Automation in Construction最新文献_第9页

Intelligent prediction and control of deformation induced by a servo-strutted deep excavation adjacent to existing tunnels 与既有隧道相邻的伺服支撑深基坑变形智能预测与控制

IF 11.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-12-24 DOI: 10.1016/j.autcon.2025.106735

Mingpeng Liu , Dechun Lu , Franz Tschuchnigg , Fengwen Lai , Xin Zhou , Feng Chen

To enable intelligent prediction and control of excavation-induced deformations, including wall deflection, ground surface settlement, and nearby tunnel displacements, this paper proposes an integrated approach combining in-situ test-based numerical modelling, Bayesian-optimised deep neural networks (BO-DNNs), and a DNN-based Newton-Raphson (DNN-NR) algorithm. The proposed framework serves as a decision-support tool for pre-construction planning of a deep excavation adjacent to existing tunnels. Specifically, the verified numerical models generate the training dataset for the BO-DNN model, which achieves high predictive accuracy for maximum deformations under varying servo-force combinations and excavation geometries. The BO-DNN analysis reveals that servo forces significantly influence deformation patterns and can even alter the direction of wall deflection and ground settlement. Leveraging this surrogate model, the DNN-NR algorithm efficiently identifies optimal servo forces to minimise deformations. The applications demonstrate that the DNN-NR-derived forces effectively restrict deformations within allowable limits. Furthermore, the algorithm quantifies the relative importance of each servo strut in deformation control and provides allowable axial force thresholds, facilitating adaptive force adjustments during the excavation.

为了智能预测和控制开挖引起的变形，包括墙体挠度、地表沉降和隧道附近位移，本文提出了一种综合方法，将基于原位试验的数值模拟、贝叶斯优化深度神经网络（bo - dnn）和基于dnn的牛顿-拉斐尔（DNN-NR）算法相结合。该框架可作为邻近现有隧道的深基坑施工前规划的决策支持工具。具体来说，经过验证的数值模型生成了BO-DNN模型的训练数据集，该模型对不同伺服力组合和开挖几何形状下的最大变形达到了很高的预测精度。BO-DNN分析表明，随动力对变形模式有显著影响，甚至可以改变墙体挠度和地面沉降方向。利用该替代模型，DNN-NR算法有效地识别最佳伺服力以最小化变形。应用表明，dnn - nn推导的力有效地将变形限制在允许的范围内。此外，该算法量化了各伺服支柱在变形控制中的相对重要性，并提供了允许的轴向力阈值，便于开挖过程中的自适应力调整。

{"title":"Intelligent prediction and control of deformation induced by a servo-strutted deep excavation adjacent to existing tunnels","authors":"Mingpeng Liu , Dechun Lu , Franz Tschuchnigg , Fengwen Lai , Xin Zhou , Feng Chen","doi":"10.1016/j.autcon.2025.106735","DOIUrl":"10.1016/j.autcon.2025.106735","url":null,"abstract":"<div><div>To enable intelligent prediction and control of excavation-induced deformations, including wall deflection, ground surface settlement, and nearby tunnel displacements, this paper proposes an integrated approach combining in-situ test-based numerical modelling, Bayesian-optimised deep neural networks (BO-DNNs), and a DNN-based Newton-Raphson (DNN-NR) algorithm. The proposed framework serves as a decision-support tool for pre-construction planning of a deep excavation adjacent to existing tunnels. Specifically, the verified numerical models generate the training dataset for the BO-DNN model, which achieves high predictive accuracy for maximum deformations under varying servo-force combinations and excavation geometries. The BO-DNN analysis reveals that servo forces significantly influence deformation patterns and can even alter the direction of wall deflection and ground settlement. Leveraging this surrogate model, the DNN-NR algorithm efficiently identifies optimal servo forces to minimise deformations. The applications demonstrate that the DNN-NR-derived forces effectively restrict deformations within allowable limits. Furthermore, the algorithm quantifies the relative importance of each servo strut in deformation control and provides allowable axial force thresholds, facilitating adaptive force adjustments during the excavation.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106735"},"PeriodicalIF":11.5,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823223","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

Vision-language model-based intelligent assistant for onsite construction safety inspection 基于视觉语言模型的施工现场安全检查智能助手

