Automation in Construction最新文献_第9页

Incremental digital twin framework: A design science research approach for practical deployment 增量数字孪生框架：用于实际部署的设计科学研究方法

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

Automation in Construction

Pub Date : 2025-02-01 DOI: 10.1016/j.autcon.2024.105954

Diego Calvetti , Pedro Mêda , Eilif Hjelseth , Hipólito de Sousa

Digital Twins (DTw) in the construction industry combine multiple digital concepts aimed at achieving high levels of automation. While the industry pursues digital transition, professionals struggle to implement DTw due to their complexity and lack of standards. An incremental approach to deploying DTw can enable phased implementations, reducing costs and delivering faster outcomes. This paper applies Design Science Research (DSR) to develop, test, and improve an incremental Digital Twin (iDTw) framework for practical deployment. The iDTw is demonstrated and evaluated over three diversified use cases (Municipality Implementations, Residential House and Industrial Facility Operation) provided by experienced professionals from different backgrounds. With-case and cross-case analyses are conducted. iDTw results gave proper responses for the use cases, demonstrating the capability to drive awareness of DTw implementation. Finally, the iDTw combines theory and practice by offering a structured approach for assessing DTw smartness levels and tailored responses, bridging theoretical concepts with real-world applications.

建筑行业的数字孪生（DTw）结合了多种数字概念，旨在实现高水平的自动化。虽然行业追求数字化转型，但由于其复杂性和缺乏标准，专业人士很难实施DTw。部署DTw的增量方法可以实现分阶段实现，降低成本并交付更快的结果。本文应用设计科学研究（DSR）来开发、测试和改进增量数字孪生（iDTw）框架，以供实际部署。iDTw通过三个不同的用例（市政实施、住宅和工业设施运营）进行演示和评估，由来自不同背景的经验丰富的专业人员提供。与案例和跨案例分析进行。iDTw结果为用例提供了适当的响应，展示了驱动对DTw实现的认识的能力。最后，iDTw结合了理论和实践，提供了一种结构化的方法来评估DTw的智能水平和定制的响应，将理论概念与现实世界的应用联系起来。

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引用次数: 0

Hybrid deep learning model for accurate cost and schedule estimation in construction projects using sequential and non-sequential data 利用顺序和非顺序数据的混合深度学习模型，准确估算建筑项目的成本和进度

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

Automation in Construction

Pub Date : 2025-02-01 DOI: 10.1016/j.autcon.2024.105904

Min-Yuan Cheng, Quoc-Tuan Vu, Frederik Elly Gosal

Accurate estimation of construction costs and schedules is crucial for optimizing project planning and resource allocation. Most current approaches utilize traditional statistical analysis and machine learning techniques to process the vast amounts of data regularly generated in construction environments. However, these approaches do not adequately capture the intricate patterns in either time-dependent or time-independent data. Thus, a hybrid deep learning model (NN-BiGRU), combining Neural Network (NN) for time-independent and Bidirectional Gated Recurrent Unit (BiGRU) for time-dependent, was developed in this paper to estimate the final cost and schedule to completion of projects. The Optical Microscope Algorithm (OMA) was used to fine-tune the NN-BiGRU model (OMA-NN-BiGRU). The proposed model earned Reference Index (RI) values of 0.977 for construction costs and 0.932 for completion schedules. These findings underscore the potential of the OMA-NN-BiGRU model to provide highly accurate predictions, enabling stakeholders to make informed decisions that promote project efficiency and overall success.

准确估算建设成本和进度对于优化项目规划和资源配置至关重要。目前大多数方法利用传统的统计分析和机器学习技术来处理建筑环境中定期生成的大量数据。然而，这些方法并不能充分捕捉到依赖时间或不依赖时间的数据中的复杂模式。因此，本文开发了一种混合深度学习模型（NN-BiGRU），结合了时间无关的神经网络（NN）和时间相关的双向门控循环单元（BiGRU），以估计项目的最终成本和完成进度。采用光学显微镜算法（OMA）对NN-BiGRU模型（OMA-NN-BiGRU）进行微调。该模型的建造成本参考指数（RI）为0.977，完工进度参考指数（RI）为0.932。这些发现强调了OMA-NN-BiGRU模型提供高度准确预测的潜力，使利益相关者能够做出明智的决策，从而提高项目效率和整体成功。

