Automation in Construction最新文献_第3页

Bridging dual knowledge graphs for multi-hop question answering in construction safety 面向建筑安全多跳问答的桥接双知识图

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

Automation in Construction

Pub Date : 2026-01-26 DOI: 10.1016/j.autcon.2026.106794

Yuxin Zhang, Xi Wang, Mo Hu, Zhenyu Zhang

Information retrieval and question answering from safety regulations are essential for automated construction compliance checking but are hindered by the linguistic and structural complexity of regulatory text. Many queries are multi-hop, requiring synthesis across interlinked clauses. To address the challenge, this paper introduces BifrostRAG, a dual-graph retrieval-augmented generation (RAG) system that models both linguistic relationships and document structure. The proposed architecture supports a hybrid retrieval mechanism that combines graph traversal with vector-based semantic search, enabling large language models to reason over both the content and the structure of the text. On a multi-hop question dataset, BifrostRAG achieves 92.8% precision, 85.5% recall, and an F1 score of 87.3%. These results significantly outperform vector-only and graph-only RAG baselines, establishing BifrostRAG as a robust knowledge engine for LLM-driven compliance checking. The dual-graph, hybrid retrieval mechanism presented in this paper offers a transferable blueprint for navigating complex technical documents across knowledge-intensive engineering domains.

安全法规的信息检索和问题回答对于自动化建筑符合性检查至关重要，但法规文本的语言和结构复杂性阻碍了这一点。许多查询是多跳的，需要在相互连接的子句之间进行合成。为了应对这一挑战，本文介绍了BifrostRAG，这是一个双图检索增强生成（RAG）系统，可以对语言关系和文档结构进行建模。提出的体系结构支持混合检索机制，该机制结合了图遍历和基于向量的语义搜索，使大型语言模型能够对文本的内容和结构进行推理。在多跳问题数据集上，BifrostRAG达到了92.8%的准确率，85.5%的召回率和87.3%的F1分数。这些结果明显优于纯矢量和纯图形的RAG基线，使BifrostRAG成为llm驱动的符合性检查的强大知识引擎。本文提出的双图混合检索机制为跨知识密集型工程领域的复杂技术文档导航提供了可转移的蓝图。

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引用次数: 0

Margin-aware maximum classifier discrepancy for BIM-to-scan semantic segmentation of building point clouds 基于边缘感知的建筑点云bim -扫描语义分割最大分类器差异

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

Automation in Construction

Pub Date : 2026-01-24 DOI: 10.1016/j.autcon.2026.106799

Difeng Hu , You Dong , Mingkai Li , Hanmo Wang , Tao Wang

BIM-derived point clouds are valuable for semantic segmentation and BIM modeling, but distribution discrepancies between BIM and real-world scans significantly degrade segmentation performance. To mitigate this issue, this paper develops a margin-aware maximum classifier discrepancy (MMCD) method, which extends the conventional MCD framework by incorporating a margin-aware mechanism. Task-specific classifiers act as discriminators to encourage the feature generator to learn domain-invariant yet discriminative features for unlabeled real point clouds, improving BIM-to-scan distribution alignment and segmentation accuracy. A margin-aware discrepancy loss is formulated to enforce sufficient margin between features and classification boundaries, improving robustness to domain shift. In addition, a training strategy is proposed to support MMCD optimization. Finally, a refined RandLA-Net with an attention-based upsampling module is constructed as the backbone for validation. Experiments demonstrate that the proposed approach achieves superior performance, with an IoU of 72.79% and an overall accuracy of 87.99%, outperforming RandLA-Net variants with or without MCD.

BIM衍生的点云对于语义分割和BIM建模很有价值，但是BIM和真实扫描之间的分布差异会显著降低分割性能。为了解决这一问题，本文提出了一种边缘感知的最大分类器差异（MMCD）方法，该方法通过引入边缘感知机制扩展了传统的最大分类器差异框架。特定于任务的分类器充当鉴别器，以鼓励特征生成器学习未标记的真实点云的域不变但有区别的特征，从而提高bim到扫描的分布对齐和分割精度。制定了一个边界感知差异损失来强制特征和分类边界之间有足够的边界，提高了对域移位的鲁棒性。此外，提出了一种支持MMCD优化的训练策略。最后，构建了一个改进的基于注意力上采样模块的RandLA-Net作为验证的主干。实验表明，该方法取得了优异的性能，IoU为72.79%，总体准确率为87.99%，优于带或不带MCD的RandLA-Net变体。

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引用次数: 0

Computer vision for infrastructure defect detection: Methods and trends 基础设施缺陷检测的计算机视觉：方法和趋势

