Automation in Construction最新文献

英文中文

Few-shot GAN adaptation for high-fidelity and diverse crack image generation in dam damage detection 基于GAN的大坝损伤检测中高保真、多样化裂纹图像生成方法

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

Automation in Construction

Pub Date : 2026-01-20 DOI: 10.1016/j.autcon.2026.106789

Mingchao Li , Zuguang Zhang , Qiubing Ren , Yantao Yu , Jingyue Yuan , Jiamei Ma

Substantial crack imagery is hard to acquire in dam structural inspection due to high costs and risks. Crack image generation, as a crucial yet challenging visual task, still struggles with the quality-diversity trade-off under data scarcity. This paper thus presents CrackFSGAN, a few-shot Generative Adversarial Network (GAN) adaptation method for generating realistic, diverse dam crack images from limited samples. It incorporates the Cross-Scale Channel Interaction (CSCI) module to ensure robust gradient flow across network weights for efficient training, and the Self-Supervised Discriminator (SSDr), a redesigned feature-encoder with an additional decoder, to learn more discriminative, region-extensive feature maps. Extensive experiments on multiple damage datasets against state-of-the-art GANs validate CrackFSGAN's superiority in few-shot image synthesis quality and diversity, and its effectiveness in data augmentation for downstream crack detection tasks. Notably, it supports high-resolution (1024 × 1024 pixel²) crack image generation, offering a promising solution to data scarcity and advancing intelligent structural damage detection.

在大坝结构检测中，由于成本高、风险大，难以获得实质性裂缝图像。裂缝图像生成作为一项重要而又具有挑战性的视觉任务，在数据稀缺的情况下，仍然面临着质量-多样性权衡的问题。因此，本文提出了一种基于生成对抗网络（GAN）的自适应方法——CrackFSGAN，用于从有限的样本中生成逼真的、多样化的大坝裂缝图像。它结合了跨尺度通道交互（CSCI）模块，以确保跨网络权值的鲁棒梯度流，以实现高效训练，以及自监督鉴别器（SSDr），一个重新设计的带有额外解码器的特征编码器，以学习更具判别性的、区域扩展的特征映射。在多个损伤数据集上对最先进的gan进行了大量实验，验证了CrackFSGAN在少镜头图像合成质量和多样性方面的优势，以及它在下游裂纹检测任务的数据增强方面的有效性。值得注意的是，它支持高分辨率（1024 × 1024 pixel2）的裂纹图像生成，为解决数据稀缺问题和推进结构损伤智能检测提供了有希望的解决方案。

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引用次数: 0

Adaptive planning of multi-UAV refined inspection path for complex and irregular building clusters 复杂不规则建筑群多无人机精细巡检路径自适应规划

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

Automation in Construction

Pub Date : 2026-01-19 DOI: 10.1016/j.autcon.2026.106787

Penglu Chen , Yi Tan , Wen Yi

Amid rapid global urbanization, cities have shifted into a predominantly building maintenance-oriented phase. Therefore, given that existing studies focus on inspecting simple standalone buildings with single UAV, this paper proposes an automatic path planning method for the refined inspection of complex, irregular building clusters. First, an adaptive layering mechanism is introduced to generate full coverage inspection points based on the structural characteristics of the 3D building cluster model. Initial obstacle free flight paths are then derived by integrating A* and greedy algorithms. Further path optimization is conducted by applying the 2-opt algorithm to eliminate intersections and reduce flight distance, while the DP (Douglas Peucke) algorithm is employed simplified the trajectory by reducing redundant waypoints. Experimental validation on six irregularly shaped buildings demonstrates a 9.6% reduction in flight path length and a 47.7% decrease in intermediate waypoints. The proposed framework enables refined inspection path planning for building clusters, improving the automation level and practical applicability of multi-UAVs based building operation and maintenance.

