Automation in Construction最新文献

英文中文

Dual hierarchical attention-enhanced transfer learning for semantic segmentation of point clouds in building scene understanding 用于建筑场景理解中点云语义分割的双分层注意力增强迁移学习

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

Automation in Construction

Pub Date : 2024-10-07 DOI: 10.1016/j.autcon.2024.105799

Limao Zhang , Zeyang Wei , Zhonghua Xiao , Ankang Ji , Beibei Wu

Targeted to the challenge of indoor scene understanding for intelligent devices, this paper question focuses on enhancing accuracy in semantic information extraction. A framework including a dual hierarchical attention network, transfer learning, interpretability analysis, and modeling module is applied to segment and reconstruct the indoor scene. A high-rise as-built building case is used to verify the method, the results show that: (1) the method achieves a high mIoU of 0.970 in point cloud segmentation and outperforms state-of-the-art methods, both demonstrating strong performance; (2) the method has sound feature extraction and learning ability in term of the interpretive analysis; (3) the method accelerates by 37 % than manual operations, achieving higher accuracy and efficiency. Overall, the method provides an effective solution to segment multi-class objects for indoor scene understanding and can serve as a basis for automated modeling to contribute to an accurate BIM model with great potential for practical application.

针对智能设备在室内场景理解方面所面临的挑战，本文的研究重点是提高语义信息提取的准确性。本文采用了一个包括双分层注意力网络、迁移学习、可解释性分析和建模模块的框架来分割和重建室内场景。通过一个高层建筑竣工案例对该方法进行了验证，结果表明(1) 该方法在点云分割方面的 mIoU 高达 0.970，优于最先进的方法，表现出强劲的性能；(2) 该方法在可解释性分析方面具有良好的特征提取和学习能力；(3) 该方法比人工操作加快了 37%，实现了更高的精度和效率。总之，该方法为室内场景理解中的多类物体分割提供了有效的解决方案，可作为自动建模的基础，有助于建立精确的 BIM 模型，具有巨大的实际应用潜力。

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引用次数: 0

Corrigendum to “Automated geometric reconstruction and cable force inference for cable-net structures using 3D point clouds” [Automation in Construction, 165 (2024), 105543] 利用三维点云对索网结构进行自动几何重建和索力推断"[《建筑自动化》，165 (2024)，105543] 更正

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

Automation in Construction

Pub Date : 2024-10-07 DOI: 10.1016/j.autcon.2024.105821

Siwei Lin , Liping Duan , Jiming Liu , Xiao Xiao , Ji Miao , Jincheng Zhao

引用次数: 0

Non-invasive vision-based personal comfort model using thermographic images and deep learning 利用热成像图像和深度学习建立基于视觉的无创个人舒适度模型

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

Automation in Construction

Pub Date : 2024-10-05 DOI: 10.1016/j.autcon.2024.105811

Vincent Gbouna Zakka , Minhyun Lee , Ruixiaoxiao Zhang , Lijie Huang , Seunghoon Jung , Taehoon Hong

An efficient method for predicting occupants' thermal comfort is crucial for developing optimal environmental control strategies while minimizing energy consumption in buildings. This paper presents a non-invasive vision-based personal comfort model that integrates thermographic images and deep learning. Unlike previous studies, the entire thermographic image of the upper body is directly used during model training, minimizing complex data processing and maximizing the use of rich skin temperature distribution. The proposed method is validated using thermographic images and corresponding thermal sensation votes (TSV) from 10 participants under different experimental conditions. Results show that the model based on a 3-point TSV scale achieves exceptional classification performance with an average accuracy of 99.51 %, outperforming existing models. The model performance using a 7-point TSV scale is slightly lower, with an average accuracy of 89.90 %. This method offers potential for integrating thermal comfort models into real-time building environmental control, optimizing occupant comfort and energy consumption.

一种预测居住者热舒适度的有效方法，对于制定最佳环境控制策略并最大限度降低建筑物能耗至关重要。本文介绍了一种基于视觉的非侵入式个人舒适度模型，该模型集成了热成像图像和深度学习。与以往的研究不同，该模型在训练过程中直接使用了上半身的整个热成像图像，从而最大限度地减少了复杂的数据处理，并最大限度地利用了丰富的皮肤温度分布。在不同的实验条件下，使用 10 名参与者的热成像图像和相应的热感觉票数（TSV）对所提出的方法进行了验证。结果表明，基于 3 点 TSV 量表的模型取得了优异的分类性能，平均准确率达到 99.51%，优于现有模型。使用 7 点 TSV 量表的模型性能略低，平均准确率为 89.90%。这种方法为将热舒适度模型集成到实时建筑环境控制、优化居住舒适度和能源消耗提供了可能。

