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Entropy-centric framework for understanding and managing project dynamics in construction
IF 10.3 1区 工程技术
Automation in Construction Pub Date : 2024-12-09 DOI: 10.1016/j.autcon.2024.105928
Elyar Pourrahimian, Diana Salhab, Farook Hamzeh, Simaan AbouRizk
{"title":"Entropy-centric framework for understanding and managing project dynamics in construction","authors":"Elyar Pourrahimian, Diana Salhab, Farook Hamzeh, Simaan AbouRizk","doi":"10.1016/j.autcon.2024.105928","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105928","url":null,"abstract":"Traditional construction management methodologies often fail to address unforeseen challenges and uncertainties. This paper highlights that projects can exist in different states, often unidentified by project managers. These varying states necessitate different approaches, indicating that one-size-fits-all methods are insufficient. Using project data, entropy calculations, and simulations within a Design Science Research methodology, this paper offers indicators for evaluating project states and improving decision-making. The application of ChaosCompass to eight real-world projects showed higher entropy in projects exceeding budgets and schedules, indicating greater disorder and unpredictability. Conversely, projects on budget and schedule displayed more controlled progress. The findings reveal a significant correlation between high entropy and low forecast accuracy, underscoring entropy's critical role in project dynamics. This paper advocates an entropy-based approach to construction management, promising a more resilient and adaptable framework to address modern project complexities.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"249 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816515","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
Intelligent enhancement and identification of pipeline hyperbolic signal in 3D ground penetrating radar data
IF 10.3 1区 工程技术
Automation in Construction Pub Date : 2024-12-07 DOI: 10.1016/j.autcon.2024.105902
Yonggang Shen, Guoxuan Ye, Tuqiao Zhang, Tingchao Yu, Yiping Zhang, Zhenwei Yu
{"title":"Intelligent enhancement and identification of pipeline hyperbolic signal in 3D ground penetrating radar data","authors":"Yonggang Shen, Guoxuan Ye, Tuqiao Zhang, Tingchao Yu, Yiping Zhang, Zhenwei Yu","doi":"10.1016/j.autcon.2024.105902","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105902","url":null,"abstract":"Concealed pipeline maintenance in aging residential areas faces a key challenge of discrepancies between existing data and reality. Ground-penetrating radar with dense, high-speed 3D monitoring capabilities can provide massive data, but effective analysis is difficult due to the presence of irrelevant information. To accurately extract target information, this paper first proposes a 3D data array block concept, which enhances the feature relevance of target data blocks while expanding the data volume. An energy density window method is also proposed to enhance horizontal cross-sectional pipeline signals. Furthermore, a model named PR3DCNN for pipeline recognition is developed based on 3D convolutional neural networks and residual modules. Experimental results demonstrate that PR3DCNN has a classification accuracy of 0.871 for pipelines. After strengthening with 3D data array blocks and the energy density window, the PR-EDW-B model achieves an accuracy of 0.900, and can also classify the pipeline material and calculate its orientation.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"29 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816517","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
Estimating bucket fill factor for loaders using point cloud hole repairing
IF 10.3 1区 工程技术
Automation in Construction Pub Date : 2024-12-06 DOI: 10.1016/j.autcon.2024.105886
Guanlong Chen, Wenwen Dong, Zongwei Yao, Qiushi Bi, Xuefei Li
{"title":"Estimating bucket fill factor for loaders using point cloud hole repairing","authors":"Guanlong Chen, Wenwen Dong, Zongwei Yao, Qiushi Bi, Xuefei Li","doi":"10.1016/j.autcon.2024.105886","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105886","url":null,"abstract":"This paper introduces a bucket fill factor estimation method for earthmoving machinery aimed at solving sensor field-of-view blindness in measurements. Utilizing a point cloud repair technique, the method accurately reconstructs the 3D morphology of materials inside the bucket, even under occlusion conditions. The process begins by merging multiple frames of point cloud data to enhance information density. The material is then segmented from the comprehensive point cloud containing the bucket and other information. A repair strategy based on implicit surfaces reorganizes and fills holes in the point cloud. The Alpha Shape algorithm calculates the volume by using the filled point cloud. Extensive testing on loaders of different sizes proves the method’s robustness and shows significant accuracy improvements with the proposed data correction formula: 96.04% for small loaders and 95.36% for large loaders. Compared with existing volume estimation techniques, this method offers superior adaptability and reliability in real construction scenarios.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"8 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816522","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
Hybrid deep learning model for accurate cost and schedule estimation in construction projects using sequential and non-sequential data 利用顺序和非顺序数据的混合深度学习模型，准确估算建筑项目的成本和进度
IF 10.3 1区 工程技术
Automation in Construction Pub Date : 2024-12-06 DOI: 10.1016/j.autcon.2024.105904
Min-Yuan Cheng, Quoc-Tuan Vu, Frederik Elly Gosal