IF 11.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-12-24 DOI: 10.1016/j.autcon.2025.106728

Rahat Hussain , Doyeop Lee , Muhammad Sibtain Abbas , Syed Farhan Alam Zaidi , Akeem Pedro , Chansik Park

Construction site inspection demands a contextual understanding of dynamic job-site conditions, traditionally relying on inspectors' expertise combined with adherence to predefined safety regulations and industry standards to identify hazards. While vision-language models can detect and describe hazards, they struggle to correlate observations with regulations due to limitations in geometric reasoning. Recent studies show progress in compliance checking, but these models are still challenged by dynamic scenarios. This paper introduces a hybrid framework that combines geometric reasoning with LLM-powered interpretation to improve regulation-aware hazard detection. The system achieved high detection accuracy: 97 % for ladder use, 94.6 % for mobile scaffolding, and 99 % for fire-related work. Captioning performance evaluated through BLEU, ROUGE, METEOR, and BERT Score showed strong semantic alignment. User feedback confirmed its efficiency and ease of use, even under dynamic conditions. By integrating visual data with regulatory reasoning, the proposed system offers a practical, domain-adapted solution for enhancing construction safety inspections.

建筑工地检查需要对动态工作现场条件的上下文理解，传统上依赖检查员的专业知识，并遵守预定义的安全法规和行业标准来识别危险。虽然视觉语言模型可以检测和描述危险，但由于几何推理的限制，它们很难将观察结果与规则联系起来。近年来的研究表明，依从性检查取得了进展，但这些模型仍然受到动态情景的挑战。本文介绍了一种混合框架，将几何推理与llm驱动的解释相结合，以改进法规感知的危害检测。该系统达到了很高的检测精度：对梯子使用的检测准确率为97%，对移动脚手架的检测准确率为94.6%，对火灾相关工作的检测准确率为99%。通过BLEU， ROUGE， METEOR和BERT Score评估字幕性能显示出较强的语义一致性。用户反馈证实了它的效率和易用性，即使在动态条件下也是如此。通过将可视化数据与监管推理相结合，所提出的系统为加强建筑安全检查提供了一个实用的、适合领域的解决方案。

{"title":"Vision-language model-based intelligent assistant for onsite construction safety inspection","authors":"Rahat Hussain , Doyeop Lee , Muhammad Sibtain Abbas , Syed Farhan Alam Zaidi , Akeem Pedro , Chansik Park","doi":"10.1016/j.autcon.2025.106728","DOIUrl":"10.1016/j.autcon.2025.106728","url":null,"abstract":"<div><div>Construction site inspection demands a contextual understanding of dynamic job-site conditions, traditionally relying on inspectors' expertise combined with adherence to predefined safety regulations and industry standards to identify hazards. While vision-language models can detect and describe hazards, they struggle to correlate observations with regulations due to limitations in geometric reasoning. Recent studies show progress in compliance checking, but these models are still challenged by dynamic scenarios. This paper introduces a hybrid framework that combines geometric reasoning with LLM-powered interpretation to improve regulation-aware hazard detection. The system achieved high detection accuracy: 97 % for ladder use, 94.6 % for mobile scaffolding, and 99 % for fire-related work. Captioning performance evaluated through BLEU, ROUGE, METEOR, and BERT Score showed strong semantic alignment. User feedback confirmed its efficiency and ease of use, even under dynamic conditions. By integrating visual data with regulatory reasoning, the proposed system offers a practical, domain-adapted solution for enhancing construction safety inspections.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106728"},"PeriodicalIF":11.5,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823225","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 crack detection in axially loaded grouted connections of offshore wind turbines using embedded Fibre Bragg Grating sensor data 利用嵌入式光纤光栅传感器数据自动检测海上风力涡轮机轴向加载注浆连接中的裂纹

IF 11.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-12-24 DOI: 10.1016/j.autcon.2025.106734

