Automation in Construction最新文献

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Spectral Jump Anomaly Detection: Temperature-compensated algorithm for structural damage detection using vibration data

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-02-08 DOI: 10.1016/j.autcon.2025.106031

Giulio Mariniello, Tommaso Pastore, Domenico Asprone

Assessing the integrity of structural systems throughout their aging process has capital importance in infrastructure management. Monitoring these infrastructures presents challenges in distinguishing early damage from slight variations in the structural behavior caused by environmental or operational variability.

This paper introduces the Spectral Jump Anomaly Detection (SJ-AD) algorithm, a data-driven method designed to identify minor structural damage using acceleration collected under considerable environmental variability. SJ-AD focuses on anomalies in the distribution of a distance measure, the minimum jump cost, calculated between power spectra. The method effectively identifies issues in the KW-51 bridge, even with minimal structural defects and varying temperatures. Additionally, numerical experiments show that SJ-AD can detect low damping variations in noisy conditions, demonstrating robustness against minor frequency changes. Its flexible approach and sensitivity to small damages make SJ-AD a promising solution for proactive maintenance and risk management in various structural systems.

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引用次数: 0

Indoor visual positioning using stationary semantic distribution registration and building information modeling

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-02-08 DOI: 10.1016/j.autcon.2025.106033

Xiaoping Zhou , Yukang Wang , Jichao Zhao , Maozu Guo

Indoor Visual Positioning (IVP) is a prerequisite for applications like indoor location-based services in smart buildings. Building Information Modeling (BIM), representing physical and functional characteristics of buildings, is widely used in IVP. Existing BIM-based IVP methods register visual features from sensed images to BIM but suffer inaccuracies caused by dramatic disturbances from unstable objects like chairs. Stationary objects like walls may address this issue and provide a more reliable IVP scheme, yet it remains to be explored. This paper proposes an IVP scheme leveraging stationary object registration from sequential images to BIM, termed Stationary Semantic Distribution-driven Visual Positioning (S2VP). In the offline phase, S2VP generates “stationary semantic distribution-positions” datasets from BIM. During positioning, the stationary semantic distribution of sensed images is first estimated, and the indoor position is computed via a particle filter model. Experiments show that S2VP achieves an average positioning error of 0.37 m, outperforming existing methods.

{"title":"Indoor visual positioning using stationary semantic distribution registration and building information modeling","authors":"Xiaoping Zhou , Yukang Wang , Jichao Zhao , Maozu Guo","doi":"10.1016/j.autcon.2025.106033","DOIUrl":"10.1016/j.autcon.2025.106033","url":null,"abstract":"<div><div>Indoor Visual Positioning (IVP) is a prerequisite for applications like indoor location-based services in smart buildings. Building Information Modeling (BIM), representing physical and functional characteristics of buildings, is widely used in IVP. Existing BIM-based IVP methods register visual features from sensed images to BIM but suffer inaccuracies caused by dramatic disturbances from unstable objects like chairs. Stationary objects like walls may address this issue and provide a more reliable IVP scheme, yet it remains to be explored. This paper proposes an IVP scheme leveraging stationary object registration from sequential images to BIM, termed Stationary Semantic Distribution-driven Visual Positioning (S2VP). In the offline phase, S2VP generates “stationary semantic distribution-positions” datasets from BIM. During positioning, the stationary semantic distribution of sensed images is first estimated, and the indoor position is computed via a particle filter model. Experiments show that S2VP achieves an average positioning error of 0.37 m, outperforming existing methods.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"172 ","pages":"Article 106033"},"PeriodicalIF":9.6,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143369764","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}

引用次数: 0

Compaction test of rolled rockfill material using multimodal Rayleigh wave dispersion inversion

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-02-07 DOI: 10.1016/j.autcon.2025.106043

Yao Wang , Hai Liu , Xu Meng , Guiquan Yuan , Huiguo Wang , Ruige Shi , Mengxiong Tang , Billie F. Spencer

