a human–drone collaborative inspection approach leveraging flexible surface electromyography (sEMG) for conveying inspectors' speech guidance to intelligent drones. Specifically, this paper contributes a new data set, sEMG Commands for Piloting Drones (sCPD), and an sEMG‐based Cross‐subject Classification Network (sXCNet), for both command keyword recognition and inspector identification. sXCNet acquires the desired functions and performance through a synergetic effort of sEMG signal processing, spatial‐temporal‐frequency deep feature extraction, and multitasking‐enabled cross‐subject representation learning. The cross‐subject design permits deploying one unified model across all authorized inspectors, eliminating the need for subject‐dependent models tailored to individual users. sXCNet achieves notable classification accuracies of 98.1% on the sCPD data set and 86.1% on the public Ninapro db1 data set, demonstrating strong potential for advancing sEMG‐enabled human–drone collaboration in road infrastructure inspection.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"1 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144165421","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":"Deep learning for computer vision in pulse‐like ground motion identification","authors":"Lu Han, Zhengru Tao","doi":"10.1111/mice.13521","DOIUrl":"https://doi.org/10.1111/mice.13521","url":null,"abstract":"Near‐fault pulse‐like ground motions can cause severe damage to long‐period engineering structures. A rapid and accurate identification method is essential for seismic design. Deep learning offers a solution by framing pulse‐like motion identification as an image classification task. However, the application of deep learning models faces multiple challenges from data and models for pulse‐like motion classification. This study focuses on suitable input images and model architecture optimization through a comprehensive strategy. The diverse datasets are realized by transferring the original time history into Morlet wavelet time‐frequency diagram, anomaly‐marked velocity time history, Fourier amplitude spectrum and its smoothed diagram, and pixel fusion diagrams. Two types of deep learning models are constructed in the image classification task for these datasets. A convolutional neural network (CNN) is enhanced by integrating the self‐attention mechanism (SAM) to concentrate on local image features. Additionally, a seismic parameter layer is added to this enhanced model to reduce reliance on input data features. Visual Transformers, including Vision Transformer (ViT) and Swin Transformer (SwinT), are adopted in this task as well. The results of the enhanced CNN demonstrate that TF outperforms other images with higher classification accuracy and convergence speed, and dual‐input image presents inferior performance. The accuracy of all input datasets under the constraint of a single‐parameter moment magnitude (<jats:italic>M</jats:italic><jats:sub>w</jats:sub>) is higher than that under the constraint of rupture distance (<jats:italic>R</jats:italic><jats:sub>rup</jats:sub>). The accuracy under the two‐parameter constraint of <jats:italic>M</jats:italic><jats:sub>w</jats:sub> and <jats:italic>R</jats:italic><jats:sub>rup</jats:sub> is higher than that of the single parameter constraint for all input datasets, in which the accuracy from TF is the highest, and that from dual‐input data is improved. The performance of SwinT is similar to CNN+SAM and better than ViT for single‐input images, in which TF presents the highest accuracy. For dual‐input images, ViT is better than SwinT, and both of them are better than CNN+SAM. In a resource‐limited environment, the enhanced CNN with single‐input TF is the best strategy, and the physical constraint of <jats:italic>M</jats:italic><jats:sub>w</jats:sub> and <jats:italic>R</jats:italic><jats:sub>rup</jats:sub> is more effective, especially for the dual‐input images.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"50 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144165420","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":"Learning error distribution kernel‐enhanced neural network methodology for multi‐intersection signal control optimization","authors":"H. Wang, Y. Wang, W. Li, A. B. Subramaniyan, G. Zhang","doi":"10.1111/mice.13522","DOIUrl":"https://doi.org/10.1111/mice.13522","url":null,"abstract":"Traffic congestion has substantially induced significant mobility and energy inefficiency. Many research challenges are identified in traffic signal control and management associated with artificial intelligence (AI)‐based models. For example, developing AI‐driven dynamic traffic system models that accurately capture high‐resolution traffic attributes and formulate robust control algorithms for traffic signal optimization is difficult. Additionally, uncertainties in traffic system modeling and control processes can further complicate traffic signal system controllability. To partially address these challenges, this study presents a novel, hybrid neural network model enhanced with a probability density function kernel shaping technique to formulate traffic system dynamics better and improve comprehensive traffic network modeling and control. The numerical experimental tests were conducted, and the results demonstrate that the proposed control approach outperforms the baseline control strategies and reduces overall average delays by 11.64% on average. By leveraging the capabilities of this innovative model, this study aims to address major challenges related to traffic congestion and energy inefficiency toward more effective and adaptable AI‐based traffic control systems.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"24 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144165423","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":"Machine learning models for predicting the International Roughness Index of asphalt concrete overlays on Portland cement concrete pavements","authors":"K. Kwon, Y. Yeom, Y. J. Shin, A. Bae, H. Choi","doi":"10.1111/mice.13524","DOIUrl":"https://doi.org/10.1111/mice.13524","url":null,"abstract":"Although estimating the International Roughness Index (IRI) is crucial, previous studies have faced challenges in addressing IRI prediction for asphalt concrete (AC) overlays on Portland cement concrete (PCC) pavements. This study introduces machine learning to predict the IRI of AC overlays on PCC pavements, focusing on incorporating pre‐overlay treatments to reflect their composite characteristics. These treatments are categorized into concrete pavement restoration (CPR) and fracturing methods. The developed models outperformed conventional approaches by effectively capturing the impact of these pre‐overlay treatments, as evidenced by the distinct differences in their contributions to IRI predictions between the CPR and fracturing methods. Additionally, the types and occurrences of pavement distresses varied depending on the pre‐overlay treatments applied. When separate IRI prediction models were developed for each treatment group, they demonstrated improved performance, compared to the original model that combined all treatments. This demonstrates the significance of individualized modeling based on specific pre‐overlay treatment types.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"9 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144165424","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":"Adaptive feature expansion and fusion model for precast component segmentation","authors":"Ka‐Veng Yuen, Guanting Ye","doi":"10.1111/mice.13523","DOIUrl":"https://doi.org/10.1111/mice.13523","url":null,"abstract":"The assembly and production of sandwich panels for prefabricated components is crucial for the safety of modular construction. Although computer vision has been widely applied in production quality and safety monitoring, the large‐scale differences among components and numerous background interference factors in sandwich panel prefabricated components pose substantial challenges. Therefore, maintaining the model recognition accuracy remains a big challenge in practical circumstances. This paper presents an instance segmentation model, namely adaptive feature expansion and fusion (AFFS). The proposed model includes a dynamic feature aggregation mechanism and possesses a flattened network architecture, enabling efficient feature processing and precise instance segmentation. Moreover, AFFS supports rapid adaptation to newly added data or component categories by updating only the feature extraction layers. Comprehensive experimental evaluations demonstrate that the proposed AFFS achieves outstanding recognition accuracy (mAP<jats:sub>50</jats:sub> reaching 95.8% and mAP<jats:sub>min</jats:sub> reaching 99.9%), significantly outperforming several state‐of‐the‐art instance segmentation networks, including You Only Look Once (YOLO), Segmenting Objects by Locations v2 (SOLOv2), and Cascade Mask Region‐based Convolutional Neural Network (Cascade Mask R‐CNN).","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"23 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144136740","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}