{"title":"Hybrid deep learning model for accurate cost and schedule estimation in construction projects using sequential and non-sequential data","authors":"Min-Yuan Cheng, Quoc-Tuan Vu, Frederik Elly Gosal","doi":"10.1016/j.autcon.2024.105904","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105904","url":null,"abstract":"Accurate estimation of construction costs and schedules is crucial for optimizing project planning and resource allocation. Most current approaches utilize traditional statistical analysis and machine learning techniques to process the vast amounts of data regularly generated in construction environments. However, these approaches do not adequately capture the intricate patterns in either time-dependent or time-independent data. Thus, a hybrid deep learning model (NN-BiGRU), combining Neural Network (NN) for time-independent and Bidirectional Gated Recurrent Unit (BiGRU) for time-dependent, was developed in this paper to estimate the final cost and schedule to completion of projects. The Optical Microscope Algorithm (OMA) was used to fine-tune the NN-BiGRU model (OMA-NN-BiGRU). The proposed model earned Reference Index (RI) values of 0.977 for construction costs and 0.932 for completion schedules. These findings underscore the potential of the OMA-NN-BiGRU model to provide highly accurate predictions, enabling stakeholders to make informed decisions that promote project efficiency and overall success.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"12 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816520","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
Enhanced real-time detection transformer (RT-DETR) for robotic inspection of underwater bridge pier cracks 用于水下桥墩裂缝机器人检测的增强型实时检测变压器 (RT-DETR)
IF 10.3 1区 工程技术
Automation in Construction Pub Date : 2024-12-06 DOI: 10.1016/j.autcon.2024.105921
Zhenming Lv, Shaojiang Dong, Zongyou Xia, Jingyao He, Jiawei Zhang
{"title":"Enhanced real-time detection transformer (RT-DETR) for robotic inspection of underwater bridge pier cracks","authors":"Zhenming Lv, Shaojiang Dong, Zongyou Xia, Jingyao He, Jiawei Zhang","doi":"10.1016/j.autcon.2024.105921","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105921","url":null,"abstract":"The inadequate visual environment reduces the accuracy of underwater bridge pier fracture detection. Consequently, this paper suggests enhancing the backbone of the Real-Time Detection Transformer(RT-DETR) model to serve as the backbone of the YOLOv8 model. This will be achieved by substituting the Faster Implementation of CSP Bottleneck with 2 convolutions(C2f) module with the Poly Kernel Inception(PKI) Block, which is composed of the PKI Module and Context Anchor Attention(CAA) Block. Its strong capability to distinguish cracks and background features enables accurate recognition of underwater bridge pier cracks. To provide data for detecting these cracks, the enhanced Unpaired Image to Image Translation(CycleGAN) network converts land-style bridge crack images to underwater-style fracture images. The proposed model achieved an F1 score of 0.85 and a mAP50 of 0.84. The real-time detection of underwater bridge fractures by the underwater robot was facilitated by the FPS index of 87.47, which optimizes the detection efficiency.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"12 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816519","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
Self-training method for structural crack detection using image blending-based domain mixing and mutual learning
IF 10.3 1区 工程技术
Automation in Construction Pub Date : 2024-12-05 DOI: 10.1016/j.autcon.2024.105892
Quang Du Nguyen, Huu-Tai Thai, Son Dong Nguyen
{"title":"Self-training method for structural crack detection using image blending-based domain mixing and mutual learning","authors":"Quang Du Nguyen, Huu-Tai Thai, Son Dong Nguyen","doi":"10.1016/j.autcon.2024.105892","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105892","url":null,"abstract":"Deep learning-based structural crack detection utilizing fully supervised methods requires laborious labeling of training data. Moreover, models trained on one dataset often experience significant performance drops when applied to others due to domain shifts prompted by diverse structures, materials, and environmental conditions. This paper addresses the issues by introducing a robust self-training domain adaptive segmentation (STDASeg) pipeline. STDASeg incorporates an image blending-based domain mixing module to minimize domain discrepancies. Additionally, STDASeg involves a two-stage self-training framework characterized by the mutual learning scheme between Convolutional Neural Networks and Transformers, effectively learning domain invariant features from the two domains. Comprehensive evaluations across three challenging cross-dataset crack detection scenarios highlight the superiority of STDASeg over traditional supervised training approaches and current state-of-the-art methods. These results confirm the stability of STDASeg, thus supporting more efficient infrastructure assessments.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"34 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142788866","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
Bridge defect detection using small sample data with deep learning and Hyperspectral imaging
IF 10.3 1区 工程技术
Automation in Construction Pub Date : 2024-12-05 DOI: 10.1016/j.autcon.2024.105900
Xiong Peng, Pengtao Wang, Kun Zhou, Zhipeng Yan, Xingu Zhong, Chao Zhao
{"title":"Bridge defect detection using small sample data with deep learning and Hyperspectral imaging","authors":"Xiong Peng, Pengtao Wang, Kun Zhou, Zhipeng Yan, Xingu Zhong, Chao Zhao","doi":"10.1016/j.autcon.2024.105900","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105900","url":null,"abstract":"The visual sensing method is an effective way to address long-term health monitoring of bridges. However, bridge defect detection based on visible light imaging mainly relies on grayscale and regional edge gradient information, which brings challenges such as limited information dimensions and complex background. This paper introduces a bridge defect detection method that leverages hyperspectral imaging, utilizing the unique integration of spectral and spatial information. Also a convolutional neural network algorithm with dual branches and dense blocks for spectral feature extraction is developed. This framework includes spectral and spatial branches, which independently extract respective features in order to minimize mutual interference. Compared with the support vector machine and traditional deep learning algorithms, the proposed method attains an overall model prediction accuracy(OA) of 98.57 %, an average accuracy (AA) of 98.16 %, and a Kappa coefficient of 0.9814, representing the best classification performance on small sample datasets.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"20 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142788865","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
