Automation in Construction最新文献

英文中文

Smart control of bridge support forces using adaptive bearings based on physics-informed neural network (PINN) 基于物理信息神经网络（PINN）的自适应支座对桥梁支撑力的智能控制

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

Automation in Construction

Pub Date : 2024-09-26 DOI: 10.1016/j.autcon.2024.105790

Huan Yan , Hong-Ye Gou , Fei Hu , Yi-Qing Ni , You-Wu Wang , Da-Cheng Wu , Yi Bao

Bridge bearings play significant roles in the mechanical responses of bridges and foundations and impact the operation of bridges. This paper presents an adaptive bearing with adjustable height and develops an approach to control bearings toward smart bridges based on Physics-Informed Neural Network (PINN). The approach integrates the mechanical governing equation, which describes the relationship between bridge responses and bearing heights, with data-driven neural networks, enabling efficient prediction of bearing reaction forces and effective optimization of bearing heights for controlling the reaction forces. The effectiveness of the approach is evaluated by examining various types of bridges. The results showed that the proposed approach outperformed 20 machine learning models. The case study showed that the approach effectively limited the force adjustment error to 18 % while reducing both vehicle-bridge response and displacement on bearing top plate. This research will enhance bridge controllability, thereby improving bridge operation.

桥梁支座在桥梁和地基的机械响应中发挥着重要作用，并影响着桥梁的运行。本文介绍了一种高度可调的自适应支座，并开发了一种基于物理信息神经网络（PINN）的智能桥梁支座控制方法。该方法将描述桥梁反应与支座高度之间关系的机械控制方程与数据驱动的神经网络相结合，实现了对支座反力的有效预测和支座高度的有效优化，从而控制了反力。通过对各种类型的桥梁进行研究，对该方法的有效性进行了评估。结果表明，所提出的方法优于 20 种机器学习模型。案例研究表明，该方法有效地将力调整误差限制在 18%，同时降低了车辆-桥梁响应和支座顶板位移。这项研究将提高桥梁的可控性，从而改善桥梁的运行状况。

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引用次数: 0

Fabrication Information Modeling for Closed-Loop Design and Quality Improvement in Additive Manufacturing for construction 用于闭环设计和提高建筑增材制造质量的制造信息建模

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

Automation in Construction

Pub Date : 2024-09-26 DOI: 10.1016/j.autcon.2024.105792

M. Slepicka, A. Borrmann

Additive Manufacturing (AM) has emerged as a disruptive technology with the potential to revolutionize the construction industry by integrating digital design with automated manufacturing. This paper presents and extends Fabrication Information Modeling (FIM), a comprehensive framework tailored for automated manufacturing in construction. FIM facilitates the seamless integration of digital design concepts with automated manufacturing processes, enabling precise control over fabrication information and enhancing construction efficiency and quality. This paper demonstrates its potential to optimize construction processes through a detailed exploration of FIM’s capabilities, including data preparation, path planning, simulation integration, robot control, and data feedback. By enabling a circular data flow between digital modeling and manufacturing, FIM is able to bridge the gap between digital design and physical construction, revolutionizing how construction projects are conceived, planned, and executed. The paper concludes by highlighting the challenges and future research directions in advancing FIM-based construction systems, emphasizing its transformative potential in driving innovation and sustainability in the construction industry.

快速成型制造（AM）是一项颠覆性技术，通过将数字设计与自动化制造相结合，有望彻底改变建筑行业。本文介绍并扩展了制造信息建模（FIM），这是一个专为建筑业自动化制造量身定制的综合框架。FIM 可促进数字设计概念与自动化制造流程的无缝集成，实现对制造信息的精确控制，并提高施工效率和质量。本文通过详细探讨 FIM 的功能，包括数据准备、路径规划、模拟集成、机器人控制和数据反馈，展示了其优化建筑流程的潜力。通过实现数字建模和制造之间的循环数据流，FIM 能够弥合数字设计和实体施工之间的差距，彻底改变建筑项目的构思、规划和执行方式。本文最后强调了在推进基于 FIM 的建筑系统方面所面临的挑战和未来的研究方向，并强调了其在推动建筑行业创新和可持续发展方面的变革潜力。

