Automation in Construction最新文献

英文中文

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。

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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。

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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。

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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。

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引用次数: 0

Sustainability in civil construction through industry 4.0 and BIM technologies 通过工业4.0和BIM技术实现民用建筑的可持续性

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.2025.106729

Jordana de Oliveira, Dusan Schreiber, Vanusca Dalosto Jahno

Despite the potential benefits of managing buildings as material banks (BAMB) through circular economy practices, several operational challenges remain. The integration of technologies such as Building Information Modeling (BIM) and Industry 4.0 (I4.0) offers a promising pathway to address these barriers. This paper aims to evaluate the current state of research on the application of BIM and I4.0 technologies to promote sustainability in civil construction, with a particular focus on their alignment with the BAMB concept. A systematic literature review is conducted, analyzing 151 peer-reviewed articles published between 2014 and 2024 from the Scopus and Web of Science databases. The findings indicate that the use of BIM and I4.0 technologies contributes positively to all three pillars of sustainability. Based on the analysis, a conceptual framework is developed to support the implementation of the BAMB model, incorporating BIM and I4.0 technologies across all phases of the building life cycle.

尽管通过循环经济实践将建筑管理为材料库（BAMB）有潜在的好处，但仍存在一些运营挑战。建筑信息模型（BIM）和工业4.0 （I4.0）等技术的集成为解决这些障碍提供了一条有希望的途径。本文旨在评估BIM和I4.0技术应用的研究现状，以促进民用建筑的可持续性，并特别关注它们与BAMB概念的一致性。我们进行了系统的文献综述，分析了2014年至2024年间在Scopus和Web of Science数据库中发表的151篇同行评议文章。研究结果表明，BIM和工业4.0技术的使用对可持续发展的三大支柱都有积极的贡献。基于分析，开发了一个概念性框架来支持BAMB模型的实施，将BIM和I4.0技术纳入建筑生命周期的所有阶段。

引用次数: 0

Digital twin–enabled real-time control of tunnel boring machines using deep reinforcement learning for cumulative settlement management 利用深度强化学习进行累积沉降管理的隧道掘进机数字双启用实时控制

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.2025.106725

Guangcan Sun , Fei Lyu , Jinglin Fan , Qiujing Pan

The increasing complexity of urban tunneling requires the optimization of TBM operational parameters to ensure excavation stability and effective ground control. This paper introduces a Deep Reinforcement Learning (DRL)-based methodology that integrates geological conditions and settlement status within a structured decision-making framework. A tailored reward function is designed to simultaneously address stability, settlement, and cost. Furthermore, the incorporation of Monte Carlo Tree (MCT) search enhances the decision-making process by improving foresight. A digital twin, constructed from sparse geotechnical data, models the geological conditions and settlement accumulation, thus facilitating the virtual training of the RL agent. When applied to the Nanjing Metro Line 11 project in China, the proposed method effectively captures the intricate relationship between TBM parameters and ground response. Results indicate that the DRL-based approach significantly minimizes settlement and outperforms the NSGA-II algorithm in optimization performance. This paper demonstrates the significant potential of DRL-driven strategies for intelligent and adaptive tunneling control.

随着城市隧道工程的日益复杂，需要对掘进机的运行参数进行优化，以保证开挖的稳定性和有效的地面控制。本文介绍了一种基于深度强化学习（DRL）的方法，该方法将地质条件和沉降状态集成到结构化决策框架中。量身定制的奖励功能旨在同时解决稳定性，结算和成本问题。此外，蒙特卡洛树（MCT）搜索的引入通过提高预见性来提高决策过程。利用稀疏的岩土数据构建数字孪生模型，模拟地质条件和沉降积累，从而促进RL代理的虚拟训练。将该方法应用于南京地铁11号线工程，有效地捕捉了隧道掘进机参数与地面响应之间的复杂关系。结果表明，基于drl的方法显著降低了沉降，优化性能优于NSGA-II算法。本文展示了drl驱动策略在智能和自适应隧道控制方面的巨大潜力。

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引用次数: 0

Scalable road infrastructure monitoring using embedded fiber Bragg grating sensors based on wavelet scattering-long short-term memory autoencoder 基于小波散射-长短期记忆自编码器的嵌入式光纤光栅传感器可扩展道路基础设施监测

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

Automation in Construction

Pub Date : 2025-12-18 DOI: 10.1016/j.autcon.2025.106724

Ali Golmohammadi , Vahid Yaghoubi , Navid Hasheminejad , Nasser Ghaderi , Wim Van den bergh , David Hernando

Managing and extracting insights from the large volumes of data generated by optical fiber sensor networks is a major challenge. This paper presents an intelligent, scalable framework for real-time road health monitoring using fiber Bragg grating (FBG) sensor data. The proposed framework reduces reliance on manual data handling and cuts storage needs by over 99 % by constructing a compact health indicator (HI). Data preprocessing and fusion reduce volume and variability, while a wavelet scattering network (WSN) extracts damage-sensitive features that are encoded via a long short-term memory (LSTM) autoencoder to represent the health state. Temperature data is integrated to distinguish structural damage from environmental effects. The approach is evaluated through laboratory fatigue tests and synthetic damage data generated from healthy-state field measurements. Results demonstrate accurate, efficient monitoring with potential for edge deployment, enabling low-cost, real-time, long-term structural health management and representing a significant step toward automated, resource-efficient infrastructure maintenance.