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引用次数: 0

Optimizing bucket-filling strategies for wheel loaders inside a dream environment 优化轮式装载机在梦境环境中的铲斗装载策略

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

Automation in Construction

Pub Date : 2024-10-04 DOI: 10.1016/j.autcon.2024.105804

Daniel Eriksson , Reza Ghabcheloo , Marcus Geimer

Reinforcement Learning (RL) requires many interactions with the environment to converge to an optimal strategy, which makes it unfeasible to apply to wheel loaders and the bucket filling problem without using simulators. However, it is difficult to model the pile dynamics in the simulator because of unknown parameters, which results in poor transferability from the simulation to the real environment. Instead, this paper uses world models, serving as a fast surrogate simulator, creating a dream environment where a reinforcement learning (RL) agent explores and optimizes its bucket-filling behavior. The trained agent is then deployed on a full-size wheel loader without modifications, demonstrating its ability to outperform the previous benchmark controller, which was synthesized using imitation learning. Additionally, the same performance was observed as that of a controller pre-trained with imitation learning and optimized on the test pile using RL.

强化学习（RL）需要与环境进行多次交互才能收敛到最佳策略，因此如果不使用模拟器，将其应用于轮式装载机和铲斗装载问题是不可行的。然而，由于参数未知，很难在模拟器中建立桩的动态模型，这导致从模拟到真实环境的可移植性很差。相反，本文使用世界模型作为快速替代模拟器，创建了一个梦境环境，让强化学习（RL）代理探索并优化其铲斗装填行为。然后，将训练好的代理不加修改地部署到全尺寸轮式装载机上，证明其性能优于之前使用模仿学习合成的基准控制器。此外，与使用模仿学习预先训练并使用 RL 在测试桩上进行优化的控制器相比，该控制器也具有相同的性能。

引用次数: 0

Spatio-temporal heat risk analysis in construction: Digital twin-enabled monitoring 建筑时空热风险分析：数字孪生监测

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

Automation in Construction

Pub Date : 2024-10-04 DOI: 10.1016/j.autcon.2024.105805

Yoojun Kim , Youngjib Ham

To effectively mitigate heat risks, it is crucial to pinpoint areas of high vulnerability and assess the severity of heat-related threats to construction workers. This paper advances the understanding of heat risks in construction by mapping the associated risks across time and space to support informed decision-making. This paper presents a framework for heat risk monitoring, enabled by a construction site digital twin. This framework leverages geometric modeling, incorporates real-time weather data from a weather station, and utilizes computational simulations for assessing spatio-temporal heat risks. Its effectiveness was validated through a case study in Stephenville, Texas, USA, where it demonstrated superior fidelity when compared to using the conventional black-globe thermometer. Moreover, the results substantiated that incorporating the spatio-temporal variability of heat risks enhances heat risk surveillance in construction workplaces. This approach offers practical insights into imminent heat-related threats, aiming to prevent potential heat-related accidents in construction.

为有效降低高温风险，必须准确定位建筑工人易受高温影响的区域，并评估与高温有关的威胁的严重程度。本文通过绘制跨时间和空间的相关风险图来支持知情决策，从而加深对建筑业高温风险的理解。本文提出了一个由建筑工地数字孪生系统支持的热风险监测框架。该框架利用几何建模，结合气象站的实时气象数据，并利用计算模拟来评估时空热风险。在美国得克萨斯州斯蒂芬维尔进行的案例研究验证了该框架的有效性，与使用传统的黑球温度计相比，该框架显示出更高的保真度。此外，研究结果还证实，结合热风险的时空变化，可以加强对建筑工作场所热风险的监控。这种方法为了解迫在眉睫的热相关威胁提供了切实可行的见解，旨在预防建筑业中潜在的热相关事故。

引用次数: 0

Data-driven logistics collaboration for prefabricated supply chain with multiple factories 多工厂预制供应链的数据驱动型物流协作

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

Automation in Construction

Pub Date : 2024-10-04 DOI: 10.1016/j.autcon.2024.105802

Yishu Yang , Ying Yu , Chenglin Yu , Ray Y. Zhong

Prefabricated construction is increasingly replacing traditional methods due to its higher productivity, superior quality, and shorter construction time. This paper aims to optimize production and logistics collaboration within a three-tier prefabricated supply chain network to reduce overall costs and enhance response efficiency. A decision model was developed that integrates factory and logistics capacity, on-site assembly sequence, and outsourcing decisions to optimize resource allocation. The model demonstrates superior cost efficiency and resource allocation effectiveness over the Earliest Due Date (EDD) method through a hypothetical case study. This result provides robust decision support for supply chain professionals, offering significant practical implications for cost reduction and resource optimization. Our findings lay a foundation for future studies on supply chain management and optimization under dynamic conditions, offering new perspectives and methodologies.