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引用次数: 0

Digital tool integrations for architectural reuse of salvaged building materials 用于回收建筑材料的建筑再利用的数字工具集成

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

Automation in Construction

Pub Date : 2025-02-01 DOI: 10.1016/j.autcon.2024.105947

Malgorzata A. Zboinska, Frederik Göbel

Building material reuse can reduce the environmental impact of construction yet its advanced digital support is still limited. Which digital tools could effectively support repair of highly irregular, salvaged materials? To probe this question, a framework featuring six advanced digital tools is proposed and verified through six design and prototyping experiments. The experiments demonstrate that a digital toolkit integrating photogrammetry, robot vision, machine learning, computer vision, computational design, and robotic 3D printing effectively supports repair and recovery of irregular reclaimed materials, enabling their robust digitization, damage detection, and feature-informed computational redesign and refabrication. These findings contribute to the advancement of digitally aided reuse practices in the construction sector, providing valuable insights into accommodating highly heterogeneous reclaimed materials by leveraging advanced automation and digitization. They provide the crucial and currently missing technological and methodological foundation needed to inform future research on industrial digital solutions for reuse.

建筑材料再利用可以减少建筑对环境的影响，但其先进的数字支持仍然有限。哪些数字工具可以有效地支持高度不规则的回收材料的修复？为了探讨这个问题，提出了一个包含六个先进数字工具的框架，并通过六次设计和原型实验进行了验证。实验表明，集成摄影测量、机器人视觉、机器学习、计算机视觉、计算设计和机器人3D打印的数字工具包有效地支持不规则再生材料的修复和恢复，实现其强大的数字化、损伤检测和特征信息计算重新设计和再制造。这些发现有助于推进建筑行业的数字化辅助再利用实践，为利用先进的自动化和数字化来容纳高度异构的再生材料提供了有价值的见解。它们提供了关键的、目前缺失的技术和方法基础，为未来的工业数字解决方案重用研究提供了必要的信息。

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引用次数: 0

Deep learning-enhanced smart ground robotic system for automated structural damage inspection and mapping 用于自动结构损伤检测和测绘的深度学习增强智能地面机器人系统

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

Automation in Construction

Pub Date : 2025-02-01 DOI: 10.1016/j.autcon.2024.105951

Liangfu Ge , Ayan Sadhu

Ground robotic systems are essential for structural inspection, enhancing mobility, efficiency, and safety while minimizing risks in manual inspections. These systems automate 3D mapping and defect assessment in aging. However, current robotic platforms often require the integration of various sensors and complex parameter tuning, raising costs and limiting efficiency. This paper proposes a streamlined unmanned ground vehicle-based inspection platform, integrating only LiDAR and a low-cost monocular camera. Operated via the Robot Operating System, the platform deploys efficient instance segmentation, Simultaneous Localization and Mapping, and fusion algorithms, eliminating complex tuning across environments. A self-attention-enhanced YOLOv7 algorithm is proposed for accurate damage segmentation with limited datasets, while an enhanced KISS-ICP (Keep It Small and Simple-Iterative Closest Point) algorithm is developed to optimize point cloud odometry for efficient mapping and localization. By introducing camera-LiDAR information fusion, the proposed platform achieves structural mapping, damage localization, quantification, and 3D visualization. Laboratory and full-scale bridge tests demonstrated its high accuracy, efficiency, and robustness.