从光纤传感器网络产生的大量数据中管理和提取见解是一项重大挑战。本文提出了一个智能的、可扩展的框架，用于使用光纤布拉格光栅（FBG）传感器数据进行实时道路健康监测。该框架通过构建紧凑的运行状况指标（HI），减少了对人工数据处理的依赖，并将存储需求减少了99%以上。数据预处理和融合减少了体积和可变性，而小波散射网络（WSN）提取损伤敏感特征，通过长短期记忆（LSTM）自编码器编码来表示健康状态。温度数据被整合以区分结构损伤和环境影响。通过实验室疲劳测试和健康状态现场测量产生的综合损伤数据对该方法进行了评估。结果表明，准确、高效的监测具有边缘部署的潜力，可以实现低成本、实时、长期的结构健康管理，是向自动化、资源节能型基础设施维护迈出的重要一步。

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引用次数: 0

Indoor scan-to-BIM automation: From mobile perception to 3D building modelling 室内扫描到bim自动化：从移动感知到3D建筑建模

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

Automation in Construction

Pub Date : 2025-12-18 DOI: 10.1016/j.autcon.2025.106731

Junqi Luo , Zexin Yang , Pengcheng Shi , Qin Ye

Scan-to-BIM is a crucial yet challenging task in intelligent construction, bridging real-world perception and virtual reconstruction. With growing demands for high-fidelity digital twins, its importance is increasingly evident. Unlike prior surveys focusing on isolated components, this review offers an updated and cross-disciplinary overview of the complete indoor Scan-to-BIM workflow, incorporating recent AI-driven advances and available benchmark datasets. First, the relationship between Scan-to-BIM and key AEC modules is clarified. Next, the problem formulation is defined, followed by a discussion of current challenges. Then, commonly used devices and core technologies are reviewed, including mobile LiDAR-based indoor point cloud map generation, point cloud-based architectural semantic segmentation, and indoor architectural element modelling, along with emerging research directions. Finally, existing benchmarking datasets and evaluation metrics for indoor Scan-to-BIM applications are summarized. This review serves as a comprehensive resource for researchers and practitioners in civil engineering, geomatics, and robotics, advancing the understanding and application of Scan-to-BIM.

在智能建筑中，扫描到bim是一项至关重要但具有挑战性的任务，它连接了现实世界的感知和虚拟重建。随着对高保真数字孪生的需求不断增长，其重要性日益明显。不同于之前的调查侧重于孤立的组件，本综述提供了完整的室内扫描到bim工作流的更新和跨学科概述，结合了最新的人工智能驱动的进展和可用的基准数据集。首先，明确了Scan-to-BIM与关键AEC模块之间的关系。接下来，定义问题的表述，然后讨论当前的挑战。然后，对基于移动激光雷达的室内点云地图生成、基于点云的建筑语义分割、室内建筑元素建模等常用设备和核心技术进行了综述，并提出了新兴的研究方向。最后，总结了现有的室内扫描到bim应用的基准数据集和评估指标。这篇综述为土木工程、地理信息学和机器人领域的研究人员和实践者提供了全面的资源，促进了对扫描到bim的理解和应用。

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引用次数: 0

No-reference image quality assessment via degraded-content inference for sewer inspection images 基于退化内容推理的无参考图像质量评价

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

Automation in Construction

Pub Date : 2025-12-17 DOI: 10.1016/j.autcon.2025.106727

Xingyu Chen, Zegen Wang, Jianghai He, Yaowen Ran, Mi Chen, Jiayi Hu, Chaoyue Li

Sewer inspection images captured via CCTV often experience significant quality degradation due to complex pipeline environments and the motion of inspection robots. To address the challenges in quality assessment and support subsequent enhancement or detection tasks, this paper proposes DI-IQA, a no-reference image quality assessment model tailored for sewer inspection scenarios. DI-IQA introduces a degraded content inference (DCI) module based on GANs, guided by dark channel prior and luminance consistency losses, and an image quality regression (IQR) module that integrates features from the generator, discriminator, degraded images, and discrepancy images. Besides, two datasets were constructed for training: the Degraded Sewer Inspection Image Dataset (5350 image pairs) for DCI module, and the Sewer Inspection IQA Dataset (1000 images) for IQR module. Experiments show DI-IQA achieves PLCC 0.934 and SROCC 0.931 on the Sewer Inspection IQA Dataset, demonstrating outstanding performance, and up to PLCC 0.976 and SROCC 0.973 on natural image benchmarks.

由于复杂的管道环境和检测机器人的运动，通过闭路电视捕获的下水道检查图像通常会出现严重的质量下降。为了解决质量评估中的挑战并支持后续的增强或检测任务，本文提出了针对下水道检测场景量身定制的无参考图像质量评估模型DI-IQA。DI-IQA引入了一个基于gan的退化内容推理（DCI）模块，该模块以暗通道先验和亮度一致性损失为指导，以及一个图像质量回归（IQR）模块，该模块集成了生成器、鉴别器、退化图像和差异图像的特征。此外，构建了两个数据集用于训练：DCI模块的退化下水道检查图像数据集（5350对图像）和IQR模块的下水道检查IQA数据集（1000张图像）。实验表明，DI-IQA在下水道检查IQA数据集上达到PLCC 0.934和SROCC 0.931，表现出优异的性能，在自然图像基准上达到PLCC 0.976和SROCC 0.973。

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引用次数: 0

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