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引用次数: 0

Five-Year Review of Blockchain in Construction Management: Scientometric and Thematic Analysis (2017-2023) 区块链在建筑管理中的应用五年回顾：科学计量与专题分析（2017-2023 年）

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

Automation in Construction

Pub Date : 2024-09-25 DOI: 10.1016/j.autcon.2024.105773

Khalil Idrissi Gartoumi

Interest in technological innovation within the construction industry has grown significantly. By 2023, Blockchain Technology (BCT) has gained considerable popularity and reached its fifth year of scientific discussion. This paper aims to examine the expansion of BCT and evaluate its current environment. At the time of writing, 237 documents were analysed. A mixed-methods approach was employed, combining scientometric and thematic analysis with a critical review. The results outline the trends in this research area and categorise thematic BCT applications in the construction industry into eight distinct categories. The paper identifies the challenges associated with BCT deployment and offers guidance on the key factors for its successful application in resolving construction disputes. First to using a scientometric and thematic method, this paper not only reinforces existing literature but also proposes future research directions and practical actions to develop further the critical factors necessary for BCT's success in the construction industry.

建筑行业对技术创新的兴趣大幅增长。到 2023 年，区块链技术（BCT）已经获得了相当大的普及，并进入了科学讨论的第五个年头。本文旨在研究区块链技术的发展，并评估其当前环境。在撰写本文时，共分析了 237 份文件。本文采用了一种混合方法，将科学计量分析、专题分析与批判性评论相结合。分析结果概述了这一研究领域的发展趋势，并将建筑行业的专题性 BCT 应用分为八个不同的类别。论文确定了与业连技术应用相关的挑战，并就成功应用业连技术解决建筑纠纷的关键因素提供了指导。本文首先采用了科学计量学和专题方法，不仅加强了现有文献，还提出了未来的研究方向和实际行动，以进一步发展 BCT 在建筑行业成功应用的必要关键因素。

引用次数: 0

Anomaly detection in concrete dam using memory-augmented autoencoder and generative adversarial network (MemAE-GAN) 利用记忆增强自动编码器和生成式对抗网络（MemAE-GAN）检测混凝土大坝的异常情况

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

Automation in Construction

Pub Date : 2024-09-24 DOI: 10.1016/j.autcon.2024.105794

Xinyu Kang , Yanlong Li , Ye Zhang , Ning Ma , Lifeng Wen

Anomaly detection of concrete dam from deformation monitoring data is significant for dam safety evaluation. Existing anomaly detection models face challenges in identifying minor abnormal values and detection accuracy. This paper integrates the memory-augmented deep autoencoder (MemAE) with the generative adversarial network (GAN) to construct the unsupervised MemAE-GAN model, which leverages MemAE's precision in modeling and the GAN's adversarial training capability to highlight minor abnormal values, thereby significantly enhancing both sensitivity and accuracy in anomaly detection. Experimental results indicate that the MemAE-GAN model consistently achieved anomaly detection accuracy exceeding 0.97, considerably outperforming other comparative models. This model provides a highly accurate approach for deformation anomaly detection and lays the groundwork for subsequent research on deformation prediction and early warning. Future research could explore the algorithms to analyze the causes of abnormal values and establish the anomaly detection framework.

从变形监测数据中检测混凝土大坝的异常对大坝安全评估意义重大。现有的异常检测模型在识别微小异常值和检测精度方面面临挑战。本文将记忆增量深度自动编码器（MemAE）与生成式对抗网络（GAN）相结合，构建了无监督的 MemAE-GAN 模型，利用 MemAE 的建模精度和 GAN 的对抗训练能力来突出微小异常值，从而显著提高了异常检测的灵敏度和准确性。实验结果表明，MemAE-GAN 模型的异常检测准确率始终保持在 0.97 以上，大大优于其他同类模型。该模型提供了一种高精度的变形异常检测方法，为后续的变形预测和预警研究奠定了基础。未来的研究可以探索分析异常值成因的算法，并建立异常检测框架。

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引用次数: 0

Multi-equipment collaborative optimization scheduling for intelligent construction scene 智能施工场景的多设备协同优化调度