Jakob Borgelt , Joshua Possekel , Peter Schaumann , Elyas Ghafoori

Grouted connections are critical components in offshore wind turbine foundations subjected to cyclic axial loading. Understanding their fatigue degradation is essential for structural integrity. This paper presents an application of a frequency-based method for automated crack detection and evaluation in grouted connections using embedded Fibre Bragg Grating (FBG) sensors. The method identifies mechanical response reversal, from grout compression to elongation at load peaks, linked to crack initiation, using short-time Fourier transform (STFT) analysis of FBG signals. Validated through fatigue testing, it enables robust, automated, and spatially resolved crack detection throughout the fatigue life. Statistical evaluation revealed typical crack progression from outer regions towards central shear key levels. A correlation was found between crack formation and displacement behaviour, segmented into stable, incremental, and progressive degradation phases. Rapid displacement increases in the progressive phase occurred only after cracks formed across all shear key levels, offering insights for damage detection and monitoring strategies.

注浆连接是海上风力发电机基础中承受轴向循环荷载的关键部件。了解它们的疲劳退化对结构完整性至关重要。本文介绍了一种基于频率的方法，用于嵌入式光纤布拉格光栅（FBG）传感器在注浆连接中的自动裂纹检测和评估。该方法利用FBG信号的短时傅里叶变换（STFT）分析，识别与裂纹萌生有关的机械响应反转，从浆液压缩到荷载峰值时的伸长。通过疲劳测试验证，它可以在整个疲劳寿命期间实现可靠、自动化和空间分辨的裂纹检测。统计评价显示，典型的裂缝由外区向中心剪切关键水平扩展。裂缝形成和位移行为之间存在相关性，可分为稳定、增量和渐进退化阶段。在推进阶段，位移的快速增加只发生在所有剪切关键水平上形成裂缝之后，这为损伤检测和监测策略提供了见解。

{"title":"Automated crack detection in axially loaded grouted connections of offshore wind turbines using embedded Fibre Bragg Grating sensor data","authors":"Jakob Borgelt , Joshua Possekel , Peter Schaumann , Elyas Ghafoori","doi":"10.1016/j.autcon.2025.106734","DOIUrl":"10.1016/j.autcon.2025.106734","url":null,"abstract":"<div><div>Grouted connections are critical components in offshore wind turbine foundations subjected to cyclic axial loading. Understanding their fatigue degradation is essential for structural integrity. This paper presents an application of a frequency-based method for automated crack detection and evaluation in grouted connections using embedded Fibre Bragg Grating (FBG) sensors. The method identifies mechanical response reversal, from grout compression to elongation at load peaks, linked to crack initiation, using short-time Fourier transform (STFT) analysis of FBG signals. Validated through fatigue testing, it enables robust, automated, and spatially resolved crack detection throughout the fatigue life. Statistical evaluation revealed typical crack progression from outer regions towards central shear key levels. A correlation was found between crack formation and displacement behaviour, segmented into stable, incremental, and progressive degradation phases. Rapid displacement increases in the progressive phase occurred only after cracks formed across all shear key levels, offering insights for damage detection and monitoring strategies.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106734"},"PeriodicalIF":11.5,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823224","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 synchronization in closed-loop HVAC control for building operations 建筑运行闭环暖通空调控制中的数字双同步

IF 11.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-12-23 DOI: 10.1016/j.autcon.2025.106723

Jabeom Koo , Sungmin Yoon

Synchronization is essential for maintaining reliable and consistent digital twin environments in building operations. However, achieving accurate synchronization remains challenging due to limitations in virtual model representation and operational uncertainties. This paper proposes a digital twin synchronization (DTS) method based on a bidirectional synchronization framework that enhances digital twins' ability to simulate physical behaviors and mitigate uncertainties during operation. A field implementation was conducted in actual building operations, applying the DTS method to a chilled water pump differential pressure control loop. The results show that the bidirectional approach outperforms conventional unidirectional synchronization by reducing sensor errors and model uncertainties and improving overall simulator reliability. The proposed DTS method enables more consistent and reliable digital twin operation by improving synchronization accuracy and reducing inconsistencies. The DTS approach reduced the digital twin simulator's overall MAPE from 5.32 % to 0.88 %, demonstrating its effectiveness in accurately replicating physical and control behaviors.