Augmented reality in U.S. Construction: Trends and future directions
IF 10.3 1区 工程技术
Automation in Construction Pub Date : 2024-12-05 DOI: 10.1016/j.autcon.2024.105895
James O. Toyin, Anoop Sattineni, Eric M. Wetzel, Ayodele A. Fasoyinu, Jeff Kim
{"title":"Augmented reality in U.S. Construction: Trends and future directions","authors":"James O. Toyin, Anoop Sattineni, Eric M. Wetzel, Ayodele A. Fasoyinu, Jeff Kim","doi":"10.1016/j.autcon.2024.105895","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105895","url":null,"abstract":"Despite significant research attention on Augmented Reality (AR) in construction, there is a lack of literature on its application trends and future prospects in the U.S. construction industry. The objective of this paper is to investigate the current state of AR in construction, benefits, and drivers and offers actionable suggestions for enhancing AR applications. A systematic critical review and bibliometric mapping of related articles were conducted. Based on defined inclusion and exclusion criteria, 64 eligible articles published between 2006 and 2023 were selected for the final review. The result of this study reveals six key AR application areas, 24 benefits and 23 drivers. Actionable suggestions to advance AR application were also discussed. Additionally, a conceptual model to support effective AR implementation in construction was developed. By synthesizing existing knowledge and identifying future research areas, this study aims to improve the advancement and application of AR technology in the construction industry.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"10 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142788833","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
Automated legal consulting in construction procurement using metaheuristically optimized large language models
IF 10.3 1区 工程技术
Automation in Construction Pub Date : 2024-12-04 DOI: 10.1016/j.autcon.2024.105891
Chi-Yun Liu, Jui-Sheng Chou
{"title":"Automated legal consulting in construction procurement using metaheuristically optimized large language models","authors":"Chi-Yun Liu, Jui-Sheng Chou","doi":"10.1016/j.autcon.2024.105891","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105891","url":null,"abstract":"This paper introduces a hybrid optimization algorithm, Pilgrimage Walk Optimization - Differential Evolution (PWO-DE), inspired by Taiwan's cultural traditions, to fine-tune large language models (LLMs) for government procurement legal consulting. Addressing the unique requirements of Traditional Chinese, this research develops two tailored LLMs, Llama3-TAIDE and Taiwan-LLM, which significantly enhance automated legal advisory systems. Through rigorous comparative evaluations, the PWO-DE algorithm demonstrates superior performance against various well-established single and hybrid metaheuristic algorithms, ensuring effective decision-making and risk management in government procurement. A user-friendly chat interface has also been created, facilitating the practical application of LLMs, increasing their accessibility and impact on legal consulting within the construction and legal fields. This study showcases the integration of cultural insights into algorithmic design, establishing a new benchmark for future advancements in the automation of complex legal consulting tasks.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"18 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142788871","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
Plug-and-play method for segmenting concrete bridge cracks using the segment anything model with a fractal dimension matrix prompt
IF 10.3 1区 工程技术
Automation in Construction Pub Date : 2024-12-04 DOI: 10.1016/j.autcon.2024.105906
Shuai Teng, Airong Liu, Zuxiang Situ, Bingcong Chen, Zhihua Wu, Yixiao Zhang, Jialin Wang
{"title":"Plug-and-play method for segmenting concrete bridge cracks using the segment anything model with a fractal dimension matrix prompt","authors":"Shuai Teng, Airong Liu, Zuxiang Situ, Bingcong Chen, Zhihua Wu, Yixiao Zhang, Jialin Wang","doi":"10.1016/j.autcon.2024.105906","DOIUrl":"https://doi.org/10.1016/j.autcon.2024.105906","url":null,"abstract":"This paper addresses the diverse scenarios of bridge crack segmentation, proposing a method for detecting cracks on land and underwater using the Segment Anything Model (SAM) prompted by a fractal dimension matrix. The proposed method does not require additional training and obtains fractal feature information of cracks through fractal dimension matrix calculation. These feature information serve as prompt information for SAM to establish a plug-and-play crack segmentation method. The method achieves high detection performance, with a mean accuracy, IoU, and F1-Score of 99.6 %, 0.89, and 0.95 for land cracks, and 97.6 %, 0.89, and 0.95 for underwater cracks, respectively. This represents a significant improvement over methods that do not use the fractal dimension matrix for SAM prompts. Additionally, the method requires no additional training, showcasing excellent generalizability and practical potential for real-world applications in diverse environments.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"38 1","pages":""},"PeriodicalIF":10.3,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142788870","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
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