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

Automation in Construction

Pub Date : 2026-01-24 DOI: 10.1016/j.autcon.2026.106795

Yufei Zhang , Gang Li , Runjie Shen

Infrastructure defect detection is vital for public safety and sustainable societal development. In recent years, advances in computer vision have gradually promoted the intelligence and automation of infrastructure defect detection. This paper provides a comprehensive overview of research progress and emerging trends in computer vision-based detection of diverse defect types across multiple infrastructure scenarios, including datasets, evaluation metrics, and methods. A classification framework is introduced that centers on single and multiple visual modalities. The former includes traditional image processing, machine learning, and deep learning techniques, reflecting the evolution of the field. The latter focuses on data-level, feature-level, and decision-level fusion strategies, highlighting opportunities to improve detection performance with multiple visual modalities. Methods are further categorized according to their characteristics and model architectures. Finally, existing challenges are summarized, and promising research directions are outlined based on the strengths and limitations of current methods.

基础设施缺陷检测对公共安全和社会可持续发展至关重要。近年来，计算机视觉的进步逐步推动了基础设施缺陷检测的智能化和自动化。本文提供了研究进展的全面概述，以及跨多个基础设施场景的基于计算机视觉的各种缺陷类型检测的新兴趋势，包括数据集、评估度量和方法。介绍了一种以单视觉模态和多视觉模态为中心的分类框架。前者包括传统的图像处理、机器学习和深度学习技术，反映了该领域的发展。后者侧重于数据级、特征级和决策级融合策略，突出了通过多种视觉模式提高检测性能的机会。方法根据其特征和模型架构进一步分类。最后，根据现有方法的优势和局限性，总结了存在的挑战，并展望了未来的研究方向。

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引用次数: 0

GPS-free automated registration of UAV-captured façade image sequences to BIM using semantic key points 使用语义关键点将无人机捕获的farade图像序列自动注册到BIM中，无需gps

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

Automation in Construction

Pub Date : 2026-01-23 DOI: 10.1016/j.autcon.2026.106788

Cong Chen, Shenghan Zhang

Unmanned Aerial Vehicles (UAVs) have emerged as essential tools for building façade inspection. However, due to the repeating patterns on façades, automatically registering images taken by UAV to Building Information Modeling (BIM) models, though important for building maintenance, remains challenging. Existing methods often rely on GPS data, which lack sufficient accuracy in urban environments. This paper proposes a GPS-free automated framework to register UAV-captured image sequences to BIM models by leveraging information from overlapping images. The framework comprises three key components: (1) extracting semantic key points from images using the Grounded SAM 2; (2) implementing a virtual UAV camera model to enable bidirectional projection of key points between BIM coordinates and image coordinates; and (3) developing a particle filter motion model to achieve image-to-BIM registration using image sequences. The proposed method registers various data types to BIM models, including overlapping visual image sequences, infrared (IR)-visual pairs, and façade defects.

无人驾驶飞行器（uav）已成为建筑物外观检查的重要工具。然而，由于立面上的重复模式，自动注册无人机拍摄的图像到建筑信息建模（BIM）模型，虽然对建筑维护很重要，仍然具有挑战性。现有的方法通常依赖于GPS数据，在城市环境中缺乏足够的精度。本文提出了一个无gps的自动化框架，通过利用重叠图像的信息将无人机捕获的图像序列注册到BIM模型。该框架包括三个关键部分：(1)使用ground SAM 2从图像中提取语义关键点；(2)实现虚拟无人机摄像机模型，实现BIM坐标与图像坐标之间关键点的双向投影；(3)开发粒子滤波运动模型，使用图像序列实现图像到bim的注册。该方法将各种数据类型注册到BIM模型中，包括重叠的视觉图像序列，红外(IR)-视觉对和farade缺陷。

引用次数: 0

Project-level automated pavement maintenance and rehabilitation decision-making with data imbalance mitigation and post-maintenance evaluation 基于数据不平衡缓解和养护后评估的项目级自动路面养护和修复决策