同步对于在建筑操作中维护可靠和一致的数字孪生环境至关重要。然而，由于虚拟模型表示和操作不确定性的限制，实现精确同步仍然具有挑战性。提出了一种基于双向同步框架的数字孪生同步（DTS）方法，增强了数字孪生对物理行为的模拟能力，减轻了运行过程中的不确定性。在实际建筑作业中进行了现场实施，将DTS方法应用于冷冻水泵压差控制回路。结果表明，双向同步方法通过减少传感器误差和模型不确定性，提高模拟器整体可靠性，优于传统的单向同步方法。提出的DTS方法通过提高同步精度和减少不一致性，使数字孪生操作更加一致和可靠。DTS方法将数字孪生模拟器的总体MAPE从5.32%降低到0.88%，证明了其在准确复制物理和控制行为方面的有效性。

{"title":"Digital twin synchronization in closed-loop HVAC control for building operations","authors":"Jabeom Koo , Sungmin Yoon","doi":"10.1016/j.autcon.2025.106723","DOIUrl":"10.1016/j.autcon.2025.106723","url":null,"abstract":"<div><div>Synchronization is essential for maintaining reliable and consistent digital twin environments in building operations. However, achieving accurate synchronization remains challenging due to limitations in virtual model representation and operational uncertainties. This paper proposes a digital twin synchronization (DTS) method based on a bidirectional synchronization framework that enhances digital twins' ability to simulate physical behaviors and mitigate uncertainties during operation. A field implementation was conducted in actual building operations, applying the DTS method to a chilled water pump differential pressure control loop. The results show that the bidirectional approach outperforms conventional unidirectional synchronization by reducing sensor errors and model uncertainties and improving overall simulator reliability. The proposed DTS method enables more consistent and reliable digital twin operation by improving synchronization accuracy and reducing inconsistencies. The DTS approach reduced the digital twin simulator's overall MAPE from 5.32 % to 0.88 %, demonstrating its effectiveness in accurately replicating physical and control behaviors.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106723"},"PeriodicalIF":11.5,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823244","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

Text-based automatic knowledge graph construction for road infrastructure operations management 基于文本的道路基础设施运营管理知识图谱自动构建

IF 11.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-12-22 DOI: 10.1016/j.autcon.2025.106733

Yafei Sun , Xuesong Shen , Sisi Zlatanova , Khalegh Barati , James Linke

Automatic construction of knowledge graphs (ACKG) from text enables intelligent operations management of road infrastructure (OMRI). The specialized nature of OMRI text hinders direct adoption of general ACKG methods and necessitates domain-specific approaches. The rapid evolution of OMRI-specific ACKG renders a review necessary. This paper aims to summarize the latest progress and to guide future ACKG research for OMRI applications. 41 articles from seven databases (2020–August 2025) are analyzed systematically. The review provides an in-depth analysis of design motivations and underlying mechanisms of the methods involved, maps the approaches to challenges from textual characteristics, and proposes a domain-tailored process architecture. Key findings include: (1) adoption of advanced technologies, particularly machine learning, addresses domain challenges and facilitates automation; (2) the “extraction-generation-refinement” workflow forms a reusable roadmap; (3) four key aspects reflect ACKG methods' effectiveness; and (4) remaining challenges include technology coverage, and promising directions include transfer learning.

从文本自动构建知识图谱（ACKG）实现了道路基础设施（OMRI）的智能运营管理。OMRI文本的专门化性质阻碍了一般ACKG方法的直接采用，因此需要特定于领域的方法。omri特异性ACKG的快速发展使得有必要进行综述。本文旨在总结ACKG的最新研究进展，并对未来ACKG在OMRI中的应用提供指导。系统分析了7个数据库（2020 - 2025年8月）的41篇文章。这篇综述提供了对所涉及方法的设计动机和潜在机制的深入分析，绘制了来自文本特征的挑战的方法，并提出了一个领域定制的过程体系结构。主要发现包括：(1)采用先进技术，特别是机器学习，解决领域挑战并促进自动化；(2)“提取-生成-细化”工作流形成可重用的路线图；(3)四个关键方面反映了ACKG方法的有效性；(4)剩下的挑战包括技术覆盖，有希望的方向包括迁移学习。

