Automation in Construction最新文献

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Hybrid pose adjustment (HyPA) robot design for prefabricated module control in modular construction assembly 用于模块化建筑装配中预制模块控制的混合姿态调整 (HyPA) 机器人设计

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

Automation in Construction

Pub Date : 2024-10-14 DOI: 10.1016/j.autcon.2024.105798

The on-site assembly process in modular construction (MC) requires precise placement of bulky modules, which involves dangerous and labor-intensive manual work in the current practice. This study aims to automate the process by designing a hybrid pose adjustment (HyPA) robot to achieve complete pose control of the module. To this end, this paper presents the mechanism design and working principle of the HyPA system, demonstrating that module position control, leveling control, steering control, and sway damping can be achieved. The modeling of the HyPA robot is also presented, including the essential parameters to define the model and the construction of the relevant mathematical expressions. Furthermore, a model-based motion generation scheme is proposed to validate the working principle, which combines feedforward motion planning and feedback error correction. Lastly, functionality verification is conducted through both simulation and hardware tests, showcasing the capability of the HyPA robot to perform desired translation and steering angle change while maintaining horizontal leveling.

模块化建筑（MC）的现场组装过程需要精确放置笨重的模块，而目前的做法涉及危险和劳动密集型的手工作业。本研究旨在通过设计一种混合姿态调整（HyPA）机器人来实现模块的完全姿态控制，从而实现该过程的自动化。为此，本文介绍了 HyPA 系统的机构设计和工作原理，证明可以实现模块位置控制、水平控制、转向控制和摇摆阻尼。本文还介绍了 HyPA 机器人的建模，包括定义模型的基本参数和相关数学表达式的构建。此外，还提出了一种基于模型的运动生成方案来验证其工作原理，该方案结合了前馈运动规划和反馈误差校正。最后，通过仿真和硬件测试进行了功能验证，展示了 HyPA 机器人在保持水平水平的同时执行所需的平移和转向角变化的能力。

引用次数: 0

Process scheduling for prefabricated construction based on multi-objective optimization algorithm 基于多目标优化算法的预制建筑工艺调度

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

Automation in Construction

Pub Date : 2024-10-14 DOI: 10.1016/j.autcon.2024.105809

Prefabricated construction has become an increasingly important focus area in the development of the construction industry. Determining an optimal construction process scheduling program is an urgent challenge during the project execution stage. This paper presents a multi-objective optimization problem with the objective function of minimizing the total construction time and maximizing the coordinated scheduling coefficient, and proposes a non-dominated sorting genetic algorithm based on the subspecies differentiation strategy (SD-NSGA) to solve the problem. The algorithm extends the competition phenomenon at the individual level to the subpopulation level in the traditional genetic algorithm (GA). The results demonstrate that SD-NSGA exhibits superior optimization capabilities. Compared with the initial scheme of a real residential construction project, the total working time is shortened by 35.49% and the integrated dispatch factor is increased by 365.79%. Therefore, the proposed algorithm can offer a valuable reference for determining scheduling plans in practical engineering projects.¹

预制建筑已成为建筑业发展中一个日益重要的重点领域。在项目实施阶段，如何确定最优的施工过程调度方案是一个亟待解决的难题。本文提出了一个目标函数为总施工时间最小化和协调调度系数最大化的多目标优化问题，并提出了一种基于亚种分化策略的非支配排序遗传算法（SD-NSGA）来解决该问题。该算法将传统遗传算法（GA）中个体层面的竞争现象扩展到子种群层面。结果表明，SD-NSGA 表现出卓越的优化能力。与实际住宅建筑项目的初始方案相比，总工作时间缩短了 35.49%，综合调度系数提高了 365.79%。因此，所提出的算法可为实际工程项目中确定调度计划提供有价值的参考1。

引用次数: 0

Prompt-based automation of building code information transformation for compliance checking 基于提示的自动化建筑规范信息转换，用于合规性检查

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

Automation in Construction

Pub Date : 2024-10-11 DOI: 10.1016/j.autcon.2024.105817

Transforming building code information into a machine-processable format is essential for automated compliance checking, yet it presents significant challenges. A prompt-based framework was developed to automate the conversion into a logic programming language. Its effectiveness was assessed by testing the framework on 51 requirements from the International Building Code (IBC) 2015, achieving 97.37 % precision and 95.88 % recall at the logic clause level, with only 2 % of the data used for training. Further testing on crash report transformation enhanced efficiency, reducing the average code generation time to approximately 60.8 s, thereby achieving a 27.8 % time savings compared to existing rule-based methods. This paper contributes to the body of knowledge by introducing an effective, versatile, and user-friendly approach to automated building code information transformation, markedly decreasing the reliance on training data, time, and manual efforts.

