Automation in Construction最新文献_第5页

Cost optimization of repetitive project scheduling through a constraint programming-based relax-and-solve algorithm 基于约束规划的重复项目调度成本优化松弛求解算法

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-13 DOI: 10.1016/j.autcon.2025.106272

Zhiyuan Hu , Futian Wang , Yuanjie Tang

{"title":"Cost optimization of repetitive project scheduling through a constraint programming-based relax-and-solve algorithm","authors":"Zhiyuan Hu , Futian Wang , Yuanjie Tang","doi":"10.1016/j.autcon.2025.106272","DOIUrl":"10.1016/j.autcon.2025.106272","url":null,"abstract":"<div><div>This paper focuses on the cost minimization of the multi-mode resource-constrained repetitive project scheduling problem with multiple crews, crew interruptions, and soft logic. The resource allocation of each crew is considered. To explore the impact of different construction strategies on project costs, mixed-integer linear programming (MILP) and constraint programming (CP) models are developed representing different construction scenarios. A relax-and-solve (R&S) algorithm, incorporating a rolling horizon and constraint programming, is proposed to obtain near-optimal solutions within reasonable time limits. The case study reveals that considering crew resource allocation and adopting more flexible construction strategies can contribute to reducing total project costs. The findings provide construction managers with practical strategies to improve scheduling, resource management, and cost control. Meanwhile, the proposed algorithm performs competitively compared with MILP and CP models, which inspires future research to apply this algorithm to other repetitive project scheduling problems.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"176 ","pages":"Article 106272"},"PeriodicalIF":9.6,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935085","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

VIF–TOPSIS coupling algorithm for image quality assessment in smart construction site management 智能施工现场管理图像质量评价的VIF-TOPSIS耦合算法

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-13 DOI: 10.1016/j.autcon.2025.106239

Chunmei Wang, Yuming Tao

{"title":"VIF–TOPSIS coupling algorithm for image quality assessment in smart construction site management","authors":"Chunmei Wang, Yuming Tao","doi":"10.1016/j.autcon.2025.106239","DOIUrl":"10.1016/j.autcon.2025.106239","url":null,"abstract":"<div><div>Real-time monitoring is critical for smart construction management, yet environmental complexities degrade surveillance video quality. Traditional visual information fidelity (VIF) algorithms depend on reference images, which limits their use in no-reference scenarios such as autonomous systems and industrial inspection. Grounded in information theory, this paper proposes an algorithm that integrates VIF with the technique for order preference by similarity to ideal solution (TOPSIS), obtaining the coupling algorithm VIF–TOPSIS. Wavelet transforms extract the features; TOPSIS selects the optimal features; and the inverse wavelet transform reconstructs the images. An “ideal solution” image replaces the reference dependencies, enabling no-reference quality assessment. Evaluated on multiframe samples, the algorithm significantly improved peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and gradient magnitude similarity deviation (GMSD) compared to traditional methods. Enhanced contrast and brightness further validated its efficacy in dynamic environments. This framework advances real-time video processing, offering robust technical support for smart construction management.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"176 ","pages":"Article 106239"},"PeriodicalIF":9.6,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942211","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

Digital twin for 3D interactive building operations: Integrating BIM, IoT-enabled building automation systems, AI, and mixed reality 3D交互式建筑操作的数字孪生：集成BIM、物联网建筑自动化系统、人工智能和混合现实

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-13 DOI: 10.1016/j.autcon.2025.106277

Tianyou Ma , Fu Xiao , Chong Zhang , Jing Zhang , Hanbei Zhang , Kan Xu , Xiaowei Luo

{"title":"Digital twin for 3D interactive building operations: Integrating BIM, IoT-enabled building automation systems, AI, and mixed reality","authors":"Tianyou Ma , Fu Xiao , Chong Zhang , Jing Zhang , Hanbei Zhang , Kan Xu , Xiaowei Luo","doi":"10.1016/j.autcon.2025.106277","DOIUrl":"10.1016/j.autcon.2025.106277","url":null,"abstract":"<div><div>Digital Twin (DT) technology has emerged as a next-generation smart building management solution, seamlessly bridging traditional Building Automation Systems (BAS) with Industry 4.0 innovations such as Building Information Modelling (BIM), artificial intelligence (AI), big data, Internet of Things (IoT), and Extended Reality (XR). However, current DT applications in building operations remain nascent, challenged by multi-source data integration, technology interoperability, and visualization interface development. This study proposes a five-layer DT architecture integrating BIM, BAS, IoT, AI, and MR for 3D interactive building operations, implemented on a typical floor of a high-rise office building incorporating its central air conditioning system. The DT demonstrated both on-site and remote capabilities, including BIM visualization, mapping and navigation, indoor environment monitoring, portable HVAC system monitoring and control, and AI-empowered optimization. These capabilities represent significant advantages in terms of operation management, work efficiency, operator experience and response speed.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"176 ","pages":"Article 106277"},"PeriodicalIF":9.6,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942213","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

Structural geometry-informed 3D deep learning for segmental tunnel lining analysis in point clouds 基于结构几何的三维深度学习点云分段隧道衬砌分析