{"title":"Text-based automatic knowledge graph construction for road infrastructure operations management","authors":"Yafei Sun , Xuesong Shen , Sisi Zlatanova , Khalegh Barati , James Linke","doi":"10.1016/j.autcon.2025.106733","DOIUrl":"10.1016/j.autcon.2025.106733","url":null,"abstract":"<div><div>Automatic construction of knowledge graphs (ACKG) from text enables intelligent operations management of road infrastructure (OMRI). The specialized nature of OMRI text hinders direct adoption of general ACKG methods and necessitates domain-specific approaches. The rapid evolution of OMRI-specific ACKG renders a review necessary. This paper aims to summarize the latest progress and to guide future ACKG research for OMRI applications. 41 articles from seven databases (2020–August 2025) are analyzed systematically. The review provides an in-depth analysis of design motivations and underlying mechanisms of the methods involved, maps the approaches to challenges from textual characteristics, and proposes a domain-tailored process architecture. Key findings include: (1) adoption of advanced technologies, particularly machine learning, addresses domain challenges and facilitates automation; (2) the “extraction-generation-refinement” workflow forms a reusable roadmap; (3) four key aspects reflect ACKG methods' effectiveness; and (4) remaining challenges include technology coverage, and promising directions include transfer learning.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106733"},"PeriodicalIF":11.5,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145813788","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

Multitask unified large vision-language model for post-earthquake structural damage assessment of buildings 地震后建筑物结构损伤评估的多任务统一大视觉语言模型

IF 11.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-12-22 DOI: 10.1016/j.autcon.2025.106720

Yongqing Jiang , Jianze Wang , Xinyi Shen , Kaoshan Dai , Qingzi Ge

Rapid and accurate damage assessment of structures is critical for post-earthquake recovery and emergency response. Current evaluations are heavily reliant on on-site visual inspections conducted by engineering experts, which are time-consuming and resource-intensive. To this end, the large vision-language model (VLM) for multitask structural damage assessment chatbot (MT-SDAChat) is developed in this paper. It can perform both image-level and regional-level inference analysis, accurately locating and providing specific information about various structural components and damage locations. With the MT-SDAChat, a two-stage automated assessment framework that transitions from a global perspective to a component-specific perspective is proposed. A dataset containing 3348 image-text pairs of seismic structural damage with multiple attributes has been constructed. Experimental results show that MT-SDAChat performs well in multitask evaluation. It achieves a question-and-answer accuracy of 82.92 % and a localization accuracy of 78.6 %. These results highlight its strong zero-shot capability across various damage assessments in building construction.

快速准确的结构损伤评估对于震后恢复和应急响应至关重要。目前的评估严重依赖工程专家进行的现场目视检查，这既耗时又耗费资源。为此，本文开发了多任务结构损伤评估聊天机器人（MT-SDAChat）的大视觉语言模型（VLM）。它可以进行图像级和区域级的推理分析，准确定位并提供各种结构部件和损伤位置的具体信息。使用MT-SDAChat，提出了一个从全局视角到特定组件视角的两阶段自动评估框架。构建了包含3348对多属性地震结构损伤的图像-文本数据集。实验结果表明，MT-SDAChat在多任务评估中表现良好。该方法的问答准确率为82.92%，定位准确率为78.6%。这些结果突出了它在各种建筑施工损伤评估中的强大零射击能力。

{"title":"Multitask unified large vision-language model for post-earthquake structural damage assessment of buildings","authors":"Yongqing Jiang , Jianze Wang , Xinyi Shen , Kaoshan Dai , Qingzi Ge","doi":"10.1016/j.autcon.2025.106720","DOIUrl":"10.1016/j.autcon.2025.106720","url":null,"abstract":"<div><div>Rapid and accurate damage assessment of structures is critical for post-earthquake recovery and emergency response. Current evaluations are heavily reliant on on-site visual inspections conducted by engineering experts, which are time-consuming and resource-intensive. To this end, the large vision-language model (VLM) for multitask structural damage assessment chatbot (MT-SDAChat) is developed in this paper. It can perform both image-level and regional-level inference analysis, accurately locating and providing specific information about various structural components and damage locations. With the MT-SDAChat, a two-stage automated assessment framework that transitions from a global perspective to a component-specific perspective is proposed. A dataset containing 3348 image-text pairs of seismic structural damage with multiple attributes has been constructed. Experimental results show that MT-SDAChat performs well in multitask evaluation. It achieves a question-and-answer accuracy of 82.92 % and a localization accuracy of 78.6 %. These results highlight its strong zero-shot capability across various damage assessments in building construction.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106720"},"PeriodicalIF":11.5,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145813790","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