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

Automation in Construction

Pub Date : 2026-01-22 DOI: 10.1016/j.autcon.2026.106796

Qingwei Zeng , Shunxin Yang , Chang Xu , Jitong Ding , Qiwei Chen , Guoyang Lu

Pavement management data often suffers from severe class imbalance, and existing project-level maintenance and rehabilitation (M&R) decision-making models generally lack post-maintenance evaluation mechanisms. To address these issues, this paper proposes a project-level automated pavement M&R decision-making framework that considers data imbalance and incorporates post-maintenance evaluation (PMDNN). First, a Conditional Tabular Generative Adversarial Network (CTGAN) is developed to augment imbalanced M&R datasets. Next, two deep neural networks (DNNs) are constructed, for pavement performance prediction and for M&R decision-making, respectively. Finally, these two DNNs are nested to enable post-maintenance evaluation, supporting iterative adjustment of suboptimal M&R plans. Results demonstrate that the CTGAN effectively addresses data imbalance and accurately simulates the distribution of the original data. Compared with other data augmentation models, the CTGAN generates data with 4.7%–18.1% higher quality. Additionally, relative to multiple baseline frameworks, the proposed PMDNN framework achieves a 1.91%–4.71% higher overall decision accuracy. These findings indicate that PMDNN can support pavement management systems in making decisions more closely aligned with expert judgment.

路面管理数据往往存在严重的类失衡，现有的项目级养护与修复（M&；R）决策模型普遍缺乏养护后评价机制。为了解决这些问题，本文提出了一个考虑数据不平衡并结合维护后评估（PMDNN）的项目级自动路面管理决策框架。首先，开发了一种条件表格生成对抗网络（CTGAN）来增强不平衡的M&；R数据集。接下来，构建了两个深度神经网络（dnn），分别用于路面性能预测和M&；R决策。最后，这两个dnn被嵌套以支持维护后评估，支持次优M&；R计划的迭代调整。结果表明，CTGAN有效地解决了数据不平衡问题，并能准确地模拟原始数据的分布。与其他数据增强模型相比，CTGAN生成的数据质量提高了4.7%-18.1%。此外，相对于多个基线框架，所提出的PMDNN框架的总体决策准确率提高了1.91%-4.71%。这些发现表明，pmnn可以支持路面管理系统做出更符合专家判断的决策。

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引用次数: 0

Addressing data scarcity in construction safety monitoring using low-rank adaptation (LoRA)-tuned domain-specific image generation 使用低秩自适应（LoRA）调优的特定领域图像生成解决建筑安全监测中的数据稀缺问题

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

Automation in Construction

Pub Date : 2026-01-21 DOI: 10.1016/j.autcon.2026.106786

Insoo Jeong , Junghoon Kim , Seungmo Lim , Jeongbin Hwang , Seokho Chi

This paper proposes a lightweight domain adaptation framework for construction safety monitoring by fine-tuning a pretrained text-to-image diffusion model using Low-Rank Adaptation (LoRA). To simulate high-risk construction environments underrepresented in training data, the model was adapted to environmental features and specific hazards, focusing on visually dominant scenarios including falls, struck-by, and caught-in incidents. To address data scarcity, Multi-LoRA fine-tuning was conducted using 20 images per hazard type (totaling 60 across three hazards) and 30 background images, enabling both contextual and hazard-specific adaptation. The generated images achieved the highest semantic consistency, yielding the top mean Contrastive Language-Image Pre-training (CLIP) scores with minimal variance, and improved visual realism by reducing the Fréchet Inception Distance (FID) by 86.72 points. Furthermore, a YOLOv8 model trained exclusively on these synthetic images achieved a mean average precision ([email protected]:0.95) of 94.1% on real-world frames, comparable to a baseline model trained on real data.

本文提出了一个轻量级的领域自适应框架，用于建筑安全监测，该框架采用低秩自适应（Low-Rank adaptation, LoRA）对预训练的文本到图像扩散模型进行微调。为了模拟训练数据中未被充分代表的高风险建筑环境，该模型适应了环境特征和特定危害，重点关注视觉上占主导地位的场景，包括跌倒、被撞和被困事故。为了解决数据短缺问题，Multi-LoRA对每种灾害类型使用20张图像（三种灾害共60张）和30张背景图像进行了微调，从而实现了上下文和特定灾害的适应。生成的图像达到了最高的语义一致性，以最小的方差产生最高的平均对比语言图像预训练（CLIP）分数，并通过将fr起始距离（FID）减少86.72分来提高视觉真实感。此外，专门在这些合成图像上训练的YOLOv8模型在真实帧上实现了94.1%的平均精度（[email protected]:0.95），与在真实数据上训练的基线模型相当。

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引用次数: 0

Uncertainty-aware risk mapping with passive WiFi and modified Zonal Safety Analysis (mZSA) in BIM for construction 基于被动WiFi和改进的区域安全分析（mZSA）的BIM中的不确定性风险映射