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-13 DOI: 10.1016/j.autcon.2025.106281

Wei Lin , Brian Sheil , Pin Zhang , Kang Li , Xiongyao Xie

{"title":"Structural geometry-informed 3D deep learning for segmental tunnel lining analysis in point clouds","authors":"Wei Lin , Brian Sheil , Pin Zhang , Kang Li , Xiongyao Xie","doi":"10.1016/j.autcon.2025.106281","DOIUrl":"10.1016/j.autcon.2025.106281","url":null,"abstract":"<div><div>The emergence of 3D computer vision presents a promising paradigm for structural health monitoring of segmental tunnel linings. For automated inspection and analysis, it is first necessary to segment the associated point clouds into individual tunnel segments. Whilst 3D deep learning (DL) networks are suitable for such tasks, the similarity of segment geometries renders generic 3D DL network architectures unsuitable. This paper introduces a new 3D DL network ‘LiningNet’ tailored to segmental linings. LiningNet incorporates a structural geometry-informed mechanism which is derived from a bi-neighbourhood feature aggregation module, geometric label encoding, and a corresponding geometric loss function, which characterise the specialised designs. The effectiveness of LiningNet is validated using comprehensive experimental results and benchmarked against other state-of-the-art networks. Hyperparameters and feature aggregation modules in LiningNet are also determined based on rigorous ablation experiments. LiningNet presents superior segmentation performance and promising applications for structural inspection.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"176 ","pages":"Article 106281"},"PeriodicalIF":9.6,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935084","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

Balancing AI generalization and specialization: Multi-domain learning for universal computer vision models in construction 平衡人工智能泛化和专门化：构建中的通用计算机视觉模型的多领域学习

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-13 DOI: 10.1016/j.autcon.2025.106279

Jinwoo Kim

{"title":"Balancing AI generalization and specialization: Multi-domain learning for universal computer vision models in construction","authors":"Jinwoo Kim","doi":"10.1016/j.autcon.2025.106279","DOIUrl":"10.1016/j.autcon.2025.106279","url":null,"abstract":"<div><div>While model generalization and specialization are a critical concern in computer vision, balancing them in data-scarce construction settings remains challenging due to their unique nature. This paper proposes a multi-domain learning approach where a model acquires domain-generic visual knowledge from various domain datasets, while maintaining domain-specific predictabilities for each individual domain. Results show that the approach can train a more powerful model than traditional methods, regardless of training dataset size, evaluation metrics, and test domains. The model, trained on only half to one-eighth of the dataset size used in traditional methods, exhibited comparable or even superior performance while demonstrating greater robustness to challenging and diverse construction environments. These findings suggest that the approach can competitively balance model generalization and specialization, leading to improved performance across various aspects. This advance can optimize the use of given training datasets and facilitate the development of more universal computer vision models in construction.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"176 ","pages":"Article 106279"},"PeriodicalIF":9.6,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942214","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

Multimodal framework integrating multiple large language model agents for intelligent geotechnical design 集成多个大语言模型主体的岩土工程智能设计多模态框架

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-12 DOI: 10.1016/j.autcon.2025.106257

Hao-Rao Xu, Ning Zhang, Zhen-Yu Yin, Pierre Guy Atangana Njock

{"title":"Multimodal framework integrating multiple large language model agents for intelligent geotechnical design","authors":"Hao-Rao Xu, Ning Zhang, Zhen-Yu Yin, Pierre Guy Atangana Njock","doi":"10.1016/j.autcon.2025.106257","DOIUrl":"10.1016/j.autcon.2025.106257","url":null,"abstract":"<div><div>Given the remarkable comprehensive ability, Large Language Models (LLMs) offer a promising solution for automatic geotechnical design. However, addressing multimodal geotechnical design assignments involving both text and image is still challenging for existing LLMs. This paper develops a framework integrating multiple LLMs, the multi-GeoLLM, for multi-modal geotechnical design. It can understand multimodal data, extract design information, and generate design solutions. The innovations involve four aspects: a search engine for multimodal geo-prompt generation; a multiple agent-based self-review module extracting design information; a tool module for decision-making, math calculation and drawing; and a Human-in-the-Loop Feedback (HLF) module for artificial review. Experiments involving 60 sets of text, image, and text-image data of unreinforced footings showcase the high performance of multi-GeoLLM with an accuracy of 1.0, precision and recall of 0.992. 100 textual cases also validated the robustness of multi-GeoLLM, achieving a precision of 0.999, a recall of 1 and an accuracy of 0.97.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"176 ","pages":"Article 106257"},"PeriodicalIF":9.6,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935935","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

AI-driven automated and integrated structural health monitoring under environmental and operational variations 在环境和操作变化下，人工智能驱动的自动化和集成结构健康监测

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-12 DOI: 10.1016/j.autcon.2025.106222