Efficient edge-cloud digital twin for real-time SHM with server-deployed FEA and fast particle swarm optimization 基于服务器部署FEA和快速粒子群优化的高效边缘云数字孪生实时SHM

IF 11.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-12-22 DOI: 10.1016/j.autcon.2025.106718

Yazhou Zhang , Yong Deng , Gongjian Zhou , Jiwei Zhong , Xungang Zhao , Chenxu Zhou

Digital twins (DTs) are increasingly applied throughout the life cycle of structures for health monitoring and decision-making. To meet real-time requirements, this paper presented an efficiency-enhanced DT framework based on an edge-cloud collaborative architecture and three innovations. First, a Python-based finite element (FE) analysis program with a precomputed stiffness matrix strategy significantly accelerated structural load identification and response computation. Second, a deviation severity coefficient with adaptive weighting effectively detected sensor faults and improved identification robustness. Third, a modified particle swarm optimization algorithm with population differentiation and local acceleration strategies further enhanced identification accuracy and efficiency. The system was validated in a bridge pier replacement project under five construction scenarios. The results demonstrated a sub-300 ms total response time, a 70-fold reduction in one-time FE computation, a 70 % decrease in optimization cost, stress prediction errors below 10%, and safety warnings for stress levels exceeding design values by over threefold.

数字孪生（DTs）越来越多地应用于结构的整个生命周期，用于健康监测和决策。为满足实时性需求，本文提出了一种基于边缘云协同架构的高效DT框架，并进行了三项创新。首先，基于python的有限元分析程序采用预先计算刚度矩阵策略，显著加快了结构荷载识别和响应计算。其次，自适应加权偏差严重系数有效检测传感器故障，提高了识别的鲁棒性。第三，结合种群分化和局部加速策略的改进粒子群算法进一步提高了识别的精度和效率。该系统在五种施工方案下的桥梁桥墩更换项目中得到了验证。结果表明，总响应时间低于300 ms，一次性有限元计算减少70倍，优化成本降低70%，应力预测误差低于10%，应力水平超过设计值的安全警告超过三倍。

{"title":"Efficient edge-cloud digital twin for real-time SHM with server-deployed FEA and fast particle swarm optimization","authors":"Yazhou Zhang , Yong Deng , Gongjian Zhou , Jiwei Zhong , Xungang Zhao , Chenxu Zhou","doi":"10.1016/j.autcon.2025.106718","DOIUrl":"10.1016/j.autcon.2025.106718","url":null,"abstract":"<div><div>Digital twins (DTs) are increasingly applied throughout the life cycle of structures for health monitoring and decision-making. To meet real-time requirements, this paper presented an efficiency-enhanced DT framework based on an edge-cloud collaborative architecture and three innovations. First, a Python-based finite element (FE) analysis program with a precomputed stiffness matrix strategy significantly accelerated structural load identification and response computation. Second, a deviation severity coefficient with adaptive weighting effectively detected sensor faults and improved identification robustness. Third, a modified particle swarm optimization algorithm with population differentiation and local acceleration strategies further enhanced identification accuracy and efficiency. The system was validated in a bridge pier replacement project under five construction scenarios. The results demonstrated a sub-300 ms total response time, a 70-fold reduction in one-time FE computation, a 70 % decrease in optimization cost, stress prediction errors below 10%, and safety warnings for stress levels exceeding design values by over threefold.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106718"},"PeriodicalIF":11.5,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823254","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

Asphalt mixture compaction based on motion sensing intelligent aggregate (MSIA) 基于体感智能骨料的沥青混合料压实

IF 11.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-12-22 DOI: 10.1016/j.autcon.2025.106726