在全球快速城市化的背景下，城市已经进入了以建筑维护为主的阶段。因此，针对现有研究多集中于单个无人机对简单独立建筑的检测，本文提出了一种自动路径规划方法，用于复杂、不规则建筑集群的精细化检测。首先，引入自适应分层机制，根据三维建筑集群模型的结构特征生成全覆盖检测点；然后通过A*算法和贪心算法的结合得到初始无障碍飞行路径。进一步采用2-opt算法进行路径优化，消除交叉点，减小飞行距离，采用DP （Douglas Peucke）算法减少冗余航路点，简化轨迹。在六个不规则形状建筑物上的实验验证表明，飞行路径长度减少了9.6%，中间航路点减少了47.7%。提出的框架能够细化建筑集群的巡检路径规划，提高多无人机建筑运维的自动化水平和实用性。

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引用次数: 0

Construction site fall hazard identification and automated captioning using adapted vision-language models 使用自适应视觉语言模型的建筑工地坠落危险识别和自动字幕

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

Automation in Construction

Pub Date : 2026-01-19 DOI: 10.1016/j.autcon.2026.106790

Yongshuang Li, Feng Xu, Zhipeng Zhang, Xinyu Mei, He Huang

Falls are the primary safety hazard in construction, with traditional manual inspections being inefficient and error-prone, and existing computer vision methods lacking generalization in complex scenarios. This paper presents the Construction Safety Vision-Language Model (CS-VLM), a framework for construction site fall hazard identification and automated captioning, which integrates ModelScope Swift (MS-Swift) adapters and Low-Rank Adaptation (LoRA) technology for efficient fine-tuning of the Qwen2.5-7B-Instruct model. To support model training, a standardized image-text dataset for fall hazards is constructed using a Bidirectional Encoder Representations from Transformers (BERT) -based natural language conversion method. Experimental results demonstrate that CS-VLM achieves a Consensus-based Image Description Evaluation (CIDEr) score of 1.324, Semantic Propositional Image Caption Evaluation (SPICE) score of 0.391, and hazard identification F1-score of 90.2%, outperforming state-of-the-art methods in complex scenario adaptability while reducing computational costs. This research enables precise, standardized hazard description generation, facilitating proactive safety management and accident prevention in construction environments.

坠落是建筑施工中的主要安全隐患，传统的人工检查效率低下且容易出错，现有的计算机视觉方法在复杂场景下缺乏通用性。本文提出了建筑安全视觉语言模型（CS-VLM），这是一个用于建筑现场坠落危险识别和自动字幕的框架，它集成了ModelScope Swift （MS-Swift）适配器和低秩自适应（LoRA）技术，用于对qwen2.5 - 7b - directive模型进行有效微调。为了支持模型训练，使用基于变形金刚双向编码器表示（BERT）的自然语言转换方法构建了跌倒危险的标准化图像-文本数据集。实验结果表明，CS-VLM在基于共识的图像描述评价（CIDEr）得分为1.324，语义命题图像标题评价（SPICE）得分为0.391，危害识别f1得分为90.2%，在降低计算成本的同时，在复杂场景适应性方面优于现有方法。这项研究使精确、标准化的危险描述生成，促进建筑环境中的主动安全管理和事故预防。

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引用次数: 0

Generic optimization of cross-layer pavement compaction quality using multi-domain intelligent compaction measurement values 基于多域智能压实测量值的跨层路面压实质量通用优化

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

Automation in Construction

Pub Date : 2026-01-19 DOI: 10.1016/j.autcon.2026.106764

Xuefei Wang , Jiaxue Yuan , Jiale Li , Jianmin Zhang , Guowei Ma

As a critical indicator for evaluating road compaction quality, the Intelligent Compaction Measurement Value (ICMV) still suffers from significant scene dependency and the absence of a unified material-structure coupled evaluation framework, particularly in cross-layer compaction assessment. This paper develops a multi-domain analytical framework that integrates vibration signal time, frequency, and time-frequency features based on field data collected from typical road structures, including soil subgrade, cement-stabilized base layer, and asphalt layers. Rolling pass tracking, compactness prediction modeling, and Shapley additive explanations (SHAP) are employed to identify the generic ICMV applicable to pavement structural layers. Furthermore, comparative analyses are conducted to examine the numerical characteristics and vibration response behaviors of the generic ICMV across various structural layers. Finally, a statistically driven stepwise method is applied to determine the engineering ranges of the generic ICMV, thereby establishing a theoretical paradigm for multi-layer intelligent compaction standards and contributing to the digital transformation of pavement engineering.

