Automation in Construction最新文献_第3页

A digital monitoring, delay detection and visualisation framework for construction projects: RealCONs 用于建筑项目的数字监控、延迟检测和可视化框架：RealCONs

IF 11.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2026-03-01 Epub Date: 2026-01-28 DOI: 10.1016/j.autcon.2026.106781

Kambiz Radman, Mostafa Babaeian Jelodar, Ruggiero Lovreglio

Accurate and resilient monitoring of construction projects remains challenging due to fragmented reporting, data uncertainty and delayed system integration. This paper evaluates RealCONs, a QR-enabled real-time monitoring framework that integrates BIM, mobile scanning, cloud-based SQL storage, and Power BI analytics to support live project control. A 90-day comparative case analysis of two concurrent Electrical and Instrumentation projects benchmarked RealCONs against a conventional tracking system. Performance was assessed using Earned Value and Earned Schedule metrics, supported by Chi-square and two-proportion tests, confidence intervals, normality testing, regression forecasting, and non-parametric Wilcoxon and Mann–Whitney analyses. Data continuity strongly favoured RealCONs, with five missing earned-value days compared with 35 in the comparator project (χ² = 28.93, p < .001). Across 51 paired days, RealCONs achieved superior CPI (1.02 vs 0.90) and SPI (1.01 vs 0.89). During a delay event (Days 33–37), RealCONs maintained measurable progress and statistically significant SPI predictability, while the comparator recorded zero earned value. Overall, RealCONs enabled earlier delay detection, improved forecast reliability and scalable, real-time decision support aligned with Industry 4.0 objectives.

由于报告的碎片化、数据的不确定性和系统集成的延迟，对建筑项目进行准确和有弹性的监测仍然具有挑战性。本文评估了RealCONs，这是一个支持qr的实时监控框架，它集成了BIM、移动扫描、基于云的SQL存储和Power BI分析，以支持实时项目控制。对两个同时进行的电气和仪器项目进行了为期90天的比较案例分析，将realcon与传统跟踪系统进行了对比。使用挣值和挣进度指标评估绩效，并采用卡方检验和双比例检验、置信区间、正态性检验、回归预测以及非参数Wilcoxon和Mann-Whitney分析。数据连续性非常有利于RealCONs，有5天缺少挣值日，而比较项目为35天（χ2 = 28.93, p < .001）。在51个配对的日子里，RealCONs取得了卓越的CPI （1.02 vs 0.90）和SPI （1.01 vs 0.89）。在延迟事件期间（第33-37天），RealCONs保持了可测量的进度和统计上显著的SPI可预测性，而比较器记录的挣值为零。总体而言，RealCONs实现了更早的延迟检测，提高了预测可靠性和可扩展的实时决策支持，符合工业4.0的目标。

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引用次数: 0

Real-time knowledge management for construction value engineering: Live capture and BERT-aided case-based retrieval 建筑价值工程的实时知识管理：实时捕获和bert辅助的基于案例的检索

IF 11.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2026-03-01 Epub Date: 2026-01-17 DOI: 10.1016/j.autcon.2026.106782

Fuhao Zu , Xueqing Zhang

Effective reuse of creative ideas from value engineering (VE) workshops is crucial for cost-effective, innovative design. Conventional methods like post-project reviews and keyword searches often lack context, real-time availability, and semantic relevance, limiting the practical reuse of past insights. This paper addresses the fundamental question of how knowledge generated during VE workshops can be effectively captured and reused to support future idea generations. To solve this, it proposes an integrated methodology combining BIM-based live capture with a hybrid retrieval system. This system uses structured attributes and Bidirectional Encoder Representations from Transformers (BERT) based semantic similarity to ensure context-aware reuse. A prototype Revit plug-in was developed for structured capture and semantic search. Evaluation demonstrated strong performance, superiority over baseline methods, and high user acceptance. This paper provides a practical framework and tool for structured documentation and intelligent knowledge reuse, thereby enhancing creativity support for construction VE practices.

