Automation in Construction最新文献

英文中文

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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引用次数: 0

Global BIM-point cloud registration and association for construction progress monitoring 用于施工进度监控的全球 BIM 点云注册和关联

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

Automation in Construction

Pub Date : 2024-10-01 DOI: 10.1016/j.autcon.2024.105796

Yinqiang Zhang , Liang Lu , Xiaowei Luo , Jia Pan

Traditional manual and semi-automatic approaches rely heavily on surveying control points and manually picking equivalent point pairs, which is time-consuming and labor-intensive. This paper proposes an automatic algorithm for automatic global BIM-point registration and association to support construction progress monitoring. A representation using distance fields is proposed to efficiently integrate BIM in registration tasks. By leveraging a coarse-to-fine strategy, a primitive-level coarse algorithm is developed to achieve rough alignment between BIM and point cloud. This approach is then complemented by a point-level fine registration approach, which enables simultaneous pose refinement and BIM-point association. Extensive experiments are conducted on the data from simulation and real-world construction sites. The results demonstrate the promising registration and association performance of the proposed algorithm.

传统的手动和半自动方法主要依靠测量控制点和手动挑选等效点对，耗时耗力。本文提出了一种全局 BIM 点自动注册和关联的自动算法，以支持施工进度监控。本文提出了一种使用距离场的表示方法，可在注册任务中有效集成 BIM。利用从粗到细的策略，开发了一种原始级粗算法，以实现 BIM 与点云之间的粗略对齐。然后，点级精细配准方法对这种方法进行了补充，从而实现了姿态细化和 BIM 点关联的同步进行。对来自模拟和真实世界建筑工地的数据进行了广泛的实验。实验结果表明，所提出的算法具有良好的注册和关联性能。

引用次数: 0

BIM framework for efficient material procurement planning 高效材料采购规划的 BIM 框架

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

Automation in Construction

Pub Date : 2024-10-01 DOI: 10.1016/j.autcon.2024.105803

Mohammadreza Kalantari , Hosein Taghaddos , Mohammadhossein Heydari

Inefficient procurement processes can lead to increased costs and project delays. Addressing information management inefficiencies is a significant but largely unexplored area within construction procurement strategies, despite potential for automation through Database Management Systems (DBMS) and Industry Foundation Classes (IFC). Subjective approaches constrain procurement planning, hindering optimal solutions. This paper addresses the gap by developing a comprehensive semi-automated procurement planning framework. The framework offers flexibility through a two-phased optimization employing Particle Swarm Optimization (PSO) or Genetic Algorithm (GA), integrated with a Building Information Modeling (BIM)-driven database platform compatible with various modeling software. It enhances decision-making by considering indirect costs and allowing installment payments while generating a 4D schedule for improved supply chain stakeholder visualization and decision-making (e.g., project managers), demonstrating improvements over traditional procurement plans in a real-world case study. The developed framework enables future research on integrating real-time data, predictive analytics, and smart contracts to further enhance procurement management.

采购流程效率低下会导致成本增加和项目延误。尽管数据库管理系统（DBMS）和工业基础类（IFC）具有自动化的潜力，但在建筑采购战略中，解决信息管理效率低下的问题是一个重要领域，但在很大程度上尚未被开发。主观的方法限制了采购规划，阻碍了最佳解决方案的制定。本文通过开发一个全面的半自动化采购规划框架来填补这一空白。该框架通过采用粒子群优化（PSO）或遗传算法（GA）的两阶段优化，与兼容各种建模软件的建筑信息模型（BIM）驱动的数据库平台相结合，提供了灵活性。它通过考虑间接成本和允许分期付款来加强决策，同时生成 4D 时间表，以改进供应链利益相关者（如项目经理）的可视化和决策，在实际案例研究中展示了与传统采购计划相比的改进。所开发的框架有助于未来研究如何整合实时数据、预测分析和智能合约，以进一步加强采购管理。

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引用次数: 0

Automated pavement detection and artificial intelligence pavement image data processing technology 自动路面检测和人工智能路面图像数据处理技术

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

Automation in Construction

Pub Date : 2024-10-01 DOI: 10.1016/j.autcon.2024.105797

Jing Shang , Allen A. Zhang , Zishuo Dong , Hang Zhang , Anzheng He

Surging vehicle loads and changing climate environments place significant stress on road infrastructure. Pavement management requires fast and effective methods of detecting pavement distress and perform timely maintenance. This paper presents in detail the hardware devices for automated data collection and the 2D and 3D image acquisition methods. The detection methods for different pavement distresses are comprehensively analyzed and summarized in the review. In addition, the review covers the latest and classical artificial intelligence (AI) image processing algorithms, including traditional image processing, machine learning, and deep learning methods applied in pavement distress detection. The review summarizes the challenges, limitations, emerging technologies, and future trends of AI algorithms. The review findings indicate that the application of AI technology methods in pavement distress detection has grown dramatically, but challenges still exist in AI technology application in practical engineering.