作为评价道路压实质量的重要指标，智能压实测量值（ICMV）存在着严重的场景依赖性和缺乏统一的材料-结构耦合评价框架的问题，特别是在跨层压实评价中。本文开发了一个多域分析框架，该框架基于从典型道路结构（包括土壤路基、水泥稳定基层和沥青层）收集的现场数据，集成了振动信号的时间、频率和时频特征。采用滚道跟踪、密实度预测模型和Shapley加性解释（SHAP）等方法，确定了适用于路面结构层的通用ICMV。在此基础上，对通用ICMV在不同结构层间的数值特性和振动响应行为进行了对比分析。最后，采用统计驱动的逐步方法确定通用ICMV的工程范围，从而建立多层智能压实标准的理论范式，为路面工程的数字化转型做出贡献。

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引用次数: 0

Physics-informed diffusion for visible-to-infrared domain translation of pavement crack images 路面裂缝图像可见到红外域转换的物理信息扩散

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

Automation in Construction

Pub Date : 2026-01-19 DOI: 10.1016/j.autcon.2026.106780

Zhikai Su , Mengnan Shi , Tianyu Gao , Jiaqi Hao , Hongtao Li , Qiang Yao

Infrared imaging is effective for pavement-crack detection under low-illumination conditions, but the scarcity of infrared datasets hinders its broader adoption. This paper proposes a Physics-Informed Diffusion Model to convert readily available visible-light crack images into physically consistent infrared images. The model integrates physical constraints within a Latent Diffusion Model and employs a Channel-Adaptive Dynamic Gamma Correction (CDGC) method to enhance thermally relevant feature representation. Experiments on a ground-truth infrared test set demonstrate that synthetic data generated by the proposed method substantially improves segmentation performance, achieving Pixel Accuracy (PA) of 0.9678 and Frequency-Weighted IoU (FW-IoU) of 0.9459. By obviating the costly, labor-intensive process of infrared dataset collection, the proposed approach facilitates the widespread adoption of infrared machine vision and visible–infrared fusion systems.

红外成像对于低照度条件下的路面裂缝检测是有效的，但红外数据集的稀缺性阻碍了其广泛应用。本文提出了一种物理信息扩散模型，将现成的可见光裂纹图像转换为物理一致的红外图像。该模型在潜在扩散模型中集成了物理约束，并采用通道自适应动态伽玛校正（CDGC）方法来增强热相关特征表示。在红外地面真值测试集上的实验表明，该方法生成的合成数据显著提高了分割性能，像素精度（PA）达到0.9678，频率加权IoU （FW-IoU）达到0.9459。通过避免昂贵、劳动密集型的红外数据集收集过程，该方法促进了红外机器视觉和可见-红外融合系统的广泛采用。

引用次数: 0

Construction productivity and digital technologies 建筑生产力和数字技术

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

Automation in Construction

Pub Date : 2026-01-19 DOI: 10.1016/j.autcon.2026.106768

Zijian Wang , Ronen Barak , Rafael Sacks , Sitsofe K. Yevu , Arnon Bentur , Georgios M. Hadjidemetriou

Although digital technologies are increasingly studied in construction, their specific impacts on productivity remain partially understood. This review aims to investigates the relationship between digital technologies and construction productivity. The methodology comprises a bibliometric analysis and a systematic literature review of studies published over the past decade. Scopus was selected as the primary database for data retrieval, with 346 publications across 16 journals being identified and analyzed. The bibliometric analysis reveals publication trends and technology interrelations, highlighting AI and optimization as central to a cohesive ecosystem involving BIM, digital twins, sensors, and robotics. The systematic literature review is structured around categorising the use of technology for productivity into four dimensions: measurement, estimation, optimisation, and enhancement. Despite this qualitative synthesis being influenced by the authors' judgement and subjectivity, it highlights the practical benefits such as improved prediction and automation, alongside challenges including data standardization, integration, and workforce adaptation.

