Automation in Construction最新文献_第7页

Adaptive reinforcement learning algorithm for real-time energy optimization in building digital twins with heterogeneous IoT sensor networks 基于异构物联网传感器网络的数字孪生建筑实时能量优化自适应强化学习算法

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

Automation in Construction

Pub Date : 2025-12-13 DOI: 10.1016/j.autcon.2025.106714

Manea Almatared , Maher Abuhussain , Zahra Andleeb , Faizah Mohammed Bashir

Building operations contribute significantly to global carbon emissions, yet existing digital twins rely on static strategies that fail to adapt to dynamic conditions. This paper investigates whether an adaptive reinforcement learning framework with hierarchical transfer learning can optimize real-time energy use and comfort across heterogeneous buildings without extensive retraining. The paper develops and validates a proximal policy optimization controller integrated with a digital twin and 348 IoT sensors across three commercial buildings in a 14-month randomized crossover trial. The approach achieves 23.7 % and 14.9 % energy reductions compared to rule-based control and model predictive control, respectively, while maintaining 94.7 % comfort satisfaction and demonstrating 78 % transfer learning efficiency. These findings provide facility managers and grid operators with a scalable, hardware-validated approach that reduces operational costs and stabilizes demand response without compromising occupant comfort. Future work extends this hierarchical transfer framework to mixed-mode ventilation and district-level energy coordination to further enhance grid interactivity.

建筑运营对全球碳排放的贡献很大，但现有的数字孪生依赖于静态策略，无法适应动态条件。本文研究了具有分层迁移学习的自适应强化学习框架是否可以在不进行大量再培训的情况下优化异构建筑的实时能源使用和舒适度。本文在为期14个月的随机交叉试验中，在三座商业建筑中开发并验证了集成了数字孪生和348个物联网传感器的近端策略优化控制器。与基于规则的控制和模型预测控制相比，该方法分别降低了23.7%和14.9%的能量，同时保持了94.7%的舒适性满意度和78%的迁移学习效率。这些发现为设施管理人员和电网运营商提供了一种可扩展的、经过硬件验证的方法，可以降低运营成本，稳定需求响应，同时不影响居住者的舒适度。未来的工作将这种分层转移框架扩展到混合模式通风和区域一级的能源协调，以进一步增强电网的交互性。

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引用次数: 0

Distributed fiber optic sensors for monitoring cracks in civil infrastructure 用于监测民用基础设施裂缝的分布式光纤传感器

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

Automation in Construction

Pub Date : 2025-12-13 DOI: 10.1016/j.autcon.2025.106717

Shengju Xie , Yiming Liu , Farhad Ansari , Yi Bao

Distributed Fiber Optic Sensors (DFOS) have exhibited unique strengths in monitoring cracks, providing real-time, spatially-distributed data that indicate structural integrity and support automation in the construction, operation, maintenance, management, and recycling of structures or structural components. This paper systematically reviews the scientific principles, influencing factors, technical methods, and applications of DFOS, aiming to enhance understanding and promote the applications of DFOS in automated monitoring of infrastructure. Fiber optic cables, installation methods, and sensing technologies are reviewed to promote construction practices. The reviewed contents extend to advancements in machine learning and digital twinning technologies for real-time, automated data processing, analysis, and visualization of the presence, location, and severity of cracks. Major challenges and emerging opportunities of utilizing DFOS are discussed to facilitate future research. This paper advances the understanding, development, and engineering applications of DFOS for monitoring cracks toward intelligent digitalization and automation of civil infrastructure.

分布式光纤传感器（DFOS）在监测裂缝方面表现出独特的优势，提供实时、空间分布的数据，表明结构完整性，并支持结构或结构部件的施工、操作、维护、管理和回收的自动化。本文系统综述了DFOS的科学原理、影响因素、技术方法及其应用，旨在加深对DFOS在基础设施自动化监测中的认识，促进其应用。光纤电缆，安装方法和传感技术进行审查，以促进施工实践。回顾的内容扩展到机器学习和数字孪生技术的进展，用于实时，自动化数据处理，分析和可视化裂缝的存在，位置和严重程度。讨论了利用DFOS的主要挑战和新机遇，以促进未来的研究。本文对DFOS裂缝监测技术在民用基础设施智能化、数字化和自动化方面的应用进行了展望。

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引用次数: 0

Low-cost pavement roughness evaluation using Inertial Measurement Unit (IMU)-Pulsed Coherent Radar (PCR) sensor fusion 基于惯性测量单元(IMU)-脉冲相干雷达（PCR）传感器融合的低成本路面粗糙度评估

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

Automation in Construction

Pub Date : 2025-12-12 DOI: 10.1016/j.autcon.2025.106713

Yazan Ibrahim Alatoom , Omar Smadi

Pavement roughness monitoring is critical for infrastructure management, but conventional automated profiling systems require substantial capital investments, making them inaccessible to limited-budget transportation agencies. This paper introduces a low-cost system combining Inertial Measurement Unit (IMU) and Pulsed Coherent Radar (PCR) technologies through sensor fusion. The approach captures vehicle dynamics via the IMU and pavement surface profiles via the PCR, using frequency-domain processing to isolate true pavement roughness from vehicle-induced motion. Comprehensive validation across twelve road segments and 216 test configurations demonstrates strong performance: MAPE below 9 % and R² exceeding 0.96 compared to reference measurements. A multi-stage optimization framework integrating Sequential Model-Based Optimization algorithm achieves 80–88 % accuracy improvements through systematic parameter calibration. The complete system costs $214 USD, providing a cost-effective solution for IRI estimation. A user-friendly graphical interface enables practical deployment by non-technical personnel. This approach enables broader adoption of automated pavement monitoring by agencies with limited budgets.

