Automation in Construction最新文献

英文中文

Variable-depth large neighborhood search algorithm for cable routing in distributed photovoltaic systems 分布式光伏系统中电缆布线的可变深度大邻域搜索算法

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2024-10-26 DOI: 10.1016/j.autcon.2024.105839

Andong Qiu, Zhouwang Yang

Distributed photovoltaic power systems, typically deployed in complex scenarios like irregular rooftops, present a challenging detailed cable routing problem (DCRP). This involves grouping solar modules and routing cables to connect each group, traditionally addressed through manual design. This paper presents a variable-depth large neighborhood search (VDLNS) algorithm to address the DCRP, which is modeled as a specialized cycle covering problem using arc-flow and partition formulations. A cycle-split heuristic, derived from DCRP’s connection to the traveling salesman problem, is introduced and combined with a series of destroy operators to construct the VDLNS algorithm. Numerical experiments conducted on both synthetic and real-world instances validated the algorithm’s efficacy, achieving an average total cost reduction of 12.87% on house rooftop instances compared to manual design. The results indicate that the method effectively streamlines photovoltaic system design by delivering cost-efficient cable routing schemes within a reasonable timeframe.

分布式光伏发电系统通常部署在不规则屋顶等复杂场景中，这就提出了一个具有挑战性的详细电缆布线问题（DCRP）。这涉及到太阳能模块的分组和连接各组的电缆布线，传统上是通过人工设计来解决的。本文提出了一种可变深度大邻域搜索（VDLNS）算法来解决 DCRP 问题，该算法使用弧流和分割公式将 DCRP 建模为一个专门的循环覆盖问题。从 DCRP 与旅行推销员问题的联系中得出的循环分割启发式被引入并与一系列破坏算子相结合，从而构建了 VDLNS 算法。在合成实例和真实世界实例上进行的数值实验验证了该算法的有效性，与人工设计相比，该算法在房屋屋顶实例上平均降低了 12.87% 的总成本。结果表明，该方法能在合理的时间内提供具有成本效益的电缆布线方案，从而有效简化光伏系统设计。

引用次数: 0

Robust localization of shear connectors in accelerated bridge construction with neural radiance field 利用神经辐射场对加速桥梁施工中的剪力连接件进行稳健定位

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2024-10-24 DOI: 10.1016/j.autcon.2024.105843

Gyumin Lee , Ali Turab Asad , Khurram Shabbir , Sung-Han Sim , Junhwa Lee

Accelerated bridge construction (ABC) demands precise alignment of prefabricated members to prevent assembly failure. Conventional methods struggle to localize shear connectors from point cloud data (PCD) generated by structure-from-motion due to its sparsity. This paper introduces a robust method for shear connector localization using PCD generated by a neural radiance field and a three-step narrowing-down algorithm. The PCD exhibits densely populated points for small connectors, allowing the algorithm to pinpoint their locations accurately. The method successfully identified all 72 shear connectors in a mock-up prefabricated girder, with an average error of 10 mm, demonstrating its potential for assessing constructability in ABC projects. Future research may integrate deep learning-based segmentation techniques to enhance efficiency and adaptability in complex geometries and non-standard bridge designs.

加速桥梁施工（ABC）要求对预制构件进行精确对齐，以防止装配失败。由于点云数据（PCD）的稀疏性，传统方法很难从结构运动生成的点云数据中定位剪力连接件。本文介绍了一种使用神经辐射场生成的 PCD 和三步缩小算法进行剪力连接器定位的稳健方法。PCD 对小型连接器显示出密集的点，使算法能够精确定位其位置。该方法成功识别了模拟预制梁中的全部 72 个剪力连接件，平均误差为 10 毫米，证明了其在评估 ABC 项目可施工性方面的潜力。未来的研究可能会整合基于深度学习的分割技术，以提高复杂几何形状和非标准桥梁设计的效率和适应性。

引用次数: 0

From raw to refined: Data preprocessing for construction machine learning (ML), deep learning (DL), and reinforcement learning (RL) models 从原始到精炼：构建机器学习 (ML)、深度学习 (DL) 和强化学习 (RL) 模型的数据预处理