This paper investigated the potential of Rayleigh wave multimodal dispersion inversion to advance automatic construction through real-time, in-situ measurement of rockfill compaction. An acquisition system and inversion method were developed to automate the process of obtaining compaction depth profiles and implemented during a dynamic rolling test. A rockfill layer under 2 m was tested, with Rayleigh wave data collected after different compaction passes. Multi-mode dispersion inversion was used to analyze the material's velocity structure. The results show that multimodal dispersion curves accurately reflect changes in compaction. As compaction increased, the velocity structure transitioned from a complex layered to a uniform single-layered form, with a corresponding rise in the elastic modulus. Furthermore, the calculated Young's modulus exhibited a strong positive correlation with dry density measured by excavation tests. These findings offer an approach for intelligent compaction techniques, contributing to the automation of in-situ compaction monitoring in rockfill construction.

{"title":"Compaction test of rolled rockfill material using multimodal Rayleigh wave dispersion inversion","authors":"Yao Wang , Hai Liu , Xu Meng , Guiquan Yuan , Huiguo Wang , Ruige Shi , Mengxiong Tang , Billie F. Spencer","doi":"10.1016/j.autcon.2025.106043","DOIUrl":"10.1016/j.autcon.2025.106043","url":null,"abstract":"<div><div>This paper investigated the potential of Rayleigh wave multimodal dispersion inversion to advance automatic construction through real-time, in-situ measurement of rockfill compaction. An acquisition system and inversion method were developed to automate the process of obtaining compaction depth profiles and implemented during a dynamic rolling test. A rockfill layer under 2 m was tested, with Rayleigh wave data collected after different compaction passes. Multi-mode dispersion inversion was used to analyze the material's velocity structure. The results show that multimodal dispersion curves accurately reflect changes in compaction. As compaction increased, the velocity structure transitioned from a complex layered to a uniform single-layered form, with a corresponding rise in the elastic modulus. Furthermore, the calculated Young's modulus exhibited a strong positive correlation with dry density measured by excavation tests. These findings offer an approach for intelligent compaction techniques, contributing to the automation of in-situ compaction monitoring in rockfill construction.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"172 ","pages":"Article 106043"},"PeriodicalIF":9.6,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143292210","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}

引用次数: 0

Triple-stage crack detection in stone masonry using YOLO-ensemble, MobileNetV2U-net, and spectral clustering

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-02-07 DOI: 10.1016/j.autcon.2025.106045

Ali Mahmoud Mayya , Nizar Faisal Alkayem

Condition assessment of stone structures is crucial to maintain their durability. To improve the identification of stone cracks, a triple-stage framework for crack detection, segmentation, and decision-support clustering is proposed. The framework starts with an ensemble of state-of-the-art YOLO models to improve crack detection. The detected crack regions are then fed to an enhanced MobileNetV2U-Net for better crack localization. Thereafter, features are extracted from the detected and segmented stone crack regions, and the K-means and Spectral clustering are utilized to categorize crack patterns. Intensive experiments and detailed comparisons are performed to test the proposed approach. Finally, a user-friendly GUI is designed to simplify the complexity of the proposed framework. Results prove that the YOLO ensemble detector and MobileNetV2U-Net model exhibit the best performances based on statistical metrics. Moreover, it is proven that spectral clustering using five clusters applied to the detected-segmented crack patterns is the best-employed scenario.

引用次数: 0

Automated recognition of construction worker activities using multimodal decision-level fusion

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-02-07 DOI: 10.1016/j.autcon.2025.106032

Yue Gong , JoonOh Seo , Kyung-Su Kang , Mengnan Shi

This paper proposes an automated approach for construction worker activity recognition by integrating video and acceleration data, employing a decision-level fusion method that combines classification results from each data modality using the Dempster-Shafer Theory (DS). To address uneven sensor reliability, the Category-wise Weighted Dempster-Shafer (CWDS) approach is further proposed, estimating category-wise weights during training and embedding them into the fusion process. An experimental study with ten participants performing eight construction activities showed that models trained using DS and CWDS outperformed single-modal approaches, achieving accuracies of 91.8% and 95.6%, about 7% and 10% higher than those of vision-based and acceleration-based models, respectively. Category-wise improvements were also observed, indicating that the proposed multimodal fusion approaches result in a more robust and balanced model. These results highlight the effectiveness of integrating vision and accelerometer data through decision-level fusion to reduce uncertainty in multimodal data and leverage the strengths of single sensor-based approaches.