Hamed Hasani , Francesco Freddi , Riccardo Piazza

{"title":"AI-driven automated and integrated structural health monitoring under environmental and operational variations","authors":"Hamed Hasani , Francesco Freddi , Riccardo Piazza","doi":"10.1016/j.autcon.2025.106222","DOIUrl":"10.1016/j.autcon.2025.106222","url":null,"abstract":"<div><div>An automated framework for structural health monitoring is presented in this paper, encompassing modal identification, health monitoring, and damage localization while accounting for environmental and operational variations. The proposed framework automates the modal identification process using covariance-driven stochastic subspace identification, coupled with a Gaussian mixture model clustering approach for automatic pole selection. It further integrates autoencoder neural network and proposed thresholding process for ongoing health monitoring. For the automated damage localization step, a pattern recognition–based method is proposed that integrates the decomposition capabilities of advanced signal processing techniques, such as discrete wavelet transforms, with the learning capabilities of long short-term memory models, designed to minimize false positives and enable precise identification of stiffness loss zones. Experimental validation on a laboratory bridge structure subjected to simulated damage scenarios demonstrates the framework’s effectiveness. Designed with a user-friendly interface, the system eliminates the need for manual intervention and facilitates infrastructure health monitoring.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"176 ","pages":"Article 106222"},"PeriodicalIF":9.6,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935082","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

Unsupervised pavement rutting detection using structured light and area-based deep learning 使用结构光和基于区域的深度学习的无监督路面车辙检测

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-12 DOI: 10.1016/j.autcon.2025.106235

Yishun Li , Lunpeng Li , Shengchuan Jiang , Chenglong Liu , Zihang Weng , Yuchuan Du

{"title":"Unsupervised pavement rutting detection using structured light and area-based deep learning","authors":"Yishun Li , Lunpeng Li , Shengchuan Jiang , Chenglong Liu , Zihang Weng , Yuchuan Du","doi":"10.1016/j.autcon.2025.106235","DOIUrl":"10.1016/j.autcon.2025.106235","url":null,"abstract":"<div><div>Timely and comprehensive pavement rutting detection is crucial for road safety and maintenance. Traditional methods often fail to capture full morphological characteristics and severity of rutting. This paper proposes an area-based pavement rutting detection method using unsupervised deep learning. An adaptive point cloud rasterization strategy and multi-feature mapping enhance surface detail preservation while reducing complexity. A deep learning model segments rutting based on feature similarity and spatial continuity, refined by point cloud reconstruction and post-processing. Tested on a 600 km roadway dataset with 706 rutting samples, the method achieves 91.46 % accuracy, surpassing conventional models. It maintains high efficiency, reduces labeled data reliance, and requires only structured light-based scanning, making it suitable for large-scale applications. Ablation studies validate the multi-feature fusion strategy, establishing a new paradigm for high-precision rutting detection. Successfully deployed in real-world inspections, this method advances infrastructure assessment within smart transportation systems.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"176 ","pages":"Article 106235"},"PeriodicalIF":9.6,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935081","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

TBM tunneling performance fusion prediction based on multimodal decomposition and multi-deep learning 基于多模态分解和多深度学习的隧道掘进机掘进性能融合预测

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-12 DOI: 10.1016/j.autcon.2025.106271

Kang Fu , Yiguo Xue , Daohong Qiu , Fanmeng Kong , Min Han , Haolong Yan

{"title":"TBM tunneling performance fusion prediction based on multimodal decomposition and multi-deep learning","authors":"Kang Fu , Yiguo Xue , Daohong Qiu , Fanmeng Kong , Min Han , Haolong Yan","doi":"10.1016/j.autcon.2025.106271","DOIUrl":"10.1016/j.autcon.2025.106271","url":null,"abstract":"<div><div>Accurate prediction of TBM tunneling performance is crucial for improving construction efficiency. This paper proposes a fusion prediction method based on multimodal decomposition and multi-Deep Learning. First, tunneling data are preprocessed to build a sample database. Then, an improved ISTL model is developed to decompose tunneling performance into trend, seasonal, cycle, and residual components. Hyperparameters of multiple Deep Learning models are optimized using an improved IWOA algorithm, forming the ISTL-multi-DL model for preliminary prediction. Subsequently, error correction is applied to obtain the CISTL-multi-DL model, achieving <em>MAPE</em> values of 1.89 % and 1.43 % for <em>FPI</em> and <em>TPI</em> predictions, respectively. Comparative analysis shows that the CISTL-multi-DL model outperforms the IWOA-Autoformer, IWOA-Attention-LSTM, IWOA-BiTCN, and IWOA-DeepAR models by an average of over 40 %, and demonstrates superiority over unoptimized and traditional Machine Learning models. The proposed model provides accurate multi-step predictions and valuable support for TBM tunneling construction.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"176 ","pages":"Article 106271"},"PeriodicalIF":9.6,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935167","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

UAV-assisted bridge alignment measurement using enhanced small target detection and adaptive ellipse fitting 基于增强小目标检测和自适应椭圆拟合的无人机辅助桥梁对准测量

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-12 DOI: 10.1016/j.autcon.2025.106258

Lu Deng , Cheng Zhang , Weiqi Mao , Feng Zhang , Lizhi Long , Hao Dai , Jingjing Guo

引用次数: 0