将建筑规范信息转换为机器可处理的格式对于自动合规性检查至关重要，但这也带来了巨大的挑战。我们开发了一个基于提示的框架，用于将信息自动转换为逻辑编程语言。通过对 2015 年《国际建筑规范》（IBC）中的 51 项要求进行测试，评估了该框架的有效性，在逻辑条款层面实现了 97.37% 的精确度和 95.88% 的召回率，仅使用了 2% 的数据进行训练。对碰撞报告转换的进一步测试提高了效率，将平均代码生成时间减少到约 60.8 秒，因此与现有的基于规则的方法相比节省了 27.8% 的时间。本文介绍了一种有效、通用、用户友好的建筑代码信息自动转换方法，显著减少了对训练数据、时间和人工的依赖，为相关知识体系做出了贡献。

引用次数: 0

Blockchain-integrated zero-knowledge proof system for privacy-preserving near-miss reporting in construction projects 区块链集成零知识证明系统，用于建筑项目中保护隐私的险情报告

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

Automation in Construction

Pub Date : 2024-10-11 DOI: 10.1016/j.autcon.2024.105825

Effective management of near-miss data is essential for proactive safety practices in construction. Traditional reporting and management methods face challenges such as data loss, susceptibility to manipulation, and poor traceability, which undermine their reliability and collaborative efforts. Blockchain technology can enhance data integrity, security, transparency, and reliability in safety data management. However, conventional Layer-1 blockchain systems require third-party verification processes, compromising participant anonymity—crucial for effective near-miss reporting and incur high transaction fees, presenting several practical concerns. To address these issues, this paper developed and tested a zero-knowledge proof and Layer-2 blockchain integrated system for near-miss reporting. This system was validated through a proof-of-concept and hypothetical case study, achieving perfect unlinkability with a degree of anonymity scored at d = 1 and reducing the cost of report submission to USD 0.0011. These advances significantly contribute toward proactive safety management in construction by facilitating safe reporting environments and cost-effective near-miss management.

有效管理险情数据对于在施工中采取积极主动的安全措施至关重要。传统的报告和管理方法面临着数据丢失、易被篡改和可追溯性差等挑战，这些问题削弱了其可靠性和协作性。区块链技术可以提高安全数据管理的数据完整性、安全性、透明度和可靠性。然而，传统的第一层区块链系统需要第三方验证过程，这就损害了参与者的匿名性--这对有效报告近乎失误至关重要，而且会产生高昂的交易费用，从而带来一些实际问题。为解决这些问题，本文开发并测试了一个零知识证明和第 2 层区块链集成系统，用于险情报告。该系统通过概念验证和假设案例研究进行了验证，实现了完美的不可链接性，匿名度为 d = 1，并将报告提交成本降至 0.0011 美元。通过促进安全报告环境和经济高效的险情管理，这些进展极大地推动了建筑业的主动安全管理。

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引用次数: 0

Data-driven generative contextual design model for building morphology in dense metropolitan areas 密集都市区建筑形态的数据驱动生成式情境设计模型

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

Automation in Construction

Pub Date : 2024-10-11 DOI: 10.1016/j.autcon.2024.105820

Generative design has been instrumental in expanding designers' ability to create diverse alternatives. However, the current generative building morphology design presents two broad weaknesses. Firstly, it fails to consider the interaction between a design and its backdrop context, particularly in high-density metropolitan areas. Secondly, it fails to harness existing design knowledge embedded in existing designs. This paper aims to develop a data-driven generative design model: VmRF, which can learn from existing designs and generate plausible and contextual building morphologies. The model consists of a variational autoencoder (VAE) to compress high-dimensional building morphology datasets into low-dimensional building morphology datasets and a multivariate random forest (mRF) to identify explainable relationships between design parameters and morphology patterns. Performance evaluation shows the superiority of the VmRF model in terms of training speed and prediction fitness. Consequently, the proposed model promotes enhanced design efficiency, innovation in contextual awareness, and evidence-based decision-making in building morphology design.