{"title":"Automated recognition of construction worker activities using multimodal decision-level fusion","authors":"Yue Gong , JoonOh Seo , Kyung-Su Kang , Mengnan Shi","doi":"10.1016/j.autcon.2025.106032","DOIUrl":"10.1016/j.autcon.2025.106032","url":null,"abstract":"<div><div>This paper proposes an automated approach for construction worker activity recognition by integrating video and acceleration data, employing a decision-level fusion method that combines classification results from each data modality using the Dempster-Shafer Theory (DS). To address uneven sensor reliability, the Category-wise Weighted Dempster-Shafer (CWDS) approach is further proposed, estimating category-wise weights during training and embedding them into the fusion process. An experimental study with ten participants performing eight construction activities showed that models trained using DS and CWDS outperformed single-modal approaches, achieving accuracies of 91.8% and 95.6%, about 7% and 10% higher than those of vision-based and acceleration-based models, respectively. Category-wise improvements were also observed, indicating that the proposed multimodal fusion approaches result in a more robust and balanced model. These results highlight the effectiveness of integrating vision and accelerometer data through decision-level fusion to reduce uncertainty in multimodal data and leverage the strengths of single sensor-based approaches.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"172 ","pages":"Article 106032"},"PeriodicalIF":9.6,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349418","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}

引用次数: 0

Microcrack investigations of 3D printing concrete using multiple transformer networks

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-02-07 DOI: 10.1016/j.autcon.2025.106017

Hongyu Zhao , Xiangyu Wang , Zhaohui Chen , Xianda Liu , Yufei Wang , Jun Wang , Junbo Sun

Extrusion-filament and no-framework craft significantly influence microcracks in 3D printing concrete (3DPC). A detailed analysis of these microcracks is essential to improve overall performance of material. However, fast and automated methods for capturing and measuring representative microcrack information in 3DPC are currently lacking. This paper presents a transformer based method for automatic quantization of microcosmic information in 3DPC, enabling a comprehensive analysis of microcracks. Additionally, a transformer network to rapidly and cost-effectively obtain high-quality microscopic images is introduced. The proposed quantization method involves a range of enhancement tactics over an existing baseline model, demonstrating higher accuracy in detecting inner microcracks of 3DPC compared to current advanced algorithms. This method surpasses existing microscopic imaging technologies in terms of information content, computational speed, and cost-efficiency. Therefore, this method will have promising applications for analyzing other micro-details in concrete when it is supplemented with a diverse and extensive training dataset.

{"title":"Microcrack investigations of 3D printing concrete using multiple transformer networks","authors":"Hongyu Zhao , Xiangyu Wang , Zhaohui Chen , Xianda Liu , Yufei Wang , Jun Wang , Junbo Sun","doi":"10.1016/j.autcon.2025.106017","DOIUrl":"10.1016/j.autcon.2025.106017","url":null,"abstract":"<div><div>Extrusion-filament and no-framework craft significantly influence microcracks in 3D printing concrete (3DPC). A detailed analysis of these microcracks is essential to improve overall performance of material. However, fast and automated methods for capturing and measuring representative microcrack information in 3DPC are currently lacking. This paper presents a transformer based method for automatic quantization of microcosmic information in 3DPC, enabling a comprehensive analysis of microcracks. Additionally, a transformer network to rapidly and cost-effectively obtain high-quality microscopic images is introduced. The proposed quantization method involves a range of enhancement tactics over an existing baseline model, demonstrating higher accuracy in detecting inner microcracks of 3DPC compared to current advanced algorithms. This method surpasses existing microscopic imaging technologies in terms of information content, computational speed, and cost-efficiency. Therefore, this method will have promising applications for analyzing other micro-details in concrete when it is supplemented with a diverse and extensive training dataset.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"172 ","pages":"Article 106017"},"PeriodicalIF":9.6,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349417","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}