有效地重用来自价值工程（VE）车间的创意对于具有成本效益的创新设计至关重要。传统的方法，如项目后审查和关键字搜索，通常缺乏上下文、实时可用性和语义相关性，限制了对过去见解的实际重用。本文解决了如何有效地捕获和重用在VE研讨会期间生成的知识以支持未来的想法生成的基本问题。为了解决这一问题，本文提出了一种基于bim的实时捕获与混合检索系统相结合的集成方法。该系统使用结构化属性和基于语义相似度的双向编码器表示（BERT）来确保上下文感知重用。开发了用于结构化捕获和语义搜索的原型Revit插件。评估显示了强大的性能，优于基线方法，并且用户接受度高。本文为结构化文档和智能知识重用提供了一个实用的框架和工具，从而增强了对构建VE实践的创造性支持。

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引用次数: 0

Stakeholder-centric whole-lifecycle framework for guiding the development and implementation of construction digital twins 以利益相关者为中心的全生命周期框架，用于指导建筑数字孪生的开发和实施

IF 11.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2026-03-01 Epub Date: 2026-01-17 DOI: 10.1016/j.autcon.2026.106773

Wahib Saif , Omar Doukari , Mohamad Kassem

Construction Digital Twins (CDTs) are increasingly recognised for their potential to improve construction project management. However, successful implementation requires more than just deploying technology; it demands a stakeholder-centric, whole-system lifecycle approach. Existing frameworks are largely technocentric, focusing on technical demonstrations in isolated use cases and offering limited guidance on stakeholders' roles, interactions, and system lifecycle considerations. To address these gaps, this paper introduces a socio-technical CDT framework spanning five lifecycle stages: Define, Design, Deploy, Refine, and Decommission. Grounded in an eight-month longitudinal industrial case study and informed by a CDT triad taxonomy (applications, data, technologies), the framework guides CDT development and maps stakeholder engagement throughout its lifecycle. Stakeholders are categorised into four actor groups: Strategic, Advisory, Technical, and Operational, whose interdependencies are conceptualised through an actor role model. The framework extends CDT applicability beyond controlled demonstrations to real project contexts, while emphasising the need for validation across diverse organisational settings.

建筑数字孪生（CDTs）因其改善建筑项目管理的潜力而日益受到认可。然而，成功的实施需要的不仅仅是部署技术；它需要一个以涉众为中心的全系统生命周期方法。现有框架在很大程度上是以技术为中心的，关注于孤立用例中的技术演示，并提供有关涉众角色、交互和系统生命周期考虑的有限指导。为了解决这些差距，本文介绍了一个跨越五个生命周期阶段的社会技术CDT框架：定义、设计、部署、改进和退役。该框架以为期8个月的纵向工业案例研究为基础，并以CDT三元分类（应用程序、数据、技术）为依据，指导CDT开发，并在其整个生命周期中绘制涉众参与的地图。涉众被分为四个行动者组：战略、咨询、技术和运营，其相互依赖关系通过行动者角色模型概念化。该框架将CDT的适用性从受控的演示扩展到实际的项目环境，同时强调了跨不同组织设置进行验证的必要性。

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引用次数: 0

Adaptive planning of multi-UAV refined inspection path for complex and irregular building clusters 复杂不规则建筑群多无人机精细巡检路径自适应规划

IF 11.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2026-03-01 Epub Date: 2026-01-19 DOI: 10.1016/j.autcon.2026.106787

Penglu Chen , Yi Tan , Wen Yi

Amid rapid global urbanization, cities have shifted into a predominantly building maintenance-oriented phase. Therefore, given that existing studies focus on inspecting simple standalone buildings with single UAV, this paper proposes an automatic path planning method for the refined inspection of complex, irregular building clusters. First, an adaptive layering mechanism is introduced to generate full coverage inspection points based on the structural characteristics of the 3D building cluster model. Initial obstacle free flight paths are then derived by integrating A* and greedy algorithms. Further path optimization is conducted by applying the 2-opt algorithm to eliminate intersections and reduce flight distance, while the DP (Douglas Peucke) algorithm is employed simplified the trajectory by reducing redundant waypoints. Experimental validation on six irregularly shaped buildings demonstrates a 9.6% reduction in flight path length and a 47.7% decrease in intermediate waypoints. The proposed framework enables refined inspection path planning for building clusters, improving the automation level and practical applicability of multi-UAVs based building operation and maintenance.

在全球快速城市化的背景下，城市已经进入了以建筑维护为主的阶段。因此，针对现有研究多集中于单个无人机对简单独立建筑的检测，本文提出了一种自动路径规划方法，用于复杂、不规则建筑集群的精细化检测。首先，引入自适应分层机制，根据三维建筑集群模型的结构特征生成全覆盖检测点；然后通过A*算法和贪心算法的结合得到初始无障碍飞行路径。进一步采用2-opt算法进行路径优化，消除交叉点，减小飞行距离，采用DP （Douglas Peucke）算法减少冗余航路点，简化轨迹。在六个不规则形状建筑物上的实验验证表明，飞行路径长度减少了9.6%，中间航路点减少了47.7%。提出的框架能够细化建筑集群的巡检路径规划，提高多无人机建筑运维的自动化水平和实用性。