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

Automation in Construction

Pub Date : 2024-09-23 DOI: 10.1016/j.autcon.2024.105780

Zhansheng Liu , Guoliang Shi , Dechun Lu , Xiuli Du , Qingwen Zhang

How to realize the efficient scheduling of construction equipment and ensure the construction quality is the key problem that restricts the development of intelligent construction technology. This paper proposes a multi-equipment collaborative optimization scheduling method for intelligent construction scene. Firstly, a logical model of intelligent construction scene is proposed, and the characteristics and requirements of construction in intelligent construction scene are clarified. Considering the relationship between construction processes and the control requirements of construction quality, an intelligent planning model of multi-equipment collaborative scheduling scheme is established. Aiming at the problem of equipment scheduling analysis, an improved non-dominant classification genetic algorithm (NSGA-II) is proposed. According to the solution results of the improved NSGA-II, the data mapping relationship between the scheduling scheme and the construction completion time and construction energy consumption is established. The verification and application of the proposed method are carried out by a cable truss structure experimental model.

如何实现施工设备的高效调度，保证施工质量，是制约智能施工技术发展的关键问题。本文提出了一种面向智能化施工场景的多设备协同优化调度方法。首先，提出了智能化施工场景的逻辑模型，明确了智能化施工场景中施工的特点和要求。考虑到施工工序之间的关系和施工质量的控制要求，建立了多设备协同调度方案的智能规划模型。针对设备调度分析问题，提出了改进的非优势分类遗传算法（NSGA-II）。根据改进的 NSGA-II 的求解结果，建立了调度方案与施工完成时间和施工能耗之间的数据映射关系。通过索桁架结构实验模型对所提方法进行了验证和应用。

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引用次数: 0

Vision-guided robot for automated pixel-level pavement crack sealing 用于自动像素级路面裂缝密封的视觉引导机器人

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

Automation in Construction

Pub Date : 2024-09-23 DOI: 10.1016/j.autcon.2024.105783

Jianqi Zhang , Xu Yang , Wei Wang , Hainian Wang , Ling Ding , Sherif El-Badawy , Zhanping You

Automated pavement crack sealing plays a crucial role in road maintenance. However, challenges remain in refining crack segmentation and sealing control accuracy. This article proposes an automated pavement crack sealing robot(APCSbot), which employs a crack refinement network(CrackSegRefiner) and a crack sealing controller(LQR). Specifically, CrackSegRefiner is based on a denoising diffusion model to refine the coarse mask of crack through a diffusion process. Additionally, the LQR controller integrates weight matrices Q and R to ensure control and state of APCSbot based on visual servo, facilitating the delivery of emulsified asphalt for sealing through the end effector. Extensive experiments conducted on the DeepCrack, CFD, and S2TCrack datasets confirm the effectiveness of APCSbot, which achieved a segmentation precision of 84.48% and mIoU of 79.28%. Furthermore, the system demonstrated a sealing error of 6.22 mm and speed of 0.0456 m/s when addressing discontinuous cracks, showcasing its excellence and robustness in crack sealing.

路面裂缝自动密封在道路养护中发挥着至关重要的作用。然而，在细化裂缝分割和密封控制精度方面仍存在挑战。本文提出了一种自动路面裂缝密封机器人（APCSbot），它采用了裂缝细化网络（CrackSegRefiner）和裂缝密封控制器（LQR）。具体来说，CrackSegRefiner 基于去噪扩散模型，通过扩散过程细化裂缝粗掩膜。此外，LQR 控制器整合了权重矩阵 Q 和 R，以确保基于视觉伺服的 APCSbot 的控制和状态，从而促进通过终端效应器输送用于密封的乳化沥青。在 DeepCrack、CFD 和 S2TCrack 数据集上进行的大量实验证实了 APCSbot 的有效性，其分割精度达到 84.48%，mIoU 达到 79.28%。此外，该系统在处理不连续裂缝时的密封误差为 6.22 毫米，速度为 0.0456 米/秒，显示了其在裂缝密封方面的卓越性和鲁棒性。

引用次数: 0

Data integration using deep learning and real-time locating system (RTLS) for automated construction progress monitoring and reporting 利用深度学习和实时定位系统（RTLS）进行数据整合，实现自动化施工进度监测和报告