尽管数字技术在建筑领域的研究越来越多，但它们对生产力的具体影响仍不完全清楚。本文旨在探讨数字技术与建筑生产率之间的关系。该方法包括文献计量分析和对过去十年发表的研究进行系统的文献综述。选择Scopus作为数据检索的主数据库，对16种期刊的346篇出版物进行了识别和分析。文献计量分析揭示了出版趋势和技术之间的相互关系，强调人工智能和优化是涉及BIM、数字双胞胎、传感器和机器人技术的凝聚力生态系统的核心。系统的文献综述围绕着将技术用于生产力的使用分为四个维度：测量、估计、优化和增强。尽管这种定性综合受到作者的判断和主观性的影响，但它强调了实际的好处，如改进的预测和自动化，以及包括数据标准化、集成和劳动力适应在内的挑战。

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引用次数: 0

Automated construction progress monitoring and control through AI-based image recognition and BIM integration 通过基于ai的图像识别和BIM集成实现施工进度自动化监控

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

Automation in Construction

Pub Date : 2026-01-19 DOI: 10.1016/j.autcon.2026.106783

Chang-Cheng Hsieh, Hung-Ming Chen, Wan-Yu Chen, Ting-Yu Wu

This paper integrates AI image recognition and BIM technology to develop a prototype system that achieves automation and visualization of construction progress control. The system supports using a BIM model for planning deployment of multiple surveillance cameras to encompass the entire construction site. The real-time images captured by these cameras are processed using object detection technology to locate all actively constructed objects in the images and identify their respective construction phases. By integrating the perspectives of these cameras into the BIM model, the AI detection results from each camera image are automatically inputted into corresponding components of the BIM model. Subsequently, the real-time site progress information stored in the BIM model is compared with the planned schedule, and the comparative results are visually presented on the BIM model components in different colors. Through visualization, this approach enables management personnel to control progress in a specific and intuitive manner in real-time.

本文将AI图像识别与BIM技术相结合，开发了一个实现施工进度控制自动化、可视化的原型系统。系统支持使用BIM模型规划部署多个监控摄像头，覆盖整个施工现场。这些摄像机捕获的实时图像使用目标检测技术进行处理，定位图像中所有正在施工的物体，并识别其各自的施工阶段。通过将这些摄像头的视角整合到BIM模型中，每个摄像头图像的AI检测结果自动输入到BIM模型的相应组件中。随后，将BIM模型中存储的现场实时进度信息与计划进度进行对比，并将对比结果以不同颜色直观地呈现在BIM模型组件上。通过可视化，管理人员可以实时、具体、直观地控制进度。

引用次数: 0

Edge device-based vibration signal processing and convolutional neural networks for mining dumper activity recognition 基于边缘设备的振动信号处理和卷积神经网络的矿用自卸车活动识别

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

Automation in Construction

Pub Date : 2026-01-19 DOI: 10.1016/j.autcon.2026.106785

Nagesh Dewangan, Amiya Ranjan Mohanty

Previous research on mining dumper activity recognition has rarely explored edge devices for classifying processed vibration signals from deployed Convolutional Neural Network (CNN) models. Most studies have relied on remote or cloud-based platforms, limiting applicability in mines due to unreliable connectivity. This paper introduces an on-device classification approach for dumper activities from processed vibration signals by deploying trained CNN models on edge devices. Vibration signals collected were processed using signal processing methods to extract distinct features for classification and validated through SHAP 3D surface visualization. Among tested models, the combination of ResNet50 with DWT-GT achieved optimal performance, delivering 99.23% accuracy with low computational complexity. Deployment on resource-constrained devices demonstrated feasibility of edge-based computation, where BeagleBone AI-64 achieved 67.46% lower CPU time. These findings establish the feasibility of edge devices for real-time dumper activity recognition, eliminating dependency on external platforms and enhancing operational efficiency in mining environments.

以往关于矿用自卸车活动识别的研究很少探索利用卷积神经网络（CNN）模型对处理后的振动信号进行分类的边缘设备。大多数研究都依赖于远程或基于云的平台，由于连接不可靠，限制了在矿山中的适用性。本文介绍了一种通过在边缘设备上部署训练好的CNN模型，从处理过的振动信号中对翻车机活动进行设备上分类的方法。采用信号处理方法对采集到的振动信号进行处理，提取明显特征进行分类，并通过SHAP三维表面可视化进行验证。在测试的模型中，ResNet50与DWT-GT的组合获得了最佳性能，准确率达到99.23%，计算复杂度较低。在资源受限设备上的部署证明了边缘计算的可行性，其中BeagleBone AI-64的CPU时间降低了67.46%。这些发现确定了边缘设备用于实时卸料器活动识别的可行性，消除了对外部平台的依赖，提高了采矿环境中的操作效率。