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引用次数: 0

Inertia effects matching for optimal attitude control in synchronous TBM considering human behavior 考虑人行为的同步TBM最优姿态控制惯性效应匹配

IF 11.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2026-03-01 Epub Date: 2026-02-04 DOI: 10.1016/j.autcon.2026.106798

Yongsheng Li , Limao Zhang , Qixiang Yan , Jianjun Qin , Zhanpeng Luo

The paper addresses the general problem of unreliable excavation in synchronous tunnel boring machines (S-TBMs) caused by inertia effects and time-delay phenomenon. The specific research question is how to achieve robust inertia matching those accounts for both equipment dynamics and human operator response behavior. A Gaussian mixture model (GMM) and an encoder-decoder framework (EDF) are proposed to estimate the driver and S-TBM inertial response time. A dynamic expression of the S-TBM excavation system is formulated, taking into account both human response time and equipment inertia effects. The results demonstrate that the proposed method accurately fits driver response time, achieves high-precision estimation of system inertia, and significantly reduces attitude errors by over 86% compared to non-matched control. An important contribution of this study is the integration of human behavioral inertia into the field of engineering equipment control, providing theoretical support for human-machine collaboration and real-time sharing control.

本文研究了同步隧道掘进机由于惯性效应和时滞现象引起的掘进不可靠问题。具体的研究问题是如何实现鲁棒惯性匹配那些考虑设备动力学和人类操作员响应行为的因素。提出了高斯混合模型（GMM）和编码器-解码器框架（EDF）来估计驱动和S-TBM惯性响应时间。建立了考虑人的响应时间和设备惯性效应的S-TBM开挖系统的动态表达式。结果表明，该方法能准确拟合驾驶员响应时间，实现系统惯性的高精度估计，与非匹配控制相比，姿态误差显著降低86%以上。本研究的一个重要贡献是将人的行为惯性整合到工程设备控制领域，为人机协作和实时共享控制提供理论支持。

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引用次数: 0

Project-level automated pavement maintenance and rehabilitation decision-making with data imbalance mitigation and post-maintenance evaluation 基于数据不平衡缓解和养护后评估的项目级自动路面养护和修复决策

IF 11.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2026-03-01 Epub Date: 2026-01-22 DOI: 10.1016/j.autcon.2026.106796

Qingwei Zeng , Shunxin Yang , Chang Xu , Jitong Ding , Qiwei Chen , Guoyang Lu

Pavement management data often suffers from severe class imbalance, and existing project-level maintenance and rehabilitation (M&R) decision-making models generally lack post-maintenance evaluation mechanisms. To address these issues, this paper proposes a project-level automated pavement M&R decision-making framework that considers data imbalance and incorporates post-maintenance evaluation (PMDNN). First, a Conditional Tabular Generative Adversarial Network (CTGAN) is developed to augment imbalanced M&R datasets. Next, two deep neural networks (DNNs) are constructed, for pavement performance prediction and for M&R decision-making, respectively. Finally, these two DNNs are nested to enable post-maintenance evaluation, supporting iterative adjustment of suboptimal M&R plans. Results demonstrate that the CTGAN effectively addresses data imbalance and accurately simulates the distribution of the original data. Compared with other data augmentation models, the CTGAN generates data with 4.7%–18.1% higher quality. Additionally, relative to multiple baseline frameworks, the proposed PMDNN framework achieves a 1.91%–4.71% higher overall decision accuracy. These findings indicate that PMDNN can support pavement management systems in making decisions more closely aligned with expert judgment.

路面管理数据往往存在严重的类失衡，现有的项目级养护与修复（M&；R）决策模型普遍缺乏养护后评价机制。为了解决这些问题，本文提出了一个考虑数据不平衡并结合维护后评估（PMDNN）的项目级自动路面管理决策框架。首先，开发了一种条件表格生成对抗网络（CTGAN）来增强不平衡的M&；R数据集。接下来，构建了两个深度神经网络（dnn），分别用于路面性能预测和M&；R决策。最后，这两个dnn被嵌套以支持维护后评估，支持次优M&；R计划的迭代调整。结果表明，CTGAN有效地解决了数据不平衡问题，并能准确地模拟原始数据的分布。与其他数据增强模型相比，CTGAN生成的数据质量提高了4.7%-18.1%。此外，相对于多个基线框架，所提出的PMDNN框架的总体决策准确率提高了1.91%-4.71%。这些发现表明，pmnn可以支持路面管理系统做出更符合专家判断的决策。