地面机器人系统对于结构检查至关重要，可以提高机动性、效率和安全性，同时最大限度地降低人工检查的风险。这些系统自动化三维绘图和老化缺陷评估。然而，目前的机器人平台往往需要集成各种传感器和复杂的参数调整，这提高了成本，限制了效率。本文提出了一种仅集成激光雷达和低成本单目摄像机的流线型无人地面车辆检测平台。该平台通过机器人操作系统运行，部署了高效的实例分割、同步定位和映射以及融合算法，消除了跨环境的复杂调优。提出了一种自关注增强的YOLOv7算法，用于在有限数据集下进行准确的损伤分割；开发了一种增强的KISS-ICP （Keep It Small and Simple-Iterative nearest Point）算法，用于优化点云测程，实现高效的映射和定位。该平台通过引入摄像头-激光雷达信息融合，实现了结构制图、损伤定位、量化和三维可视化。实验室和全尺寸桥梁测试证明了该方法的准确性、效率和稳健性。

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引用次数: 0

Parametric design methodology for developing BIM object libraries in construction site modeling 在建筑工地建模中开发BIM对象库的参数化设计方法

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

Automation in Construction

Pub Date : 2025-02-01 DOI: 10.1016/j.autcon.2024.105897

Vito Getuli , Alessandro Bruttini , Farzad Rahimian

The adoption of Building Information Modeling (BIM) in construction site layout planning and activity scheduling faces challenges due to the lack of standardized approaches for digitally reproducing and organizing site elements that meet information requirements of diverse regulatory frameworks and stakeholders' use cases. This paper addresses the question of how to streamline the development of BIM objects for construction site modeling by proposing a vendor-neutral parametric design methodology and introduces a dedicated hierarchical structure for BIM object libraries to support users in their implementation. The methodology includes a six-step process for creating informative content, parametric geometries, and documentation, and is demonstrated through the development and implementation of a construction site BIM object library suitable for the Italian context. This approach fills a gap in BIM object development standards and offers a foundation for future research, benefiting practitioners and industry stakeholders involved in BIM-based site layout modeling and activity planning.

建筑信息模型（BIM）在施工现场布局规划和活动调度中的应用面临着挑战，因为缺乏标准化的方法来数字化地复制和组织满足不同监管框架和利益相关者用例的信息需求的现场元素。本文通过提出一种供应商中立的参数化设计方法，解决了如何简化建筑工地建模的BIM对象开发的问题，并为BIM对象库引入了一个专用的分层结构，以支持用户的实施。该方法包括创建信息内容、参数几何和文档的六步过程，并通过开发和实施适合意大利环境的建筑工地BIM对象库进行演示。该方法填补了BIM对象开发标准的空白，为未来的研究奠定了基础，有利于基于BIM的场地布局建模和活动规划的从业者和行业利益相关者。

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引用次数: 0

Enhanced real-time detection transformer (RT-DETR) for robotic inspection of underwater bridge pier cracks 用于水下桥墩裂缝机器人检测的增强型实时检测变压器 (RT-DETR)

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

Automation in Construction

Pub Date : 2025-02-01 DOI: 10.1016/j.autcon.2024.105921

Zhenming Lv , Shaojiang Dong , Zongyou Xia , Jingyao He , Jiawei Zhang

The inadequate visual environment reduces the accuracy of underwater bridge pier fracture detection. Consequently, this paper suggests enhancing the backbone of the Real-Time Detection Transformer(RT-DETR) model to serve as the backbone of the YOLOv8 model. This will be achieved by substituting the Faster Implementation of CSP Bottleneck with 2 convolutions(C2f) module with the Poly Kernel Inception(PKI) Block, which is composed of the PKI Module and Context Anchor Attention(CAA) Block. Its strong capability to distinguish cracks and background features enables accurate recognition of underwater bridge pier cracks. To provide data for detecting these cracks, the enhanced Unpaired Image to Image Translation(CycleGAN) network converts land-style bridge crack images to underwater-style fracture images. The proposed model achieved an F1 score of 0.85 and a mAP50 of 0.84. The real-time detection of underwater bridge fractures by the underwater robot was facilitated by the FPS index of 87.47, which optimizes the detection efficiency.

