Automation in Construction最新文献

英文中文

Design and construction automation of reconfigurable timber slabs 可重组木板的设计和施工自动化

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

Automation in Construction

Pub Date : 2024-11-16 DOI: 10.1016/j.autcon.2024.105872

Anja Kunic, Davide Angeletti, Giuseppe Marrone, Roberto Naboni

Structural adaptivity and readiness for change are some of the key enablers of resilient and sustainable architecture. This paper presents an approach to the design and construction of reconfigurable timber slabs, termed ReconWood Slabs, which integrate a stress-driven design approach and cyber-physical construction processes to enhance data-informed circularity. Using advanced computational design tools, the research outlines a workflow for generating optimised slab configurations that balance structural performance with material reusability. The slabs, composed of modular beams connected via reversible steel fasteners, are designed for easy disassembly and reconfiguration, promoting material reuse across multiple building lifecycles. The paper demonstrates the system's potential through the construction of three slab structures employing Mixed Reality for material data tracking and assembly. The three structures store nearly a ton of CO2eq in reusable parts.

结构适应性和变革准备是弹性和可持续建筑的一些关键因素。本文介绍了一种设计和建造可重新配置木板（称为 "ReconWood Slabs"）的方法，该方法整合了应力驱动设计方法和网络物理建造流程，以提高数据知情的循环性。这项研究利用先进的计算设计工具，概述了生成优化板块配置的工作流程，从而在结构性能与材料可重复使用性之间取得平衡。楼板由通过可逆钢紧固件连接的模块化梁组成，其设计便于拆卸和重新配置，促进了多个建筑生命周期内的材料再利用。论文通过建造三个采用混合现实技术进行材料数据跟踪和组装的楼板结构，展示了该系统的潜力。这三个结构的可重复使用部件储存了近一吨的二氧化碳当量。

引用次数: 0

Knowledge graph for safety management standards of water conservancy construction engineering 水利建设工程安全管理标准知识图谱

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

Automation in Construction

Pub Date : 2024-11-16 DOI: 10.1016/j.autcon.2024.105873

Yun Chen , Gengyang Lu , Ke Wang , Shu Chen , Chenfei Duan

With the increasing demand for water conservancy engineering (WCE), the number of safety accidents during construction has continued to rise, requiring an urgent improvement in construction safety. The existing safety management regulations for water conservancy construction engineering (WCCE) comprise a considerable amount of text, with cross-references between different standards severely reducing their use efficiency. To address this issue, this paper proposes an ALBERT-BiLSTM-CRF model based on textual data from WCCE safety management standards. ALBERT, a lightweight pretrained language model, is integrated with the BiLSTM-CRF to construct an intelligent text entity recognition method. Association rules are used to extract entity relationships, and a knowledge graph representing the WCCE safety management standards is established. The results show that the ALBERT-BiLSTM-CRF algorithm improves the precision, with a recognition accuracy exceeding 85 %. Case studies validate that the constructed knowledge graph can quickly query safety standard knowledge, aiding in the generation of safety measures.

随着对水利工程（WCE）需求的不断增加，施工过程中的安全事故数量持续上升，急需提高施工安全。现有的水利建筑工程（WCCE）安全管理条例包含大量文本，不同标准之间的交叉引用严重降低了其使用效率。针对这一问题，本文提出了基于 WCCE 安全管理标准文本数据的 ALBERT-BiLSTM-CRF 模型。ALBERT 是一种轻量级预训练语言模型，它与 BiLSTM-CRF 相结合，构建了一种智能文本实体识别方法。关联规则用于提取实体关系，并建立了代表 WCCE 安全管理标准的知识图谱。结果表明，ALBERT-BiLSTM-CRF 算法提高了识别精度，识别准确率超过 85%。案例研究验证了所构建的知识图谱可以快速查询安全标准知识，帮助生成安全措施。

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引用次数: 0

BIM-blockchain integrated automatic asset tracking and delay propagation analysis for prefabricated construction projects 预制建筑项目的 BIM-区块链集成自动资产跟踪和延迟传播分析