引用次数: 0

Cost-effective LiDAR for pothole detection and quantification using a low-point-density approach

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-02-07 DOI: 10.1016/j.autcon.2025.106006

Ali Faisal, Suliman Gargoum

Pothole-induced vehicle damage and accidents have significantly increased recently, motivating urgent needs for effective detection and maintenance strategies. This paper introduces an algorithm optimized for low-cost LiDAR sensors that improves the detection and quantification of potholes on road surfaces. The algorithm uses curvature-based analysis to detect potholes in spatially thinned, structured LiDAR datasets and assesses their size through boundary delineation and voxelization. Testing on high-resolution LiDAR scans in Edmonton, Alberta demonstrated consistent detection of varying pothole sizes and shapes, with measurements matching manual LiDAR analysis. Statistical sensitivity analysis revealed that reducing point density significantly to 205 points/m

^{2}

(ppsm) had no measurable impact on detection and geometric assessment accuracy, maintaining measurement errors consistently within 3%–10%. The algorithm proved highly efficient with processing times of 88”/km and 23”/km for test segments with reduced point density, suggesting potential integration with city fleet vehicles for continuous and automated road maintenance monitoring.

{"title":"Cost-effective LiDAR for pothole detection and quantification using a low-point-density approach","authors":"Ali Faisal, Suliman Gargoum","doi":"10.1016/j.autcon.2025.106006","DOIUrl":"10.1016/j.autcon.2025.106006","url":null,"abstract":"<div><div>Pothole-induced vehicle damage and accidents have significantly increased recently, motivating urgent needs for effective detection and maintenance strategies. This paper introduces an algorithm optimized for low-cost LiDAR sensors that improves the detection and quantification of potholes on road surfaces. The algorithm uses curvature-based analysis to detect potholes in spatially thinned, structured LiDAR datasets and assesses their size through boundary delineation and voxelization. Testing on high-resolution LiDAR scans in Edmonton, Alberta demonstrated consistent detection of varying pothole sizes and shapes, with measurements matching manual LiDAR analysis. Statistical sensitivity analysis revealed that reducing point density significantly to 205 points/m<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> (ppsm) had no measurable impact on detection and geometric assessment accuracy, maintaining measurement errors consistently within 3%–10%. The algorithm proved highly efficient with processing times of 88”/km and 23”/km for test segments with reduced point density, suggesting potential integration with city fleet vehicles for continuous and automated road maintenance monitoring.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"172 ","pages":"Article 106006"},"PeriodicalIF":9.6,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349419","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}

引用次数: 0

Change detection network for construction housekeeping using feature fusion and large vision models

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-02-06 DOI: 10.1016/j.autcon.2025.106038

Kailai Sun , Zherui Shao , Yang Miang Goh , Jing Tian , Vincent J.L. Gan

Although poor housekeeping leads to construction accidents, there is limited technological research on it. Existing methods for detecting poor housekeeping face many challenges, including limited explanations, lack of locating of poor housekeeping and annotated datasets. To address these challenges, this paper proposes the Housekeeping Change Detection Network (HCDN), integrating a feature fusion module and a large vision model. This paper introduces the approach to establish a change detection dataset (Housekeeping-CCD) focused on construction housekeeping, along with a housekeeping segmentation dataset. Experimental results of our Housekeeping-CCD dataset demonstrate that HCDN outperforms existing state-of-the-art (SOTA) methods, achieving average accuracy (89.32 %), mean IoU (76.97 %), and mean F-score (86.67 %). The contributions include significant performance improvements compared to existing methods, providing an effective tool for enhancing construction housekeeping and safety.