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2024-10-24 DOI: 10.1016/j.autcon.2024.105844

SeyedeZahra Golazad , Abbas Mohammadi , Abbas Rashidi , Mohammad Ilbeigi

As the use of predictive models in construction rapidly increases, the need for preprocessing raw construction data has become more critical. This systematic review investigates data preprocessing techniques for machine learning (ML), deep learning (DL), and reinforcement learning (RL) models in the construction domain. Through a comprehensive analysis of 457 studies, the prevalence of six data types (i.e., tabular, image, video frame, time series, text, and point cloud) and their respective preprocessing methods are examined. Key findings reveal data transformation, cleaning, reduction, augmentation, and scaling as fundamental preprocessing categories, with applications varying across data types. The paper highlights knowledge gaps, including limited synthetic data adoption, lack of standardized annotation practices, absence of comprehensive preprocessing frameworks, and need for automated labeling. Furthermore, critical considerations regarding data privacy, security, sharing, and management practices are discussed. The review underscores the pivotal role of robust data preprocessing in enabling reliable predictive models.

随着预测模型在建筑领域的使用迅速增加，对原始建筑数据进行预处理的需求也变得更加迫切。本系统综述研究了建筑领域机器学习（ML）、深度学习（DL）和强化学习（RL）模型的数据预处理技术。通过对 457 项研究的综合分析，考察了六种数据类型（即表格、图像、视频帧、时间序列、文本和点云）的普遍性及其各自的预处理方法。主要研究结果表明，数据转换、清理、缩减、增强和缩放是基本的预处理类别，在不同数据类型中的应用各不相同。论文强调了知识差距，包括合成数据采用有限、缺乏标准化注释实践、缺乏全面的预处理框架以及对自动标记的需求。此外，还讨论了有关数据隐私、安全、共享和管理实践的重要考虑因素。综述强调了强大的数据预处理在建立可靠的预测模型中的关键作用。

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引用次数: 0

Performance evaluation of struck-by-accident alert systems for road work zone safety 道路施工区安全事故报警系统性能评估

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2024-10-24 DOI: 10.1016/j.autcon.2024.105837

Qishen Ye, Yihai Fang, Nan Zheng

Road work zones pose significant safety risks to both vehicles passing by and the construction workers moving within the work zones. Over recent years, significant research efforts have been dedicated to work zone safety, particularly by leveraging emerging technologies. This paper aims to review the literature on performance evaluation of safety technologies designed to mitigate struck-by hazards. This review identified 57 relevant publications focusing on technology evaluation, which were critically reviewed using the Four-component Cyber-Physical System (CPS) hierarchy and the Adapted Layer of Protection Analysis (ALOPA) framework. The CPS hierarchy-based review unveiled the focused components under evaluation, the relationship among these components, the methodologies employed, and the key performance results. The extent and completeness of the evaluation methods were examined through the ALOPA framework. The findings of this research highlight emerging trends that explore the impact of human factors on accident avoidance outcomes in risk-free virtual environments and suggest several prospective considerations as per ALOPA that can guide future research towards performance-based evaluations and design optimisations.

道路施工区对过往车辆和在施工区内行驶的建筑工人都构成了极大的安全风险。近年来，人们致力于对施工区安全进行大量研究，特别是利用新兴技术。本文旨在回顾有关旨在减轻撞击危险的安全技术性能评估的文献。该综述确定了 57 篇以技术评估为重点的相关出版物，并使用四组件网络-物理系统 (CPS) 层次结构和适应性保护层分析 (ALOPA) 框架对这些出版物进行了严格审查。基于 CPS 层次结构的审查揭示了所评估的重点组件、这些组件之间的关系、所采用的方法以及关键性能结果。通过 ALOPA 框架审查了评估方法的范围和完整性。这项研究的结果突出了探索人为因素对无风险虚拟环境中事故避免结果的影响的新趋势，并根据 ALOPA 框架提出了若干前瞻性考虑因素，这些因素可以指导未来的研究，实现基于性能的评估和设计优化。