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

Automation in Construction

Pub Date : 2024-11-15 DOI: 10.1016/j.autcon.2024.105854

Yaxian Dong , Yuqing Hu , Shuai Li , Jiannan Cai , Zhu Han

Asset tracking is crucial for managing prefabricated construction projects, as delayed deliveries might disrupt interdependent offsite and onsite activities, causing economic losses and disputes. To clarify liabilities, tamperproof asset tracking and delay propagation analysis are necessary. To achieve this, a BIM-blockchain integrated framework via smart contracts is proposed given rich information in BIM and blockchain's immutable records. First, asset information and interdependent activity schedule are automatically transmitted from BIM to blockchain. Then, QR codes are generated and attached to physical assets for tracking. If any delays, compiled smart contracts will automatically derive propagated impacts on offsite and onsite activities considering their interdependencies and proactively notify relevant parties. Affected activities with assets, certification time, and responsible parties are automatically visualized in 4D BIM for timely collaboration. The developed IFC-Ethereum prototype demonstrates the framework's feasibility and effectiveness, reducing coordination overhead costs and time. Traceable records help further calculate parties' penalties and compensation.

资产跟踪对于管理预制建筑项目至关重要，因为延迟交付可能会扰乱相互依赖的场外和现场活动，造成经济损失和纠纷。为了明确责任，有必要进行防篡改资产跟踪和延迟传播分析。为此，鉴于 BIM 中的丰富信息和区块链的不可篡改记录，我们提出了一个通过智能合约实现的 BIM 区块链集成框架。首先，资产信息和相互依存的活动计划自动从 BIM 传输到区块链。然后，生成二维码并附加到实物资产上以进行跟踪。如果出现任何延误，编译后的智能合约将自动推导出对场外和场内活动的影响，并考虑到它们之间的相互依赖关系，主动通知相关方。受影响活动的资产、认证时间和责任方都会在 4D BIM 中自动可视化，以便及时协作。所开发的 IFC-Ethereum 原型展示了该框架的可行性和有效性，减少了协调的间接成本和时间。可追溯记录有助于进一步计算各方的处罚和赔偿。

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引用次数: 0

Smart embedded technologies and materials for enhanced pavement management 用于加强路面管理的智能嵌入式技术和材料

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

Automation in Construction

Pub Date : 2024-11-13 DOI: 10.1016/j.autcon.2024.105865

Atena Karbalaei Mohammad Hossein, Amir Golroo, Medya Akhoundzadeh

The integration of smart technologies is set to revolutionize pavement data collection and analysis, leading to more efficient decision-making in Pavement Management Systems (PMS). Smart pavements, featuring embedded sensors, offer continuous streams of high-quality real-time data, enhancing the PMS data analysis process. This paper provides a detailed examination of these embedded smart systems, discussing their technologies, applications, and potential impacts on pavement management. The study highlights the role of smart materials in pavement engineering, offering self-sensing, self-healing, and energy-harvesting capabilities. It investigates sensor technologies for monitoring pavement conditions, focusing on both on-surface and below-surface sensors for comprehensive data collection. Future research directions emphasize advanced data management systems, sensor durability enhancement, economic modeling, standardization efforts, energy-efficient technologies, and pilot programs for real-world testing. This research provides insights into smart pavement advancements and challenges, paving the way for improved road infrastructure efficiency and sustainability.

智能技术的集成将彻底改变路面数据的收集和分析，从而提高路面管理系统（PMS）的决策效率。以嵌入式传感器为特色的智能路面可提供连续的高质量实时数据流，从而增强路面管理系统的数据分析过程。本文详细介绍了这些嵌入式智能系统，讨论了它们的技术、应用以及对路面管理的潜在影响。研究强调了智能材料在路面工程中的作用，它们具有自传感、自修复和能量收集功能。研究还探讨了用于监测路面状况的传感器技术，重点是用于全面数据收集的地表和地下传感器。未来的研究方向强调先进的数据管理系统、传感器耐久性增强、经济建模、标准化工作、节能技术以及用于实际测试的试点项目。这项研究为智能路面的进步和挑战提供了见解，为提高道路基础设施的效率和可持续性铺平了道路。