预制建筑因其更高的生产率、更优的质量和更短的施工时间，正日益取代传统方法。本文旨在优化三级预制供应链网络中的生产和物流协作，以降低总体成本，提高响应效率。本文建立了一个决策模型，将工厂和物流能力、现场组装顺序和外包决策整合在一起，以优化资源配置。通过一个假设案例研究，该模型展示了比最早到期日（EDD）方法更优越的成本效率和资源分配效果。这一结果为供应链专业人士提供了强有力的决策支持，对降低成本和优化资源具有重要的现实意义。我们的研究结果为未来动态条件下的供应链管理和优化研究奠定了基础，提供了新的视角和方法。

引用次数: 0

Deep learning network for indoor point cloud semantic segmentation with transferability 用于室内点云语义分割的可移植性深度学习网络

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

Automation in Construction

Pub Date : 2024-10-04 DOI: 10.1016/j.autcon.2024.105806

Luping Li , Jian Chen , Xing Su , Haoying Han , Chao Fan

Semantic segmentation is crucial for interpreting point cloud data and plays a fundamental role in automating the creation of as-built BIM. Existing neural network models for semantic segmentation often heavily rely on the training dataset, resulting in a significant performance drop when applied to new datasets. This paper presents AttTransNet, a neural network model for automated point cloud semantic segmentation. Its attention-based pooling module improves local feature extraction from point clouds while reducing computational costs. The transfer learning framework enhances segmentation accuracy with minimal training on target datasets. Comparative experiments show that AttTransNet reduces training time by 80 % and improves segmentation accuracy by over 20 % compared with other SOTA methods. Cross-dataset experiments reveal that the transfer learning framework increases accuracy on new datasets by 150 %. By adding semantic information to point clouds, AttTransNet aids BIM modelers with direct reference, encouraging broader application of automated point cloud segmentation in the industry.

语义分割对于解释点云数据至关重要，在自动创建竣工 BIM 中发挥着基础性作用。现有的语义分割神经网络模型通常严重依赖于训练数据集，导致在应用于新数据集时性能大幅下降。本文介绍了用于自动点云语义分割的神经网络模型 AttTransNet。其基于注意力的池化模块可改进点云的局部特征提取，同时降低计算成本。迁移学习框架只需在目标数据集上进行最少的训练，就能提高分割精度。对比实验表明，与其他 SOTA 方法相比，AttTransNet 减少了 80% 的训练时间，提高了 20% 以上的分割准确率。跨数据集实验表明，迁移学习框架在新数据集上的准确率提高了 150%。通过在点云中添加语义信息，AttTransNet 为 BIM 建模人员提供了直接参考，从而促进了自动点云分割在行业中的广泛应用。

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引用次数: 0

Energy-efficient configuration and scheduling framework for electric construction machinery collaboration systems 电动工程机械协作系统的节能配置和调度框架

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

Automation in Construction

Pub Date : 2024-10-03 DOI: 10.1016/j.autcon.2024.105808

Xiaohui Huang , Wanbin Yan , Guibao Tao , Sujiao Chen , Huajun Cao

The electrification of construction machinery has created a perceptible future trend of the development of electric construction machinery collaboration systems (ECMCSs). However, there is a lack of research on energy-efficient operation of ECMCS. This paper proposes a theoretical configuration and scheduling framework promoting the applications of ECMCSs. In the configuration stage, this paper considers the effect of charging time and proposes an electric matching factor to achieve an optimal system configuration. In the scheduling stage, a multi-objective scheduling problem is formulated for achieving energy-efficient system operation, which considers the transport volume, cost and idle time. A validation of the framework was carried out using a case study that found the optimal system solution, while the advantages of the considered ECMCS compared to a fossil fuel-powered system were discussed. The impact of battery and charging technology developments was also assessed. This framework can be widely applied to deployment of ECMCSs.