Xuejiao Cheng, Fangyuan Gong, Longbang Qing

Motion sensing intelligent aggregates (MSIAs) enable the real-time acquisition of asphalt mixture internal dynamic response data during pavement compaction, providing detailed information on aggregate motion within the compacted layer. These data support the development of an accurate evaluation feedback system for optimizing the field compaction strategy. Therefore, MSIAs can potentially promote the automation and high-quality development of road compaction construction. This paper aims to elucidate the current applications and prospects of MSIAs in pavement compaction research. Using bibliometric statistics of pavement compaction, research hotspots related to technical approaches, material composition, and methodological advances are systematically reviewed. Recommendations are proposed based on the summary of MSIA development, data analysis, and application in asphalt mixture compaction. Future research is needed to minimize size effect influences, develop prediction models, and advance array-arrangement designs of MSIAs that align with the complex conditions of asphalt mixture compaction.

运动感应智能骨料（MSIAs）能够实时获取沥青混合料在路面压实过程中的内部动态响应数据，提供压实层内骨料运动的详细信息。这些数据支持开发准确的评估反馈系统，以优化现场压实策略。因此，msia可以潜在地促进道路压实施工的自动化和高质量发展。本文旨在阐述MSIAs在路面压实研究中的应用现状及前景。运用文献计量统计学方法，系统评述了路面压实的技术途径、材料组成和方法进展等方面的研究热点。总结了MSIA的发展、数据分析和在沥青混合料压实中的应用，提出了建议。未来的研究需要最小化尺寸效应的影响，开发预测模型，并推进与沥青混合料压实复杂条件相一致的MSIAs阵列排列设计。

引用次数: 0

Excavator trajectory planning via global probabilistic learning from expert demonstrations 基于专家演示的全局概率学习的挖掘机轨迹规划

IF 11.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-12-20 DOI: 10.1016/j.autcon.2025.106736

Chenlong Feng , Quan Zhang , Jixin Wang , Xinxing Liu , Yuying Shen , Qingzheng Jia , Jiazhi Zhao

Excavator trajectory planning remains challenging due to dependence on expert skill, changing tasks, and complex environments. This paper integrates global probabilistic modeling of expert demonstrations with sampling-based optimization to enable flexible, efficient, and safe autonomous operation. A Global Modulated Movement Primitive (GMMP) model captures global evolution of expert demonstration trajectories in SE(3) space, the 3D rigid-body pose space that combines orientation and translation. A Bayesian update supports efficient task generalization by adjusting new via points. The workspace density of excavator is introduced to enable the transfer of GMMP across different excavator without retraining. A Guided Model Predictive Path Integral (GMPPI) method with SE(3)-consistency cost optimizes GMMP generated trajectories via sampling, handling obstacle avoidance and execution constraints. The method was validated on a full-size excavator and a scaled platform. Results show improved trajectory similarity, execution efficiency, and task adaptability, indicating strong practicality.

由于对专家技能的依赖、不断变化的任务和复杂的环境，挖掘机轨迹规划仍然具有挑战性。本文将专家演示的全局概率建模与基于采样的优化相结合，实现灵活、高效、安全的自主运行。全局调制运动原语（GMMP）模型捕获了SE(3)空间中专家演示轨迹的全局演化，SE(3)空间是结合了方向和平移的3D刚体姿态空间。贝叶斯更新通过调整新的通过点来支持有效的任务泛化。引入挖掘机的工作空间密度，使GMMP在不同挖掘机之间的转移无需再培训。一种具有SE(3)-一致性代价的引导模型预测路径积分（GMPPI）方法通过采样、避障处理和执行约束对GMPPI生成的轨迹进行优化。该方法在一台全尺寸挖掘机和一个规模化平台上进行了验证。结果表明，该方法提高了轨迹相似度、执行效率和任务适应性，具有较强的实用性。