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引用次数: 0

Digital twins in bridge engineering for streamlined maintenance and enhanced sustainability 桥梁工程中的数字双胞胎简化维护工作并提高可持续性

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2024-10-23 DOI: 10.1016/j.autcon.2024.105834

M. Franciosi, M. Kasser, M. Viviani

Digital twins are evolving to oversee the entire construction life cycle, with a strong emphasis on sustainability across environmental, financial, regulatory, and administrative dimensions. This paper introduces a methodology for managing existing bridges through an adaptable digital twin. The aim of this research is to develop a framework for constructing digital twins that, by enabling structural analysis and “what-if” scenario simulations, supports more reliable maintenance decision-making. Such type of digital twin ensure safety, extend lifespan, and provide a precise database for managing end-of-life processes within a circular “cradle to cradle” framework. This methodology also addresses obsolescence issues related to software evolution and the longer lifespan of a bridge compared to its creator. A case study demonstrates the methodology's effectiveness, showing that digital twins can be flexible, cost-effective tools for managing all types of bridges, including small and existing ones.

数字孪生正在不断发展，以监督整个建筑生命周期，并着重强调环境、财务、监管和行政方面的可持续性。本文介绍了一种通过可调整的数字孪生来管理现有桥梁的方法。这项研究旨在开发一个构建数字孪生的框架，通过结构分析和 "假设 "情景模拟，支持更可靠的维护决策。这种类型的数字孪生可确保安全、延长使用寿命，并为在 "从摇篮到摇篮 "的循环框架内管理报废流程提供精确的数据库。这种方法还能解决与软件进化相关的报废问题，以及与创建者相比桥梁寿命更长的问题。一项案例研究证明了该方法的有效性，表明数字孪生可以成为灵活、经济高效的工具，用于管理各种类型的桥梁，包括小型桥梁和现有桥梁。

引用次数: 0

Topology-aware mamba for crack segmentation in structures 用于结构裂缝分割的拓扑感知 Mamba

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2024-10-23 DOI: 10.1016/j.autcon.2024.105845

Xin Zuo , Yu Sheng , Jifeng Shen , Yongwei Shan

CrackMamba, a Mamba-based model, is designed for efficient and accurate crack segmentation for monitoring the structural health of infrastructure. Traditional Convolutional Neural Network (CNN) models struggle with limited receptive fields, and while Vision Transformers (ViT) improve segmentation accuracy, they are computationally intensive. CrackMamba addresses these challenges by utilizing the VMambaV2 with pre-trained ImageNet-1 k weights as the encoder and a newly designed decoder for better performance. To handle the random and complex nature of crack development, a Snake Scan module is proposed to reshape crack feature sequences, enhancing feature extraction. Additionally, the three-branch Snake Conv VSS (SCVSS) block is proposed to target cracks more effectively. Experiments show that CrackMamba achieves state-of-the-art (SOTA) performance on the CrackSeg9k and SewerCrack datasets, and demonstrates competitive performance on the retinal vessel segmentation dataset CHASE_DB1, highlighting its generalization capability.

CrackMamba 是一种基于 Mamba 的模型，设计用于高效、准确地分割裂缝，以监测基础设施的结构健康状况。传统的卷积神经网络（CNN）模型在有限的感受野中挣扎，而视觉变换器（ViT）虽然提高了分割精度，但却耗费大量计算资源。CrackMamba 利用 VMambaV2 和预训练的 ImageNet-1 k 权重作为编码器，并采用全新设计的解码器来提高性能，从而解决了这些难题。为了处理裂纹发展的随机性和复杂性，我们提出了蛇形扫描模块来重塑裂纹特征序列，从而加强特征提取。此外，还提出了三分支 Snake Conv VSS（SCVSS）模块，以更有效地锁定裂纹。实验表明，CrackMamba 在 CrackSeg9k 和 SewerCrack 数据集上实现了最先进的性能（SOTA），并在视网膜血管分割数据集 CHASE_DB1 上表现出极具竞争力的性能，突显了其泛化能力。