引用次数: 0

Digital twin-based fatigue life assessment of orthotropic steel bridge decks using inspection robot and deep learning

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-02-06 DOI: 10.1016/j.autcon.2025.106022

Fei Hu , Hongye Gou , Haozhe Yang , Yi-Qing Ni , You-Wu Wang , Yi Bao

Fatigue cracks are a major issue affecting the lifespan and operation and maintenance (O&M) costs of bridges with orthotropic steel decks (OSDs), while current practices for detecting fatigue cracks often rely on manual inspection with time inefficiency. This paper presents a digital twin framework that employs robots equipped with nondestructive testing devices for data collection and deep learning algorithms for data analytics, aiming to enable automatic detection of cracks and assessment of fatigue life. Inspected crack are fed into a finite element model constructed via ABAQUS-FRANC3D co-simulation to conduct fatigue life analysis, and an MLE-PCE-Kriging surrogate modeling technique is developed to facilitate rapid assessment of fatigue life. The deep learning-based crack detection achieves accuracy and recall of 95.6 % and 92.2 %, respectively, while the MLR-PCE-Kriging model exhibits an MPAE of 2 %, demonstrating high accuracy. The proposed digital twin framework can guide automated bridge inspection, thereby promoting intelligent O&M management for bridges.

{"title":"Digital twin-based fatigue life assessment of orthotropic steel bridge decks using inspection robot and deep learning","authors":"Fei Hu , Hongye Gou , Haozhe Yang , Yi-Qing Ni , You-Wu Wang , Yi Bao","doi":"10.1016/j.autcon.2025.106022","DOIUrl":"10.1016/j.autcon.2025.106022","url":null,"abstract":"<div><div>Fatigue cracks are a major issue affecting the lifespan and operation and maintenance (O&M) costs of bridges with orthotropic steel decks (OSDs), while current practices for detecting fatigue cracks often rely on manual inspection with time inefficiency. This paper presents a digital twin framework that employs robots equipped with nondestructive testing devices for data collection and deep learning algorithms for data analytics, aiming to enable automatic detection of cracks and assessment of fatigue life. Inspected crack are fed into a finite element model constructed via ABAQUS-FRANC3D co-simulation to conduct fatigue life analysis, and an MLE-PCE-Kriging surrogate modeling technique is developed to facilitate rapid assessment of fatigue life. The deep learning-based crack detection achieves accuracy and recall of 95.6 % and 92.2 %, respectively, while the MLR-PCE-Kriging model exhibits an MPAE of 2 %, demonstrating high accuracy. The proposed digital twin framework can guide automated bridge inspection, thereby promoting intelligent O&M management for bridges.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"172 ","pages":"Article 106022"},"PeriodicalIF":9.6,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143292109","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}

引用次数: 0

Transformer-based deep learning model and video dataset for installation action recognition in offsite projects

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-02-06 DOI: 10.1016/j.autcon.2025.106042

Junyoung Jang , Eunbeen Jeong , Tae Wan Kim

This paper developed and evaluated the Precast Concrete Installation Dataset (PCI-Dataset), a large-scale video dataset for automatically recognizing precast concrete (PC) installation activities. The dataset comprises 12,791 video clips (5 s each, 1080 × 1080 resolution, 30fps) from actual PC construction sites, including 12 balanced activity classes combining three component types and four work stages. Evaluation of six Transformer-based video classification models showed VideoMAE V2 achieved the highest overall accuracy of 98.10 %, followed by UniFormer V2, Video Swin, MVIT, ViViT, and TimeSformer. VideoMAE V2 achieved F1 scores above 80 % for most activities, with a peak of 92.20 % for slab assembly. In a case study on a real PC construction site, the model demonstrated high recognition accuracies: 100 % for lifting, 85.83–100 % for rigging, and 93.75–100 % for assembly operations. The paper contributes to PC construction management theory by applying computer vision for real-time and automated work recognition and analysis.

引用次数: 0

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