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引用次数: 0

Terrain-adaptive motion planner for articulated construction vehicles in unstructured environments 非结构化环境中铰接式工程车辆的地形自适应运动规划器

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

Automation in Construction

Pub Date : 2024-11-13 DOI: 10.1016/j.autcon.2024.105864

Tengchao Huang , Xuanwei Chen , Huosheng Hu , Shuang Song , Guifang Shao , Qingyuan Zhu

In this paper, a terrain-adaptive motion planner is developed specifically for articulated construction vehicles (ACVs) to address instability issues caused by elevation changes on unstructured construction sites—challenges that traditional 2D motion planners struggle to manage effectively. The proposed planner adopts a modular framework, incorporating a terrain elevation model, an articulated vehicle kinematic model, and a posture response model. These models collaboratively capture the dynamic interactions between the vehicle and the terrain. The planner utilizes a multi-objective evaluation function to enhance the vehicle's 3D motion stability, especially in challenging terrains. By considering real-time vehicle-terrain interactions, this function estimates and optimizes the vehicle's stability. The planner's effectiveness is validated through field tests with a scaled-down ACV prototype, demonstrating significant improvements in stability and confirming its potential for practical application on unstructured terrains.

本文专门为铰接式工程车（ACV）开发了一种地形适应运动规划器，以解决非结构化建筑工地上高程变化引起的不稳定性问题--传统的二维运动规划器很难有效地应对这些挑战。拟议的规划器采用模块化框架，包含地形高程模型、铰接式车辆运动学模型和姿态响应模型。这些模型共同捕捉车辆与地形之间的动态交互。规划器利用多目标评估函数来增强车辆的三维运动稳定性，尤其是在具有挑战性的地形中。通过考虑车辆与地形的实时交互作用，该功能可估算并优化车辆的稳定性。通过对一辆缩小版 ACV 原型车进行实地测试，验证了该规划器的有效性，证明其稳定性有了显著提高，并证实了其在非结构化地形上的实际应用潜力。

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引用次数: 0

Automatic registration of large-scale building point clouds with high outlier rates 自动登记离群率高的大规模建筑点云

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

Automation in Construction

Pub Date : 2024-11-13 DOI: 10.1016/j.autcon.2024.105870

Raobo Li , Shu Gan , Xiping Yuan , Rui Bi , Weidong Luo , Cheng Chen , Zhifu Zhu

Point cloud registration plays a crucial role in processing large-scale building point cloud data. However, existing registration algorithms face challenges in effectively handling outliers in descriptor-based correspondence. This paper presents an automatic registration method for large-scale building point clouds that is capable of achieving swift and accurate registration without the need for initial guessing. The method employs a two-step matching optimization approach: coarse (two-point)-to-fine (three-point), selecting matches based on two-point reliability and three-point consistency. Spatial transformation parameters are broken down into rotations and translations. A progressively optimized kernel function is proposed for estimating rotation, while a clustering confidence algorithm computes translation. Comprehensive experiments were conducted using real-world data. The results indicate that the approach swiftly and accurately estimates optimal outcomes when processing large-scale building point clouds with outlier rates up to 99%. Compared to six existing registration methods, the proposed approach reduces rotation error by 6.15% and translation error by 12.83%, while improving efficiency by 2.57%.