引用次数: 0

Virtual in-situ modeling between digital twin and BIM for advanced building operations and maintenance 数字孪生和 BIM 之间的虚拟原位建模，用于先进的楼宇运营和维护

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2024-10-23 DOI: 10.1016/j.autcon.2024.105823

Sungmin Yoon , Jeyoon Lee , Jiteng Li , Peng Wang

A virtual model that mathematically represents operational behaviors is essential for implementing the concepts of digital twins (DTs) and building information modeling (BIM) to achieve intelligent, optimal building operations. However, current research lacks an approach to reliably construct virtual models. This paper introduces a concept named virtual in-situ modeling (VIM), designed to comprehensively represent building behaviors. The VIM framework is based on five key aspects: modeling environments, model types, modeling sources, modeling approaches, and model fusion techniques. VIM bridges BIM and DT, enabling virtual modeling during the operational phase and enhancing both BIM-based DT and DT-enhanced BIM. Case studies conducted using real building operations demonstrate the effectiveness of VIM, achieving a highly accuracy (RMSE of 0.24 °C). Additionally, the VIM-assisted fault detection and diagnosis (FDD) provided early detection and diagnostic estimation, outperforming FDD without the virtual model. This paper highlights the potential of VIM for advanced building operations and maintenance.

数字孪生（DTs）和建筑信息建模（BIM）概念的实施，对于实现智能化、最优化的建筑运营而言，一个能以数学方式表达运营行为的虚拟模型至关重要。然而，目前的研究缺乏可靠构建虚拟模型的方法。本文介绍了一种名为 "虚拟原位建模"（VIM）的概念，旨在全面表现建筑行为。VIM 框架基于五个关键方面：建模环境、模型类型、建模来源、建模方法和模型融合技术。VIM 是 BIM 和 DT 的桥梁，可在运营阶段进行虚拟建模，并增强基于 BIM 的 DT 和 DT 增强型 BIM。使用真实建筑运行进行的案例研究证明了 VIM 的有效性，实现了高精度（RMSE 为 0.24 °C）。此外，VIM 辅助故障检测和诊断（FDD）提供了早期检测和诊断估算，其性能优于没有虚拟模型的 FDD。本文强调了 VIM 在先进楼宇运营和维护方面的潜力。

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引用次数: 0

Partial annotations in active learning for semantic segmentation 语义分割主动学习中的部分注释

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2024-10-22 DOI: 10.1016/j.autcon.2024.105828

B.G. Pantoja-Rosero , A. Chassignet , A. Rezaie , M. Kozinski , R. Achanta , K. Beyer

Semantic segmentation with deep learning plays a crucial role in various fields, including civil engineering, particularly in tasks such as damage assessment and urban planning. This paper addresses the challenge of efficiently training deep learning models for semantic segmentation with a limited set of annotated data, thus reducing the burden of ground truth labeling. An active learning strategy is introduced, leveraging partial annotations informed by predictions and uncertainties from previously trained models. Unlike other active learning frameworks, this approach not only facilitates the annotation of highly uncertain image regions but also targets those with low uncertainty, which often lead to false positives and negatives. The results demonstrate that using partial annotations within an active learning framework significantly reduces manual annotation efforts and training time without compromising model performance. These findings have substantial implications for the efficiency and scalability of deep learning in civil engineering, paving the way for future research in active learning and semantic segmentation.

利用深度学习进行语义分割在包括土木工程在内的多个领域发挥着至关重要的作用，尤其是在损害评估和城市规划等任务中。本文探讨了如何利用有限的标注数据集高效地训练语义分割深度学习模型，从而减轻地面实况标注的负担。本文引入了一种主动学习策略，利用先前训练过的模型的预测和不确定性提供的部分注释。与其他主动学习框架不同的是，这种方法不仅有利于对高度不确定的图像区域进行标注，而且还能针对那些不确定性较低的区域进行标注，而不确定性较低的区域往往会导致假阳性和假阴性。研究结果表明，在主动学习框架内使用部分注释可显著减少人工注释工作量和训练时间，同时不会影响模型性能。这些发现对土木工程中深度学习的效率和可扩展性具有重大意义，为未来的主动学习和语义分割研究铺平了道路。