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引用次数: 0

Automated robotic deployment of distributed fiber optic sensing for construction monitoring 分布式光纤传感用于施工监测的自动化机器人部署

IF 11.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2026-03-01 Epub Date: 2026-01-21 DOI: 10.1016/j.autcon.2026.106793

Tzu-Hsuan Lin , Sheng-Hong Wu , Yu-Chen Su , Alan Putranto

Distributed fiber optic sensing (DFOS) enables continuous strain and temperature monitoring across civil infrastructure, yet installation remains labor-intensive. This paper presents ROADRobot (Robotic System for Automated Deployment of DFOS), a robotic platform integrating closed-loop tension control, calibrated adhesive dispensing, infrared-guided trajectory tracking, and mechanical bead consolidation for automated DFOS deployment. Laboratory validation on wooden and steel substrates identified optimal parameters of 3–6 cm/s traverse velocity and 0.16–0.32 mm/s dispensing velocity, achieving trajectory deviation within 2 mm. Confined-space deployment in a 450 × 450 mm steel channel demonstrated operation under geometric constraints. Comparative trials showed a 46.8% reduction in deployment time versus single-technician manual installation (p < 0.001, Cohen's d = 34.98) with 41% lower variability. OTDR testing confirmed fiber integrity with 0.042 dB insertion loss over 5.5 m. These results establish technical viability, though significant development remains for field application, including curved paths and non-horizontal surfaces.

分布式光纤传感（DFOS）可以实现对民用基础设施的连续应变和温度监测，但安装仍然是劳动密集型的。本文介绍了ROADRobot（自动部署DFOS的机器人系统），这是一个集成了闭环张力控制、校准粘合剂点胶、红外制导轨迹跟踪和自动部署DFOS的机械头固化的机器人平台。在木制和钢制基材上的实验室验证，确定了3-6 cm/s横移速度和0.16-0.32 mm/s点胶速度的最佳参数，实现了2 mm以内的轨迹偏差。在一个450 × 450毫米的钢通道中进行的密闭空间部署演示了几何约束下的操作。对比试验显示，与单个技术人员手动安装相比，部署时间减少了46.8% (p < 0.001, Cohen’s d = 34.98)，可变性降低了41%。OTDR测试证实了光纤的完整性，在5.5 m范围内插入损耗为0.042 dB。这些结果确立了技术上的可行性，但在现场应用方面仍有重大发展，包括弯曲路径和非水平表面。

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引用次数: 0

Multi-objective scientific approach to problem solving-inspired optimization integrated with the finite element method for automated structural design 多目标科学的问题求解启发式优化与有限元法相结合的自动化结构设计方法

IF 11.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2026-03-01 Epub Date: 2026-01-27 DOI: 10.1016/j.autcon.2026.106770

Dinh-Nhat Truong , Jui-Sheng Chou

This paper presents a simulation-driven framework integrating the Multi-Objective Scientific Approach to Problem Solving-inspired Optimization (MOSAPSO) algorithm with the finite element method (FEM) for automated structural design in construction. The proposed MOSAPSO integrates chaotic initialization, Lévy flight dynamics, elite population control, and sparsity-biased Pareto archiving to enhance convergence and diversity, while embedding the scientific research process, including review and problem definition, hypothesis formulation, data collection, and analysis and interpretation, into a unified optimization strategy. A temporal control strategy balances exploration and exploitation during the optimization process. Benchmarking on 24 CEC-2020 test functions reveals that MOSAPSO outperforms 11 established multi-objective algorithms across hypervolume (HV), generational distance (GD), and spacing (SP) metrics. Integrated with FEM, MOSAPSO–FEM automatically generates Pareto-optimal designs for five large-scale structural systems, balancing weight, displacement, and stability constraints. The framework provides a robust foundation for intelligent, simulation-driven decision-making in construction design, offering significant opportunities for integration with BIM, digital twins, and automated design tools.

本文提出了一种将多目标问题求解启发优化（MOSAPSO）算法与有限元法相结合的仿真驱动框架，用于建筑自动化结构设计。MOSAPSO集成了混沌初始化、lsamvy飞行动力学、精英群体控制和稀疏偏Pareto存档，增强了收敛性和多样性，同时将科学研究过程（包括审查和问题定义、假设制定、数据收集、分析和解释）嵌入到一个统一的优化策略中。在优化过程中，时序控制策略平衡了探索和开发。对24个CEC-2020测试功能的基准测试表明，MOSAPSO在超体积（HV）、世代距离（GD）和间隔（SP）指标上优于11种已建立的多目标算法。MOSAPSO-FEM与FEM相结合，自动生成五个大型结构体系的pareto最优设计，平衡重量、位移和稳定性约束。该框架为建筑设计中的智能、仿真驱动决策提供了坚实的基础，为与BIM、数字孪生和自动化设计工具的集成提供了重要的机会。