点云注册在处理大规模建筑点云数据中起着至关重要的作用。然而，现有的注册算法在有效处理基于描述符对应的异常值方面面临挑战。本文提出了一种大规模建筑点云自动注册方法，无需初始猜测即可实现快速准确的注册。该方法采用两步匹配优化法：从粗（两点）到细（三点），根据两点可靠性和三点一致性选择匹配。空间变换参数分为旋转和平移。为估算旋转提出了一个逐步优化的核函数，而计算平移则采用聚类置信算法。我们使用真实世界的数据进行了综合实验。结果表明，在处理大规模建筑点云时，该方法能迅速、准确地估计出最佳结果，离群率高达 99%。与现有的六种注册方法相比，所提出的方法减少了 6.15% 的旋转误差和 12.83% 的平移误差，同时提高了 2.57% 的效率。

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引用次数: 0

Procedural modeling of historic buildings' timber frames for HBIM based on carpenters' architectural rules 基于木匠建筑规则的 HBIM 历史建筑木结构程序建模

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

Automation in Construction

Pub Date : 2024-11-12 DOI: 10.1016/j.autcon.2024.105862

Feng Xu, Yexin Zou, Yangwenzhao Li

The inherent complexity of historic buildings, particularly their internal structures, presents significant challenges to the efficiency of digital model creation. This paper aims to enhance modeling efficiency by automating the creation of timber frames using a procedural modeling method. It translates the architectural rules used by local carpenters into modeling rules for procedural modeling, allowing for the automatic generation of a digital model that closely resembles the actual timber frame with a few simple constraints. While some manual identification and correction are still necessary, the workload is significantly reduced compared to traditional methods, and the precision meets the requirements of HBIM. The results show that this approach greatly improves the efficiency of modeling large-scale historic buildings and serves as a valuable complement to traditional HBIM methods. Future research will focus on enhancing the integrity and diversity of the models, such as expanding the range of supported traditional building types.

历史建筑本身的复杂性，尤其是其内部结构，给数字模型创建的效率带来了巨大挑战。本文旨在通过使用程序建模方法自动创建木框架来提高建模效率。它将当地木匠使用的建筑规则转化为程序建模的建模规则，只需几个简单的约束条件，就能自动生成与实际木结构非常相似的数字模型。虽然仍需要一些人工识别和修正，但与传统方法相比，工作量大大减少，精度也达到了 HBIM 的要求。结果表明，这种方法大大提高了大型历史建筑建模的效率，是对传统 HBIM 方法的重要补充。未来的研究将侧重于增强模型的完整性和多样性，例如扩大支持的传统建筑类型的范围。

引用次数: 0

Exploratory study on time-delayed excavator teleoperation in virtual lunar construction simulation: Task performance and operator behavior 虚拟月球施工模拟中的延时挖掘机远程操作探索性研究：任务性能和操作员行为

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

Automation in Construction

Pub Date : 2024-11-12 DOI: 10.1016/j.autcon.2024.105871

Miran Seo , Samraat Gupta , Youngjib Ham

Building sustainable habitats on the moon has been planned for decades. However, applying fully automated construction systems is still challenging in altered environments. Teleoperation, which is the remote control of the machine, can serve as an intermediate phase before achieving fully autonomous systems. Since the teleoperation between operators on the earth-ground and robots on the lunar surface introduces inevitable communication time delays under a deep space network system, it is important to understand its impact on task performance and operator behaviors in teleoperated construction tasks. This paper develops a simulated lunar environment for excavator teleoperation systems in virtual reality to examine task performance and operator behaviors in time delay conditions. The outcomes indicate that time delays significantly degrade task performance, and the operators modify their control strategies to cope with the time delay conditions. The findings will contribute to understanding human behaviors in time-delayed teleoperation of lunar construction tasks.

在月球上建造可持续的居住地已经计划了几十年。然而，在改变了的环境中应用全自动建筑系统仍然具有挑战性。远程操作，即对机器的远程控制，可以作为实现完全自主系统之前的一个中间阶段。由于在深空网络系统下，地面操作员和月球表面机器人之间的远程操作会带来不可避免的通信时间延迟，因此了解其对远程操作建筑任务中任务性能和操作员行为的影响非常重要。本文在虚拟现实中为挖掘机远程操作系统开发了一个模拟月球环境，以研究时延条件下的任务性能和操作员行为。结果表明，时间延迟会明显降低任务性能，操作员会修改其控制策略以应对时间延迟条件。这些研究结果将有助于理解人类在延时遥控月球施工任务中的行为。