生成式设计有助于提高设计师创造多样化替代方案的能力。然而，目前的建筑形态生成设计存在两大缺陷。首先，它没有考虑到设计与其背景环境之间的相互作用，尤其是在高密度的大都市地区。其次，它未能利用现有设计中蕴含的现有设计知识。本文旨在开发一种数据驱动的生成设计模型：VmRF，它可以从现有设计中学习，生成合理的、符合实际情况的建筑形态。该模型由一个变异自动编码器（VAE）和一个多变量随机森林（mRF）组成，前者用于将高维建筑形态数据集压缩为低维建筑形态数据集，后者用于识别设计参数与形态模式之间的可解释关系。性能评估表明，VmRF 模型在训练速度和预测适配性方面都具有优势。因此，所提出的模型有助于提高设计效率、创新情境意识以及在建筑形态设计中做出基于证据的决策。

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引用次数: 0

Graph-based intelligent accident hazard ontology using natural language processing for tracking, prediction, and learning 使用自然语言处理的基于图形的智能事故隐患本体，用于跟踪、预测和学习

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

Automation in Construction

Pub Date : 2024-10-10 DOI: 10.1016/j.autcon.2024.105800

This paper addresses the challenge of dispersed accident-related information on construction sites, which hinders consensus among employers, workers, supervisors, and society. A robust NLP-based framework is presented to analyze and structure accident-related textual data into a comprehensive knowledge base that reveals accident patterns and risk information. Accident scenarios, including frequency and severity scores, are structured into a graph database through knowledge modeling, establishing an ontology to elucidate keyword relationships. Network analysis identifies accident patterns, quantifies scenario likelihood and severity, and predicts criticality, forming an accident hazard ontology. This vectorized ontology supports accident tracking, prediction, and learning with potential applications. The framework ensures reliable data integration, real-time hazard assessment, and proactive safety measures.

建筑工地上与事故相关的信息非常分散，这阻碍了雇主、工人、监理和社会之间达成共识，本文针对这一难题提出了解决方案。本文提出了一个基于 NLP 的强大框架，用于分析与事故相关的文本数据并将其结构化，形成一个全面的知识库，揭示事故模式和风险信息。通过知识建模，将事故场景（包括频率和严重程度评分）结构化为图数据库，建立本体论以阐明关键字关系。网络分析可识别事故模式，量化情景可能性和严重性，并预测临界度，从而形成事故危害本体。这一矢量化本体支持事故跟踪、预测和学习，并具有潜在的应用价值。该框架可确保可靠的数据集成、实时危险评估和前瞻性安全措施。

引用次数: 0

AI model for analyzing construction litigation precedents to support decision-making 用于分析建筑诉讼先例以支持决策的人工智能模型

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

Automation in Construction

Pub Date : 2024-10-10 DOI: 10.1016/j.autcon.2024.105824

Litigation among stakeholders in construction projects has a significantly negative impact on successful project completion and overall performance. Prompt decision-making in relation to litigation is crucial, but the manual review of extensive document sets is time-consuming. In this paper, the natural language processing (NLP) technique was applied to litigation data to develop a model for case summarization and winner prediction. By automatically summarizing the data and predicting litigation outcomes, the proposed model aids practitioners in making timely decisions and enhances document management during disputes. This paper contributes to existing knowledge in two ways. Firstly, the model aids practitioners in making timely decisions about proceeding with litigation. Secondly, unlike previous studies that manually processed raw data such as contracts and specifications, this study utilized NLP to process raw litigation case data automatically. As big data becomes increasingly common, the methodology employed in this study holds academic significance.

建筑项目中利益相关者之间的诉讼对项目的顺利完成和整体绩效有很大的负面影响。及时做出与诉讼相关的决策至关重要，但人工审查大量文件集非常耗时。本文将自然语言处理（NLP）技术应用于诉讼数据，开发了一个用于案件总结和胜诉者预测的模型。通过自动总结数据和预测诉讼结果，所提出的模型可帮助从业人员及时做出决策，并加强争议期间的文档管理。本文在两个方面对现有知识做出了贡献。首先，该模型有助于从业人员及时做出诉讼决策。其次，与以往人工处理合同和说明书等原始数据的研究不同，本研究利用 NLP 自动处理原始诉讼案件数据。随着大数据的日益普及，本研究采用的方法具有重要的学术意义。

引用次数: 0

Integrated operation centers for storage and repair of imported precast modules 用于储存和维修进口预制模块的综合运营中心

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

Automation in Construction

Pub Date : 2024-10-10 DOI: 10.1016/j.autcon.2024.105815

Modular construction is recognized as a promising solution to the pressing housing demands of densely populated cities. However, temporary storage of modules in urban environments and the risk of damage during transportation present significant supply chain challenges. Some governments have begun investing in integrated operation centers (IOCs) to provide module storage and repair services. However, there lacks an effective planning framework for IOC establishment and operation. This paper formulates a bi-level programming model that comprehensively considers land availability, budget limitation, and government–contractor interactions. A particle swarm optimization based algorithm is developed and validated through a Hong Kong case study. Computational experiments provide governments with valuable managerial implications regarding IOC investment budget, number and locations of IOCs, and services provided by IOCs. Overall, the proposed models, solutions, and recommendations are expected to facilitate the just-in-time cross-border delivery of precast modules in densely populated cities.