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引用次数: 0

Human digital twin for optimizing labor productivity in construction 5.0 优化建筑业劳动生产率的人类数字双胞胎5.0

IF 11.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2026-03-01 Epub Date: 2026-01-29 DOI: 10.1016/j.autcon.2026.106800

Chukwuka Christian Ohueri

Globally, labor productivity declined by 8% from 2022 to 2024, primarily due to human-centric factors. In transition to Construction 5.0 (C5.0), Human Digital Twin (HDT) integrates humans and systems to enhance productivity. However, existing review studies have not identified human-centric productivity drivers or HDT components, nor examined their interactions in enhancing labor productivity. This paper develops a framework that operationalizes the interactions between human-centric productivity drivers and HDT components to optimize labor productivity. A systematic review was conducted by searching for keywords in Scopus, using predefined criteria to select 185 articles published over the last decade, and analyzing the articles using thematic synthesis. Consequently, human-centric productivity drivers and HDT components were identified, and their interactions operationalized via a structured framework to optimize labor productivity in C5.0. This paper advances automation in construction by establishing a pioneering approach that integrates human attributes and cyber-physical systems for optimal human-system interaction.

从全球来看，从2022年到2024年，劳动生产率下降了8%，主要是由于以人为中心的因素。在向建筑5.0 （C5.0）过渡的过程中，人类数字孪生（HDT）将人类和系统集成在一起，以提高生产力。然而，现有的综述研究并没有确定以人为中心的生产力驱动因素或HDT成分，也没有检查它们在提高劳动生产率方面的相互作用。本文开发了一个框架，使以人为中心的生产力驱动因素和HDT组件之间的相互作用得以运作，以优化劳动生产率。通过在Scopus中搜索关键词，使用预定义的标准选择过去十年发表的185篇文章，并使用主题综合方法对文章进行系统评价。因此，我们确定了以人为中心的生产力驱动因素和HDT组件，并通过结构化框架对它们的相互作用进行了操作，以优化C5.0中的劳动生产率。本文通过建立一种开创性的方法，将人的属性和网络物理系统集成在一起，以实现最佳的人-系统交互，从而推进建筑自动化。

引用次数: 0

Automated diagnosis of bridge expansion joint defects using voiceprint features and deep learning 基于声纹特征和深度学习的桥梁伸缩缝缺陷自动诊断

IF 11.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2026-03-01 Epub Date: 2026-01-27 DOI: 10.1016/j.autcon.2025.106739

Yixuan Chen , Hongzhe Zhao , Yichao Xu , Yufeng Zhang , Jian Zhang

Bridge Expansion Joints (BEJs) are crucial for bridge safety, yet their acoustic signals are complex and easily disturbed by traffic noise, limiting traditional identification accuracy. To address this, an intelligent monitoring system based on voiceprint features and deep learning is developed. Its key contributions include: (1) a cloud-edge collaborative voiceprint monitoring device that integrates audio sampling, embedded processing, cloud server and wireless transmission, enabling long-term data collection and remote diagnosis under noisy environments; (2) the use of first- and second-order differential Mel Frequency Cepstral Coefficients (MFCC) for feature extraction, improving discriminability; and (3) the Hybrid Attention Fusion Network (HAFNet), built on a pre-trained convolutional backbone with multi-scale attention, achieving high-precision recognition of typical BEJ faults, with testing accuracies of 97.99% and 99.00% for two vehicle types. Field experiments demonstrate the system's stability, reliability, and feasibility for real-time BEJ monitoring.

桥梁伸缩缝对桥梁安全至关重要，但其声信号复杂，容易受到交通噪声的干扰，限制了传统的识别精度。为了解决这个问题，开发了一种基于声纹特征和深度学习的智能监控系统。其主要贡献包括：(1)集成了音频采样、嵌入式处理、云服务器和无线传输的云边缘协作声纹监测设备，实现了嘈杂环境下的长期数据采集和远程诊断；(2)利用一阶和二阶差分模频倒谱系数（MFCC）进行特征提取，提高了识别能力；(3)混合注意力融合网络（HAFNet），基于预训练的多尺度关注卷积主干，实现了对典型BEJ故障的高精度识别，两种车型的测试准确率分别为97.99%和99.00%。现场实验验证了该系统的稳定性、可靠性和实时监测的可行性。

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引用次数: 0