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引用次数: 0

Performance comparison of retrieval-augmented generation and fine-tuned large language models for construction safety management knowledge retrieval 检索增强生成和微调大语言模型在建筑安全管理知识检索中的性能比较

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

Automation in Construction

Pub Date : 2024-11-12 DOI: 10.1016/j.autcon.2024.105846

Jungwon Lee , Seungjun Ahn , Daeho Kim , Dongkyun Kim

Construction safety standards are in unstructured formats like text and images, complicating their effective use in daily tasks. This paper compares the performance of Retrieval-Augmented Generation (RAG) and fine-tuned Large Language Model (LLM) for the construction safety knowledge retrieval. The RAG model was created by integrating GPT-4 with a knowledge graph derived from construction safety guidelines, while the fine-tuned LLM was fine-tuned using a question-answering dataset derived from the same guidelines. These models' performance is tested through case studies, using accident synopses as a query to generate preventive measurements. The responses were assessed using metrics, including cosine similarity, Euclidean distance, BLEU, and ROUGE scores. It was found that both models outperformed GPT-4, with the RAG model improving by 21.5 % and the fine-tuned LLM by 26 %. The findings highlight the relative strengths and weaknesses of the RAG and fine-tuned LLM approaches in terms of applicability and reliability for safety management.

建筑安全标准采用文本和图像等非结构化格式，使其在日常工作中的有效使用变得复杂。本文比较了检索增强生成（RAG）和微调大语言模型（LLM）在建筑安全知识检索方面的性能。RAG 模型是通过将 GPT-4 与源自建筑安全指南的知识图谱整合而创建的，而微调 LLM 则是使用源自相同指南的问题解答数据集进行微调的。这些模型的性能通过案例研究进行了测试，使用事故概要作为查询来生成预防性测量。使用余弦相似度、欧氏距离、BLEU 和 ROUGE 分数等指标对回答进行了评估。结果发现，两种模型的性能都优于 GPT-4，其中 RAG 模型提高了 21.5%，微调 LLM 提高了 26%。研究结果凸显了 RAG 和微调 LLM 方法在安全管理的适用性和可靠性方面的相对优缺点。

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引用次数: 0

Predicting and managing risk interactions and systemic risks in infrastructure projects using machine learning 利用机器学习预测和管理基础设施项目中的风险互动和系统性风险

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

Automation in Construction

Pub Date : 2024-11-10 DOI: 10.1016/j.autcon.2024.105836

Ahmed Moussa , Mohamed Ezzeldin , Wael El-Dakhakhni

Infrastructure projects often encounter performance challenges, such as cost overruns and safety issues, due to complex risk interactions and systemic risks. Existing literature treats risk interactions and systemic risks separately and relies on models that struggle with nonlinearities, adaptability, and practical applications, leading to suboptimal risk management. To address this gap, this paper uses machine learning (ML) algorithms to analyze historical project data and predict the impacts of risk interactions and systemic risks on future projects. The results show that ML-based models provide accurate and practical data-driven predictions of project performance under risk interactions and systemic risks. These findings are valuable for infrastructure project managers seeking to improve risk mitigation strategies and project outcomes. The paper lays also the foundation for future research on leveraging advanced predictive analytics in managing complex project risks more effectively.

由于复杂的风险相互作用和系统性风险，基础设施项目经常会遇到绩效挑战，如成本超支和安全问题。现有文献将风险相互作用和系统性风险分开处理，并依赖于难以解决非线性、适应性和实际应用问题的模型，从而导致了次优的风险管理。为了弥补这一不足，本文使用机器学习（ML）算法分析历史项目数据，预测风险相互作用和系统性风险对未来项目的影响。研究结果表明，基于 ML 的模型可以对风险相互作用和系统性风险下的项目绩效进行准确、实用的数据驱动预测。这些发现对于寻求改善风险缓解策略和项目成果的基础设施项目经理来说非常有价值。本文还为今后研究如何利用先进的预测分析技术更有效地管理复杂的项目风险奠定了基础。

引用次数: 0

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