工程机械的电气化使电动工程机械协作系统（ECMCS）的发展成为一种可感知的未来趋势。然而，关于 ECMCS 节能运行的研究还很缺乏。本文提出了促进 ECMCS 应用的理论配置和调度框架。在配置阶段，本文考虑了充电时间的影响，并提出了电匹配系数，以实现最优系统配置。在调度阶段，本文提出了一个多目标调度问题，以实现节能的系统运行，该问题考虑了运输量、成本和空闲时间。通过案例研究对该框架进行了验证，找到了最佳系统解决方案，同时讨论了所考虑的 ECMCS 与化石燃料动力系统相比的优势。此外，还评估了电池和充电技术发展的影响。该框架可广泛应用于 ECMCS 的部署。

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引用次数: 0

Detecting district heating leaks in thermal imagery: Comparison of anomaly detection methods 从热图像中检测区域供热泄漏：异常检测方法的比较

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

Automation in Construction

Pub Date : 2024-10-02 DOI: 10.1016/j.autcon.2024.105709

Elena Vollmer, Julian Ruck, Rebekka Volk, Frank Schultmann

District heating systems offer means to transport heat to end-energy users through underground pipelines. When leakages occur, a lack of reliable monitoring makes pinpointing their locations a difficult and costly task for network operators. In recent years, aerial thermography has emerged as a means to find leakages as hot-spots, with several papers proposing image analysis algorithms for their detection. While all publications boast high performance metrics, the methods are constructed around very different datasets, making a true comparison impossible.

Using a new set of aerial thermal images from two German cities, this paper implements, improves, and evaluates three anomaly detection methods for leakage detection: triangle-histogram-thresholding, saliency mapping, and local thresholding with filter kernels. The approaches are integrated into a software pipeline with globally applicable pre- and postprocessing, including vignetting correction. While all methods reliably detect thermal anomalies and are suitable for automated leakage detection, triangle-histogram-thresholding is the most robust.

区域供热系统通过地下管道向终端能源用户输送热量。当发生泄漏时，由于缺乏可靠的监测，对于管网运营商来说，确定泄漏位置是一项困难且成本高昂的任务。近年来，航空热成像技术已成为发现泄漏热点的一种手段，多篇论文提出了探测泄漏的图像分析算法。本文利用来自两个德国城市的一组新的航空热图像，实现、改进并评估了三种用于泄漏检测的异常检测方法：三角组图阈值法、显著性映射法和带有滤波核的局部阈值法。这些方法被集成到一个软件流水线中，进行全球适用的前处理和后处理，包括渐晕校正。虽然所有方法都能可靠地检测出热异常，并适用于自动泄漏检测，但三角形组图阈值法最为稳健。

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引用次数: 0

Strategic alignment of BIM and big data through systematic analysis and model development 通过系统分析和模型开发实现 BIM 和大数据的战略协调

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

Automation in Construction

Pub Date : 2024-10-02 DOI: 10.1016/j.autcon.2024.105801

Apeesada Sompolgrunk , Saeed Banihashemi , Hamed Golzad , Khuong Le Nguyen

Organisations increasingly rely on data-driven strategies, utilising analytics to achieve competitive advantages. This paper systematically investigates the integration of big data into Building Information Modeling (BIM) within the Architecture, Engineering, and Construction (AEC) sectors, named “big BIM data.” Employing mixed methods of systematic and bibliometric analysis, it synthesises findings from 125 records published 2013–23. While many studies are at preliminary stages with conceptual or small-scale experimental approaches, the paper categorises its results into four domains: AEC organisational infrastructure, big BIM data (IT) infrastructure, AEC organisational strategic domain, and big BIM data (IT) strategic domain, aligned with the Strategic Alignment Model (SAM), exploring organisational competencies, governance factors, and strategic frameworks. This paper introduces the AEC Organisational - Big BIM Data SAM as the research agenda to implement big BIM data utilisation across AEC industry. This framework thoroughly addresses organisational dynamics while emphasising interconnectedness among individual projects, organisational tiers, and industry-wide standards.

企业越来越依赖于数据驱动战略，利用分析来实现竞争优势。本文系统地研究了在建筑、工程和施工（AEC）领域将大数据整合到建筑信息模型（BIM）中的情况，并将其命名为 "BIM 大数据"。本文采用系统分析和文献计量分析的混合方法，综合了 2013 年至 2013 年发表的 125 篇文献的研究结果。虽然许多研究还处于概念性或小规模实验方法的初步阶段，但本文将研究结果分为四个领域：AEC 组织基础设施、大 BIM 数据（IT）基础设施、AEC 组织战略领域和大 BIM 数据（IT）战略领域，与战略联盟模型（SAM）保持一致，探索组织能力、治理因素和战略框架。本文介绍了 AEC 组织 - 大 BIM 数据 SAM，作为在整个 AEC 行业实施大 BIM 数据利用的研究议程。该框架全面探讨了组织动态，同时强调了单个项目、组织层级和行业标准之间的相互联系。

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