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

Automation in Construction

Pub Date : 2024-09-23 DOI: 10.1016/j.autcon.2024.105778

Dena Shamsollahi , Osama Moselhi , Khashayar Khorasani

The shift towards automated progress monitoring using new technologies for efficient delivery of construction projects has received significant attention. The application of vision-based techniques for object recognition and real-time locating system (RTLS) for object localization has been widely studied. However, a single technology cannot provide complete information needed to determine the status of tracked elements on a job site. This paper presents an integrated method for progress monitoring through recognition and localization of elements in construction sites. This method integrates data derived from a deep learning model and Ultra-wideband (UWB) system, and reports each element's ID, location, visual data and capture time. Such information is essential for project managers to assess progress on site. The method is validated in a mechanical room, a challenging environment for RTLS and object recognition models due to signal interferences and occlusions. The findings suggest further research on improving integrated methods for efficient progress reporting.

利用新技术实现自动化进度监控，从而高效交付建筑项目，这一转变受到了广泛关注。基于视觉的物体识别技术和用于物体定位的实时定位系统（RTLS）的应用已得到广泛研究。然而，单一技术无法提供确定施工现场被跟踪元素状态所需的完整信息。本文介绍了一种通过识别和定位建筑工地上的构件来进行进度监控的综合方法。该方法整合了从深度学习模型和超宽带（UWB）系统中获取的数据，并报告每个元素的 ID、位置、视觉数据和捕捉时间。这些信息对于项目经理评估现场进度至关重要。该方法在机房中进行了验证，由于信号干扰和遮挡，机房对于 RTLS 和物体识别模型来说是一个具有挑战性的环境。研究结果表明，应进一步研究改进综合方法，以实现高效的进度报告。

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引用次数: 0

Enhancing intelligent compaction quality assessment utilizing mathematical-geographical data processing 利用数学地理数据处理加强智能压实质量评估

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

Automation in Construction

Pub Date : 2024-09-23 DOI: 10.1016/j.autcon.2024.105786

Chi Cheng , Xuefei Wang , Jiale Li , Jianmin Zhang , Guowei Ma

The advent of Intelligent Compaction (IC) has revolutionized real-time monitoring of compaction quality. The Compaction Meter Value (CMV) is widely used in highway construction but demonstrates insufficient reliability, which generates challenges for accurate quality assessment. A mathematical-geographical-based processing method is proposed to refine IC datasets. Six datasets from highway compaction sites were used to verify the effectiveness of the method. Statistical analysis is employed to cleanse redundant values, while a near-neighbor weighted method, accounting for spatial distribution characteristics, is utilized to identify and replace outliers. CMV has instability under complex influence factors, and it shows the best applicability in the subgrade. The optimized datasets perform well in correlation models, showcasing a significant improvement in quality evaluation effectiveness. This paper aims to optimize the utilization of IC datasets, thereby bolstering the reliability of CMV. The proposed method advocates integration into the IC system to promote highway construction quality.

智能压实（IC）的出现彻底改变了压实质量的实时监测。压实度测量值（CMV）被广泛应用于公路建设中，但其可靠性不足，给精确的质量评估带来了挑战。本文提出了一种基于数学地理学的处理方法来完善集成电路数据集。为了验证该方法的有效性，我们使用了来自高速公路压实现场的六个数据集。该方法采用统计分析来清除冗余值，同时利用近邻加权法（考虑空间分布特征）来识别和替换异常值。CMV 在复杂的影响因素下具有不稳定性，在路基中表现出最佳的适用性。优化后的数据集在相关模型中表现良好，质量评价效果显著提高。本文旨在优化集成电路数据集的利用，从而提高 CMV 的可靠性。本文提出的方法主张集成到集成电路系统中，以促进公路建设质量。

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引用次数: 0

Bayesian continuous wavelet transform for time-varying damping identification of cables using full-field measurement 利用全场测量贝叶斯连续小波变换识别电缆的时变阻尼