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引用次数: 0

Investigating construction workers' perception of risk, likelihood, and severity using electroencephalogram and machine learning 利用脑电图和机器学习调查建筑工人对风险、可能性和严重性的感知

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2024-10-22 DOI: 10.1016/j.autcon.2024.105814

Zhengkai Zhao , Shu Zhang , Xinyu Hua , Xiuzhi Shi

Understanding how workers perceive risk is essential to construction safety management. Firstly, an event-related potential (ERP) experiment was conducted to investigate the relationship between risk, likelihood, and severity. Then, a linear model was developed to predict workers' risk perception based on ERP components and quantify the relative importance of severity to likelihood. Finally, an additive model was constructed to reflect the risk perception pattern. The results indicate: (1) Workers' emotional responses stem from the process of associating accident consequences in severity assessment, which is represented by the late positive potential (LPP) component. (2) Workers' risk perception relies more on severity compared with likelihood. (3) The additive model (risk = 0.203 * likelihood +0.758 * severity) better matches the risk perception patterns than the multiplicative model. The research results provide a new perspective for understanding workers' risk perception patterns and contributing to proactive safety management in the construction industry.

了解工人如何感知风险对建筑安全管理至关重要。首先，我们进行了一项事件相关电位（ERP）实验，以研究风险、可能性和严重性之间的关系。然后，建立了一个线性模型，根据 ERP 成分预测工人的风险感知，并量化严重性与可能性的相对重要性。最后，构建了一个加法模型来反映风险感知模式。结果表明：(1) 工人的情绪反应源于在严重性评估中对事故后果的联想过程，这表现为晚期积极潜能（LPP）成分。(2) 与可能性相比，工人的风险认知更依赖于严重性。(3) 与乘法模型相比，加法模型（风险 = 0.203 * 可能性 +0.758 * 严重性）更符合风险认知模式。研究结果为了解工人的风险感知模式提供了一个新的视角，有助于建筑行业积极主动的安全管理。

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Real-time lightweight YOLO model for grouting defect detection in external post-tensioned ducts via infrared thermography 通过红外热成像检测外部后张法管道灌浆缺陷的实时轻量级 YOLO 模型

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2024-10-21 DOI: 10.1016/j.autcon.2024.105830

Shengli Li , Shiji Sun , Yang Liu , Wanshuai Qi , Nan Jiang , Can Cui , Pengfei Zheng

It is challenging to distinguish the defective areas using infrared thermography to automatically analyze external post-tensioned tendon duct grouting defects. To achieve efficient and stable automated detection, a lightweight real-time grouting defects detection method based on YOLO deep learning is proposed. Firstly, the Cutpaste data augmentation method was used to effectively alleviate the problem of overfitting. Then, the C3Ghost module was introduced into the neck network, and the number of channels in the network layers was adjusted to 50 % of those in the YOLOv5s model, reducing the number of parameters and computational resources. Finally, the SGD optimizer and GIOU loss function, as well as the Sim attention module, were used to improve detection accuracy. Based on instance analysis and comparison, this method achieves [email protected] of 96.9 % and detection speed of 66FPS. Compared with YOLOv5s, it reduces the number of parameters by 79 % and FLOPs by 77 %.

利用红外热成像技术自动分析外部后张法筋管灌浆缺陷，区分缺陷区域是一项挑战。为了实现高效稳定的自动检测，本文提出了一种基于 YOLO 深度学习的轻量级灌浆缺陷实时检测方法。首先，使用 Cutpaste 数据增强方法有效缓解了过拟合问题。然后，在颈部网络中引入 C3Ghost 模块，并将网络层的通道数调整为 YOLOv5s 模型的 50%，减少了参数数量和计算资源。最后，利用 SGD 优化器和 GIOU 损失函数以及 Sim attention 模块提高了检测精度。根据实例分析和比较，该方法的[email protected] 达到 96.9 %，检测速度达到 66FPS。与 YOLOv5s 相比，参数数量减少了 79%，FLOPs 减少了 77%。

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