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引用次数: 0

Real-time knowledge management for construction value engineering: Live capture and BERT-aided case-based retrieval 建筑价值工程的实时知识管理：实时捕获和bert辅助的基于案例的检索

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

Automation in Construction

Pub Date : 2026-01-17 DOI: 10.1016/j.autcon.2026.106782

Fuhao Zu , Xueqing Zhang

Effective reuse of creative ideas from value engineering (VE) workshops is crucial for cost-effective, innovative design. Conventional methods like post-project reviews and keyword searches often lack context, real-time availability, and semantic relevance, limiting the practical reuse of past insights. This paper addresses the fundamental question of how knowledge generated during VE workshops can be effectively captured and reused to support future idea generations. To solve this, it proposes an integrated methodology combining BIM-based live capture with a hybrid retrieval system. This system uses structured attributes and Bidirectional Encoder Representations from Transformers (BERT) based semantic similarity to ensure context-aware reuse. A prototype Revit plug-in was developed for structured capture and semantic search. Evaluation demonstrated strong performance, superiority over baseline methods, and high user acceptance. This paper provides a practical framework and tool for structured documentation and intelligent knowledge reuse, thereby enhancing creativity support for construction VE practices.

有效地重用来自价值工程（VE）车间的创意对于具有成本效益的创新设计至关重要。传统的方法，如项目后审查和关键字搜索，通常缺乏上下文、实时可用性和语义相关性，限制了对过去见解的实际重用。本文解决了如何有效地捕获和重用在VE研讨会期间生成的知识以支持未来的想法生成的基本问题。为了解决这一问题，本文提出了一种基于bim的实时捕获与混合检索系统相结合的集成方法。该系统使用结构化属性和基于语义相似度的双向编码器表示（BERT）来确保上下文感知重用。开发了用于结构化捕获和语义搜索的原型Revit插件。评估显示了强大的性能，优于基线方法，并且用户接受度高。本文为结构化文档和智能知识重用提供了一个实用的框架和工具，从而增强了对构建VE实践的创造性支持。

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引用次数: 0

Stakeholder-centric whole-lifecycle framework for guiding the development and implementation of construction digital twins 以利益相关者为中心的全生命周期框架，用于指导建筑数字孪生的开发和实施

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

Automation in Construction

Pub Date : 2026-01-17 DOI: 10.1016/j.autcon.2026.106773

Wahib Saif , Omar Doukari , Mohamad Kassem

Construction Digital Twins (CDTs) are increasingly recognised for their potential to improve construction project management. However, successful implementation requires more than just deploying technology; it demands a stakeholder-centric, whole-system lifecycle approach. Existing frameworks are largely technocentric, focusing on technical demonstrations in isolated use cases and offering limited guidance on stakeholders' roles, interactions, and system lifecycle considerations. To address these gaps, this paper introduces a socio-technical CDT framework spanning five lifecycle stages: Define, Design, Deploy, Refine, and Decommission. Grounded in an eight-month longitudinal industrial case study and informed by a CDT triad taxonomy (applications, data, technologies), the framework guides CDT development and maps stakeholder engagement throughout its lifecycle. Stakeholders are categorised into four actor groups: Strategic, Advisory, Technical, and Operational, whose interdependencies are conceptualised through an actor role model. The framework extends CDT applicability beyond controlled demonstrations to real project contexts, while emphasising the need for validation across diverse organisational settings.

建筑数字孪生（CDTs）因其改善建筑项目管理的潜力而日益受到认可。然而，成功的实施需要的不仅仅是部署技术；它需要一个以涉众为中心的全系统生命周期方法。现有框架在很大程度上是以技术为中心的，关注于孤立用例中的技术演示，并提供有关涉众角色、交互和系统生命周期考虑的有限指导。为了解决这些差距，本文介绍了一个跨越五个生命周期阶段的社会技术CDT框架：定义、设计、部署、改进和退役。该框架以为期8个月的纵向工业案例研究为基础，并以CDT三元分类（应用程序、数据、技术）为依据，指导CDT开发，并在其整个生命周期中绘制涉众参与的地图。涉众被分为四个行动者组：战略、咨询、技术和运营，其相互依赖关系通过行动者角色模型概念化。该框架将CDT的适用性从受控的演示扩展到实际的项目环境，同时强调了跨不同组织设置进行验证的必要性。

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引用次数: 0

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