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

Automation in Construction

Pub Date : 2024-09-21 DOI: 10.1016/j.autcon.2024.105791

Junying Wang , Qiankun Zhu , Qiong Zhang , Xianyu Wang , Yongfeng Du

Cables serve as the primary load-bearing element in cable-stayed bridges, making their damping level critical for structural safety evaluation. Traditional operational modal analysis (OMA) faces challenges in damping identification due to result discreteness, and limited sensor deployment often leads to the loss of crucial modal information. This paper proposes a Bayesian continuous wavelet transform with Gabor wavelet (BCWT-G) method for time-varying damping identification using full-field measurement data. A computer vision technique combining the pyramid grafting network (PGNet) with neighboring frame pixel fitting (NFPF) is used to accurately capture full-field vibration data. The time-frequency domain properties of these data are then extracted and incorporated into a Bayesian probabilistic estimation framework for modal updating. The proposed method was validated through numerical simulations using a physics-based graphics model (PBGM), and actual cable testing under complex environments, demonstrating its effectiveness and robustness in identifying the time-varying dynamic characteristics of cables.

电缆是斜拉桥的主要承重构件，因此其阻尼水平对结构安全评估至关重要。由于结果的离散性，传统的运行模态分析（OMA）在阻尼识别方面面临挑战，而有限的传感器部署往往导致关键模态信息的丢失。本文提出了一种贝叶斯连续小波变换与 Gabor 小波（BCWT-G）方法，用于利用全场测量数据进行时变阻尼识别。结合金字塔嫁接网络（PGNet）和邻帧像素拟合（NFPF）的计算机视觉技术被用于精确捕捉全场振动数据。然后提取这些数据的时频域属性，并将其纳入贝叶斯概率估计框架以进行模态更新。通过使用基于物理的图形模型（PBGM）进行数值模拟，以及在复杂环境下进行实际电缆测试，验证了所提出的方法在识别电缆时变动态特性方面的有效性和稳健性。

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引用次数: 0

Intelligent design of key joints in aerial building machine using topology optimization and generative adversarial network 利用拓扑优化和生成式对抗网络智能设计空中造物机的关键关节

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

Automation in Construction

Pub Date : 2024-09-21 DOI: 10.1016/j.autcon.2024.105747

Zhuang Xia , Jiaqi Wang , Yongsheng Li , Limao Zhang , Changyong Liu

Joints are crucial connections in an aerial building machine (ABM), yet they often undergo experience-based local optimization design. This paper presents an intelligent design method for key joints in the ABM using a generative adversarial network (GAN), aiming to achieve new and superior global optimization schemes. A database of topology-optimized structures is fed into the boundary equilibrium GAN (BEGAN) for training, which in turn generates innovative and diverse design schemes. The optimal scheme selection under multi-working conditions is then realized by the multiple-attribute decision-making (MADM) method. A case study of an ABM joist confirms the effectiveness of this method, showing it meets safety requirements under various conditions and achieves significant improvements (43.45 % for construction, 43.67 % for jacking, and 42.89 % for shutdown). Additionally, the BEGAN model surpasses existing generative models for ABM joint design. To determine evaluation schemes and optimal designs, this paper provides a method for global optimization of joints that considers the integrated effects of multiple conditions, constructing a rapid and comprehensive solution for designing and evaluating key joints in the ABM.

关节是高空作业机械（ABM）中的关键连接件，但它们通常需要进行基于经验的局部优化设计。本文提出了一种利用生成式对抗网络（GAN）对 ABM 中的关键连接点进行智能设计的方法，旨在实现新的、更优越的全局优化方案。拓扑优化结构数据库被输入边界平衡 GAN（BEGAN）进行训练，进而生成创新和多样化的设计方案。然后通过多属性决策（MADM）方法实现多工作条件下的最优方案选择。对 ABM 托梁的案例研究证实了该方法的有效性，表明它满足各种条件下的安全要求，并实现了显著的改进（施工改进 43.45%，顶升改进 43.67%，停工改进 42.89%）。此外，BEGAN 模型超越了现有的 ABM 联合设计生成模型。为了确定评估方案和优化设计，本文提供了一种考虑多种条件综合影响的接头全局优化方法，为设计和评估 ABM 中的关键接头构建了一个快速而全面的解决方案。

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