视觉环境的不足降低了水下桥墩断裂检测的准确性。因此，本文建议增强实时检测变压器（RT-DETR）模型的主干，作为YOLOv8模型的主干。这将通过将CSP瓶颈的快速实现与2卷积（C2f）模块替换为多核初始化（PKI）块来实现，该块由PKI模块和上下文锚定注意（CAA）块组成。它具有较强的裂缝和背景特征识别能力，能够准确识别水下桥墩裂缝。为了提供检测这些裂缝的数据，增强的非配对图像到图像转换（CycleGAN）网络将陆桥裂缝图像转换为水下裂缝图像。该模型的F1得分为0.85，mAP50为0.84。FPS指数为87.47，有利于水下机器人对水下桥梁裂缝的实时探测，优化了探测效率。

引用次数: 0

Dynamic hazard analysis on construction sites using knowledge graphs integrated with real-time information 基于知识图谱和实时信息的建筑工地动态危害分析

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

Automation in Construction

Pub Date : 2025-02-01 DOI: 10.1016/j.autcon.2024.105938

Juntong Zhang , Xin Ruan , Han Si , Xiangyu Wang

Construction, as a significant production activity, is inherently prone to accidents. These accidents often result from a chain of multiple hazards. However, existing methods of hazard analysis are limited to single-dimensional network modeling and static analysis, which makes them inadequate for addressing the complexity and variability of construction sites. This paper presents a dynamic construction hazard analysis method that integrates real-time information into knowledge graphs. In this approach, label entities are added to general knowledge graphs, linking hazard entities to their labels. Labels identified through vision-based methods are then incorporated into the graphs, allowing for the effective extraction and updating of subgraphs in response to spatiotemporal changes in the scenario. Additionally, graph analysis metrics have been proposed to evaluate the system from multiple levels. Finally, the method was applied to a bridge foundation construction case, demonstrating its practicality and significance in preventing accidents by enabling dynamic hazard analysis.

建筑作为一项重要的生产活动，本身就容易发生事故。这些事故往往是由一系列多重危险造成的。然而，现有的危害分析方法仅限于一维网络建模和静态分析，不足以解决建筑工地的复杂性和可变性。提出了一种将实时信息与知识图谱相结合的动态施工危害分析方法。在这种方法中，标签实体被添加到一般知识图中，将危险实体与其标签联系起来。然后，通过基于视觉的方法识别的标签被合并到图中，允许有效地提取和更新子图，以响应场景中的时空变化。此外，图形分析指标已被提出从多个层面评估系统。最后，将该方法应用于某桥梁基础施工实例，验证了该方法的实用性和通过动态危害分析预防事故的意义。

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引用次数: 0

Automation in manufacturing and assembly of industrialised construction 工业建筑的制造和装配自动化

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

Automation in Construction

Pub Date : 2025-02-01 DOI: 10.1016/j.autcon.2024.105945

Li Xu , Yang Zou , Yuqian Lu , Alice Chang-Richards

The integration of automation technologies has improved the efficiency of industrialised construction (IC), yet a deeper understanding of their effects on the manufacturing and assembly stages remains necessary. This paper provides a systematic review of how various automation technologies influence these key stages in IC, analysing 53 articles. It explores the deployment of 22 technologies, including the Internet of Things (IoT), deep learning, digital twins, and robotics, and identifies seven benefits for IC: (1) interoperability, (2) scheduling optimisation, (3) production traceability, (4) production safety, (5) manufacturability, (6) quality assurance, and (7) constructability. To further advance automation in IC, future research should address critical challenges, including enhancing data quality, expanding empirical testing, exploring emerging technologies in depth, and integrating fragmented workflows. This article underscores the need of strategic technology deployment to seamlessly integrate various processes in future construction practices, offering insights into the transformative potential of automation.

自动化技术的集成提高了工业建筑（IC）的效率，但仍有必要更深入地了解它们对制造和装配阶段的影响。本文系统地回顾了各种自动化技术如何影响集成电路中的这些关键阶段，分析了53篇文章。它探讨了22种技术的部署，包括物联网（IoT）、深度学习、数字孪生和机器人技术，并确定了集成电路的七大优势：(1)互操作性，(2)调度优化，(3)生产可追溯性，(4)生产安全性，(5)可制造性，(6)质量保证，(7)可建造性。为了进一步推进集成电路的自动化，未来的研究应解决关键挑战，包括提高数据质量，扩大实证测试，深入探索新兴技术，以及整合碎片化的工作流程。本文强调了战略技术部署的必要性，以便在未来的建设实践中无缝集成各种过程，并提供了对自动化变革潜力的见解。