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

Automation in Construction

Pub Date : 2026-01-21 DOI: 10.1016/j.autcon.2026.106779

Mohamed Elrifaee , Tarek Zayed , Ahmed Mansour , Eslam Ali

Construction sites remain among the most hazardous work environments, where the lack of non-intrusive, worker-independent monitoring systems limits proactive safety management. Compared to existing approaches that rely heavily on wearables, RFID tags, or bespoke infrastructure, this paper presents a passive and non-intrusive framework leveraging WiFi probe request tracking for safety monitoring in semi-open areas with static hazards. Using low-cost TP-Link routers, the proposed system localizes workers without requiring active participation or additional equipment. To improve robustness beyond conventional fingerprinting models, a joint Autoencoder–Transformer architecture is employed to capture latent dependencies among access points, significantly reducing localization uncertainty. The resulting position estimates are integrated into a modified Zonal Safety Analysis (mZSA) framework adapted for semi-open construction zones. Unlike deterministic approaches that overlook error variability, the proposed method incorporates distribution-specific error modeling, enabling confidence-aware risk buffers. The framework provides a scalable, uncertainty-aware pathway for real-time risk detection in semi-open construction environments.

建筑工地仍然是最危险的工作环境之一，缺乏非侵入式的、独立于工人的监控系统，限制了主动的安全管理。与现有的严重依赖可穿戴设备、RFID标签或定制基础设施的方法相比，本文提出了一种被动的非侵入式框架，利用WiFi探针请求跟踪在具有静态危险的半开放区域进行安全监控。使用低成本的TP-Link路由器，所提出的系统无需积极参与或额外的设备就可以使工作人员本地化。为了提高传统指纹识别模型的鲁棒性，采用了一种联合的Autoencoder-Transformer架构来捕获接入点之间的潜在依赖关系，显著降低了定位的不确定性。由此产生的位置估计被整合到一个改进的区域安全分析（mZSA）框架中，该框架适用于半开放的建筑区域。与忽略误差可变性的确定性方法不同，所提出的方法结合了特定于分布的误差建模，从而实现了信心感知风险缓冲。该框架为半开放式施工环境中的实时风险检测提供了一种可扩展的、不确定性感知的途径。

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引用次数: 0

Automated robotic deployment of distributed fiber optic sensing for construction monitoring 分布式光纤传感用于施工监测的自动化机器人部署

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

Automation in Construction

Pub Date : 2026-01-21 DOI: 10.1016/j.autcon.2026.106793

Tzu-Hsuan Lin , Sheng-Hong Wu , Yu-Chen Su , Alan Putranto

Distributed fiber optic sensing (DFOS) enables continuous strain and temperature monitoring across civil infrastructure, yet installation remains labor-intensive. This paper presents ROADRobot (Robotic System for Automated Deployment of DFOS), a robotic platform integrating closed-loop tension control, calibrated adhesive dispensing, infrared-guided trajectory tracking, and mechanical bead consolidation for automated DFOS deployment. Laboratory validation on wooden and steel substrates identified optimal parameters of 3–6 cm/s traverse velocity and 0.16–0.32 mm/s dispensing velocity, achieving trajectory deviation within 2 mm. Confined-space deployment in a 450 × 450 mm steel channel demonstrated operation under geometric constraints. Comparative trials showed a 46.8% reduction in deployment time versus single-technician manual installation (p < 0.001, Cohen's d = 34.98) with 41% lower variability. OTDR testing confirmed fiber integrity with 0.042 dB insertion loss over 5.5 m. These results establish technical viability, though significant development remains for field application, including curved paths and non-horizontal surfaces.

分布式光纤传感（DFOS）可以实现对民用基础设施的连续应变和温度监测，但安装仍然是劳动密集型的。本文介绍了ROADRobot（自动部署DFOS的机器人系统），这是一个集成了闭环张力控制、校准粘合剂点胶、红外制导轨迹跟踪和自动部署DFOS的机械头固化的机器人平台。在木制和钢制基材上的实验室验证，确定了3-6 cm/s横移速度和0.16-0.32 mm/s点胶速度的最佳参数，实现了2 mm以内的轨迹偏差。在一个450 × 450毫米的钢通道中进行的密闭空间部署演示了几何约束下的操作。对比试验显示，与单个技术人员手动安装相比，部署时间减少了46.8% (p < 0.001, Cohen’s d = 34.98)，可变性降低了41%。OTDR测试证实了光纤的完整性，在5.5 m范围内插入损耗为0.042 dB。这些结果确立了技术上的可行性，但在现场应用方面仍有重大发展，包括弯曲路径和非水平表面。

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引用次数: 0

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