模块化建筑被认为是解决人口稠密城市迫切住房需求的一种可行方案。然而，模块在城市环境中的临时存储和运输过程中的损坏风险给供应链带来了巨大挑战。一些政府已开始投资综合运营中心（IOCs），以提供模块存储和维修服务。然而，目前还缺乏一个有效的规划框架来建立和运营综合运营中心。本文提出了一个双层规划模型，综合考虑了土地可用性、预算限制以及政府与承包商之间的互动。本文开发了一种基于粒子群优化的算法，并通过香港的案例研究进行了验证。计算实验为政府提供了有关增支经营成本投资预算、增支经营成本的数量和地点以及增支经营成本提供的服务等宝贵的管理启示。总之，所提出的模型、解决方案和建议有望促进人口稠密城市预制模块的及时跨境交付。

引用次数: 0

Deep learning-based YOLO for crack segmentation and measurement in metro tunnels 基于深度学习的 YOLO，用于地铁隧道裂缝分割和测量

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

Automation in Construction

Pub Date : 2024-10-09 DOI: 10.1016/j.autcon.2024.105818

To address the increasing issue of cracks in metro shield tunnels, this paper proposes the YOLOv8-GSD model, which integrates DySnakeConv, BiLevelRoutingAttention, and the Gather-and-Distribute Mechanism with the YOLOv8 algorithm. This model is designed for detecting and segmenting cracks in tunnel linings and employs a pixel grouping method to measure crack length and width. Using a real crack dataset from a subway section in Suzhou, China, comparative experiments with YOLOv8x, BlendMask, SOLOv2, and YOLACT demonstrate that YOLOv8-GSD excels in segmentation performance (AP of 82.4 %) and accuracy (IoU of 0.812). The measured crack dimensions show an error within 5 % compared to actual values, confirming the model's effectiveness. These results highlight the potential of YOLOv8-GSD for enhancing the maintenance and safety of metro tunnels.

针对地铁盾构隧道中日益严重的裂缝问题，本文提出了 YOLOv8-GSD 模型，该模型将 DySnakeConv、BiLevelRoutingAttention 和 Gather-and-Distribute Mechanism 与 YOLOv8 算法集成在一起。该模型专为检测和分割隧道衬砌裂缝而设计，采用像素分组法测量裂缝长度和宽度。通过使用来自中国苏州地铁路段的真实裂缝数据集，与 YOLOv8x、BlendMask、SOLOv2 和 YOLACT 的对比实验表明，YOLOv8-GSD 在分割性能（AP 为 82.4%）和准确性（IoU 为 0.812）方面表现出色。测得的裂缝尺寸与实际值相比误差在 5% 以内，证明了模型的有效性。这些结果凸显了 YOLOv8-GSD 在提高地铁隧道维护和安全性方面的潜力。

引用次数: 0

Curtain wall frame segmentation using a dual-flow aggregation network: Application to robot pose estimation 使用双流聚合网络进行幕墙框架分割：机器人姿态估计应用

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

Automation in Construction

Pub Date : 2024-10-09 DOI: 10.1016/j.autcon.2024.105816

In the field of curtain wall construction, manual installation presents significant safety hazards and suffers from low efficiency, while automated installation is constrained by the limited localization capabilities of curtain wall installation robots. In this paper, an automated installation solution based on machine vision is proposed, and a detailed discussion of several steps involved is provided. To locate the installation area, DANF, a deep learning-based dual-flow aggregation network designed for curtain wall frame segmentation, is proposed. It employs Transformer for global analysis and CNNs for detailed feature extraction to handle curtain wall frame structures. On the dataset constructed in this paper, DANF achieves an IoU of 85.19 % with a parameter count of only 4.24 M, demonstrating higher accuracy compared to other algorithms. Additionally, a pose-solving method based on the semantic segmentation results of the curtain wall frame is designed to adapt to curtain wall installation scenarios.

在幕墙建筑领域，人工安装存在严重的安全隐患且效率低下，而自动安装则受限于幕墙安装机器人有限的定位能力。本文提出了一种基于机器视觉的自动安装解决方案，并对其中的几个步骤进行了详细讨论。为了定位安装区域，本文提出了基于深度学习的双流聚合网络 DANF，该网络专为幕墙框架分割而设计。它采用 Transformer 进行全局分析，采用 CNN 进行细节特征提取，以处理幕墙框架结构。在本文构建的数据集上，DANF 实现了 85.19 % 的 IoU，而参数数量仅为 4.24 M，与其他算法相比具有更高的准确性。此外，还设计了一种基于幕墙框架语义分割结果的姿势解决方法，以适应幕墙安装场景。

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Automation in Construction

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