激增的车辆负荷和不断变化的气候环境给道路基础设施带来了巨大压力。路面管理需要快速有效的方法来检测路面状况并及时进行维护。本文详细介绍了用于自动数据采集的硬件设备以及二维和三维图像采集方法。综述全面分析和总结了不同路面状况的检测方法。此外，综述还涵盖了最新和经典的人工智能（AI）图像处理算法，包括应用于路面病害检测的传统图像处理、机器学习和深度学习方法。综述总结了人工智能算法面临的挑战、局限性、新兴技术和未来趋势。综述结果表明，人工智能技术方法在路面窘迫检测中的应用有了显著增长，但人工智能技术在实际工程中的应用仍存在挑战。

引用次数: 0

Real-time prediction of TBM penetration rates using a transformer-based ensemble deep learning model 使用基于变压器的集合深度学习模型实时预测 TBM 贯入率

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

Automation in Construction

Pub Date : 2024-09-30 DOI: 10.1016/j.autcon.2024.105793

Minggong Zhang , Ankang Ji , Chang Zhou , Yuexiong Ding , Luqi Wang

Targeted to address the challenge of accurately predicting Tunnel Boring Machine (TBM) penetration rates in real-time, this paper explores how to develop a deep learning method that effectively and efficiently predicts penetration rates. A deep learning method termed a transformer-based ensemble bi-directional Long Short-Term Memory network (TransBiLSTMNet) is developed, comprising several modules, namely, the data processing, a backbone ensemble model, an improved transformer, loss function, and evaluation metrics. Validated on an actual TBM operation database, the developed method attains excellent performance with Mean Squared Error (MSE) of 0.1372, Mean Absolute Error (MAE) of 0.2099, Root MSE (RMSE) of 0.3704, Mean Absolute Percentage Error (MAPE) of 0.7091 %, and

R^{2}

of 0.9961. Furthermore, the ablation experiments and comparative results illustrate the superior predictive accuracy. Accordingly, the TransBiLSTMNet provides a robust solution for real-time TBM operation management. Future research could focus on refining the model and exploring its application to other predictive scenarios.

为了应对实时准确预测隧道掘进机（TBM）贯入率的挑战，本文探讨了如何开发一种深度学习方法，有效且高效地预测贯入率。本文开发了一种深度学习方法，称为基于变压器的双向集合长短期记忆网络（TransBiLSTMNet），由多个模块组成，即数据处理、骨干集合模型、改进的变压器、损失函数和评估指标。经实际 TBM 运行数据库验证，所开发的方法性能优异，平均平方误差 (MSE) 为 0.1372，平均绝对误差 (MAE) 为 0.2099，根 MSE (RMSE) 为 0.3704，平均绝对百分比误差 (MAPE) 为 0.7091 %，R2 为 0.9961。此外，消融实验和比较结果表明了其卓越的预测准确性。因此，TransBiLSTMNet 为实时 TBM 运行管理提供了强大的解决方案。未来的研究重点是完善该模型，并探索其在其他预测场景中的应用。

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引用次数: 0

Automating construction of road digital twin geometry using context and location aware segmentation 利用上下文和位置感知分割技术自动构建道路数字孪生几何图形

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

Automation in Construction

Pub Date : 2024-09-28 DOI: 10.1016/j.autcon.2024.105795

Diana Davletshina, Varun Kumar Reja, Ioannis Brilakis

Geometric Digital Twins (GDT) represent a critical advancement in road management, yet their practical implementation encounters a substantial obstacle due to development costs outweighing the expected benefits. This paper addresses this challenge and introduces an automated solution for creating 3D geometric foundation models for road digital twins. The proposed approach utilises point clouds to generate meshed, coloured, and semantically labelled models of road objects. The proposed solution incorporates context- and location-aware segmentation, followed by a 3D representation step via meshing. Experiments showed that the solution achieves a 91.7% mean intersection over union segmentation on road furniture in the Digital Roads dataset and surpasses the current leader on the KITTI360 dataset by +16.93%. As a result, the fully automatic method enables scalable and affordable geometry digital twinning for roads.

几何数字孪生（GDT）是道路管理领域的一项重要进步，但由于开发成本超过预期效益，其实际应用遇到了巨大障碍。本文针对这一挑战，介绍了一种为道路数字孪生创建三维几何基础模型的自动化解决方案。所提出的方法利用点云生成网格、彩色和语义标注的道路对象模型。建议的解决方案包括上下文和位置感知分割，然后通过网格化进行三维表示。实验表明，该解决方案在数字道路数据集的道路家具上实现了 91.7% 的平均交叉率，超过了目前 KITTI360 数据集上的领先者 16.93%。因此，全自动方法实现了可扩展且经济实惠的道路几何数字孪生。

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