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引用次数: 0

Delamination detection in concrete decks using numerical simulation and UAV-based infrared thermography with deep learning 基于数值模拟和深度学习的无人机红外热成像技术的混凝土甲板分层检测

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

Automation in Construction

Pub Date : 2025-02-01 DOI: 10.1016/j.autcon.2024.105940

Dyala Aljagoub , Ri Na , Chongsheng Cheng

The potential of concrete bridge delamination detection using infrared thermography (IRT) has grown with technological advancements. However, most current studies require an external input (subjective threshold), reducing the detection's objectivity and accuracy. Deep learning enables automation and streamlines data processing, potentially enhancing accuracy. Yet, data scarcity poses a challenge to deep learning applications, hindering their performance. This paper aims to develop a deep learning approach using supervised learning object detection models with extended data from real and simulated images. The numerical simulation image supplementation seeks to eliminate the limited data barrier by creating a comprehensive dataset, potentially improving model performance and robustness. Mask R-CNN and YOLOv5 were tested across various training data and model parameter combinations to develop an optimal detection model. Lastly, when tested, the model showed a remarkable ability to detect delamination of varying properties accurately compared to currently employed IRT techniques.

随着技术的进步，利用红外热像仪（IRT）检测混凝土桥梁分层的潜力越来越大。然而，目前大多数研究需要外部输入（主观阈值），降低了检测的客观性和准确性。深度学习可以实现自动化并简化数据处理，从而潜在地提高准确性。然而，数据稀缺性给深度学习应用带来了挑战，阻碍了它们的性能。本文旨在开发一种使用监督学习对象检测模型的深度学习方法，该模型具有来自真实和模拟图像的扩展数据。数值模拟图像补充旨在通过创建一个全面的数据集来消除有限的数据障碍，潜在地提高模型性能和鲁棒性。对Mask R-CNN和YOLOv5进行了各种训练数据和模型参数组合的测试，以建立最优检测模型。最后，当测试时，与目前使用的IRT技术相比，该模型显示出准确检测不同属性分层的卓越能力。

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引用次数: 0

Automation in tower cranes over the past two decades (2003–2024) 过去二十年（2003-2024）塔机自动化

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

Automation in Construction

Pub Date : 2025-02-01 DOI: 10.1016/j.autcon.2024.105889

Muhammad Muddassir , Tarek Zayed , Ali Hassan Ali , Mohamed Elrifaee , Sulemana Fatoama Abdulai , Tong Yang , Amr Eldemiry

Tower cranes play a vital role in modern construction for transporting material, yet the persisting issue of crane-related accidents, often attributable to human error, underscores the urgent need for automated crane operations to enhance safety on construction sites. Despite active research in this area, a gap exists in systematically examining and categorising advancements in tower crane automation and identifying key trends and limitations. This paper aims to address this gap by employing a mixed-methods approach, encompassing scientometric and systematic analyses. The scientometric analysis sheds light on key researchers, institutions, journals, and global research networks. Also, the systematic analysis delves into four primary research areas: crane operations, motion control, layout planning, and transport path optimisation. This paper identifies critical knowledge gaps and limitations in tower crane automation, suggests future research directions, and offers industry insights into current methodologies and global trends.

塔式起重机在现代建筑运输材料中发挥着至关重要的作用，然而，与起重机相关的事故持续存在，通常可归因于人为失误，这凸显了对自动化起重机操作的迫切需要，以提高建筑工地的安全性。尽管在这一领域进行了积极的研究，但在系统地检查和分类塔式起重机自动化的进展以及确定关键趋势和限制方面存在差距。本文旨在通过采用混合方法，包括科学计量学和系统分析来解决这一差距。科学计量分析揭示了主要研究人员、机构、期刊和全球研究网络。此外，系统分析深入到四个主要研究领域：起重机操作，运动控制，布局规划和运输路径优化。本文指出了塔式起重机自动化的关键知识差距和局限性，提出了未来的研究方向，并提供了对当前方法和全球趋势的行业见解。

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引用次数: 0