{"title":"Excavator trajectory planning via global probabilistic learning from expert demonstrations","authors":"Chenlong Feng , Quan Zhang , Jixin Wang , Xinxing Liu , Yuying Shen , Qingzheng Jia , Jiazhi Zhao","doi":"10.1016/j.autcon.2025.106736","DOIUrl":"10.1016/j.autcon.2025.106736","url":null,"abstract":"<div><div>Excavator trajectory planning remains challenging due to dependence on expert skill, changing tasks, and complex environments. This paper integrates global probabilistic modeling of expert demonstrations with sampling-based optimization to enable flexible, efficient, and safe autonomous operation. A Global Modulated Movement Primitive (GMMP) model captures global evolution of expert demonstration trajectories in SE(3) space, the 3D rigid-body pose space that combines orientation and translation. A Bayesian update supports efficient task generalization by adjusting new via points. The workspace density of excavator is introduced to enable the transfer of GMMP across different excavator without retraining. A Guided Model Predictive Path Integral (GMPPI) method with SE(3)-consistency cost optimizes GMMP generated trajectories via sampling, handling obstacle avoidance and execution constraints. The method was validated on a full-size excavator and a scaled platform. Results show improved trajectory similarity, execution efficiency, and task adaptability, indicating strong practicality.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106736"},"PeriodicalIF":11.5,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785094","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

Integrating metaheuristic optimization algorithms with random forest to predict waste generation in construction and demolition projects 结合随机森林的元启发式优化算法预测建筑和拆除工程中的废物产生

IF 11.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-12-19 DOI: 10.1016/j.autcon.2025.106732

Ruba Awad , Cenk Budayan , Idil Calik , Aslı Pelin Gurgun , Kerim Koc

The construction sector is a significant source of global waste, making accurate and proactive prediction of Construction and Demolition Waste (C&DW) essential for sustainable resource management and circular economy efforts. However, estimating C&DW at the project level remains a major challenge. This paper investigates whether C&DW prediction accuracy can be enhanced by integrating the Random Forest (RF) model with two metaheuristic optimization algorithms: the Archimedes Optimization Algorithm (AOA) and Grey Wolf Optimization (GWO). Based on data from 200 real-world projects in Palestine, the GWO-RF model achieved the highest predictive accuracy using only four input variables: project type, start date, building type, and number of floors. To ensure model transparency, Shapley Additive Explanations (SHAP) analysis confirmed that project type and the number of floors were the most influential parameters. This study thus provides a practical, robust, and highly accurate model to support effective waste management strategies in the construction industry.

建筑业是全球废物的重要来源，因此对建筑和拆除废物（C&；DW）进行准确和主动的预测对于可持续资源管理和循环经济的努力至关重要。然而，在项目级别估计C&；DW仍然是一个主要的挑战。本文研究了随机森林（RF）模型与阿基米德优化算法（AOA）和灰狼优化算法（GWO）两种元启发式优化算法相结合，能否提高C&；DW的预测精度。基于巴勒斯坦200个实际项目的数据，GWO-RF模型仅使用四个输入变量（项目类型、开始日期、建筑类型和楼层数）就实现了最高的预测精度。为了确保模型的透明度，Shapley加性解释（SHAP）分析证实，项目类型和楼层数量是最具影响力的参数。因此，本研究提供了一个实用、稳健和高度准确的模型，以支持建筑行业有效的废物管理策略。

{"title":"Integrating metaheuristic optimization algorithms with random forest to predict waste generation in construction and demolition projects","authors":"Ruba Awad , Cenk Budayan , Idil Calik , Aslı Pelin Gurgun , Kerim Koc","doi":"10.1016/j.autcon.2025.106732","DOIUrl":"10.1016/j.autcon.2025.106732","url":null,"abstract":"<div><div>The construction sector is a significant source of global waste, making accurate and proactive prediction of Construction and Demolition Waste (C&DW) essential for sustainable resource management and circular economy efforts. However, estimating C&DW at the project level remains a major challenge. This paper investigates whether C&DW prediction accuracy can be enhanced by integrating the Random Forest (RF) model with two metaheuristic optimization algorithms: the Archimedes Optimization Algorithm (AOA) and Grey Wolf Optimization (GWO). Based on data from 200 real-world projects in Palestine, the GWO-RF model achieved the highest predictive accuracy using only four input variables: project type, start date, building type, and number of floors. To ensure model transparency, Shapley Additive Explanations (SHAP) analysis confirmed that project type and the number of floors were the most influential parameters. This study thus provides a practical, robust, and highly accurate model to support effective waste management strategies in the construction industry.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"182 ","pages":"Article 106732"},"PeriodicalIF":11.5,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786090","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