路面粗糙度监测对基础设施管理至关重要，但传统的自动分析系统需要大量的资本投资，这使得预算有限的运输机构无法使用。本文介绍了一种结合惯性测量单元（IMU）和脉冲相干雷达（PCR）技术的低成本传感器融合系统。该方法通过IMU捕获车辆动态，通过PCR捕获路面表面轮廓，并使用频域处理从车辆引起的运动中分离出真实的路面粗糙度。对12个路段和216种测试配置的综合验证显示出强大的性能：与参考测量值相比，MAPE低于9%，R2超过0.96。结合序列模型优化算法的多阶段优化框架，通过系统参数标定，精度提高80 ~ 88%。整套系统的成本为214美元，为IRI估计提供了一个经济有效的解决方案。用户友好的图形界面使非技术人员也能进行实际部署。这种方法使预算有限的机构能够更广泛地采用自动路面监测。

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引用次数: 0

Structural defect segmentation using a semi-supervised algorithm integrating YOLO and the segment anything model 结合YOLO和任意分割模型的半监督结构缺陷分割算法

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

Automation in Construction

Pub Date : 2025-12-11 DOI: 10.1016/j.autcon.2025.106709

Gauthami Vijayakumar Kuttuva, Prawin J

Deep learning–based defect segmentation enables automated detection, classification, localisation, and quantification of structural defects. Although supervised segmentation methods yield strong performance, they require extensive pixel-level annotations and struggle with data scarcity, class imbalance, thin crack segmentation, and generalisation across diverse conditions. This paper proposes a semi-supervised two-stage segmentation framework that integrates a high-accuracy object detection model (Stage I: localisation using YOLO11/Oriented YOLO11) with zero-shot unsupervised segmentation (Stage II: pixel-level mapping using Segment Anything Model with box prompts). The method requires only object-detection labels, eliminating the need for dedicated segmentation annotations. Experiments on benchmark datasets involving steel, masonry, concrete cracks, spalling, and corrosion demonstrate that the hybrid Oriented YOLO11 and SAM model achieves state-of-the-art performance, with an average Dice score of 0.7, comparable to those of fully supervised models. The proposed framework offers real-time performance, strong generalisation, and high scalability, making it a promising solution for robust structural defect segmentation.

基于深度学习的缺陷分割实现了结构缺陷的自动检测、分类、定位和量化。尽管监督分割方法产生了强大的性能，但它们需要大量的像素级注释，并与数据稀缺性、类不平衡、细裂纹分割和不同条件下的泛化作斗争。本文提出了一种半监督两阶段分割框架，该框架集成了高精度目标检测模型（第一阶段：使用YOLO11/Oriented YOLO11进行定位）和零镜头无监督分割（第二阶段：使用带有框提示的Segment Anything model进行像素级映射）。该方法只需要对象检测标签，不需要专门的分割注释。在涉及钢铁、砌体、混凝土裂缝、剥落和腐蚀的基准数据集上的实验表明，混合的面向YOLO11和SAM模型达到了最先进的性能，平均Dice得分为0.7，与完全监督模型相当。该框架具有实时性好、泛化能力强、可扩展性强等特点，是鲁棒结构缺陷分割的理想解决方案。

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引用次数: 0

Automated and scalable BIM2BEM framework with zoning-based model simplification leveraging knowledge graph integration 自动化和可扩展的BIM2BEM框架，基于分区的模型简化利用知识图集成

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

Automation in Construction

Pub Date : 2025-12-11 DOI: 10.1016/j.autcon.2025.106712

Meng Wang, Georgios N. Lilis, Dimitris Mavrokapnidis, Kyriakos Katsigarakis, Ivan Korolija, Dimitrios Rovas

Accurate and scalable generation of Building Energy Models (BEM) from Building Information Modelling (BIM) data is critical for performance-driven building design. However, existing methods are often constrained by data quality issues and rigid workflows, limiting automation. This paper proposes an automated and scalable BIM-to-BEM (BIM2BEM) framework enabled by knowledge graph integration, designed to support automation and scalability in model generation from imperfect BIM data. To manage model complexity, zoning-based mappings from BIM spaces to thermal zones are derived through multi-factor analysis of spatial relationships, functional usage, thermal load similarity, and HVAC configuration. Applied to a real-world complex building, the framework reduces simulation time by up to 70%, while maintaining energy use deviations within 3% and HVAC sizing variations up to 10%, compared with the full-model baseline. These findings indicate that the proposed framework can enhance BIM2BEM automation, supporting the scalable and flexible generation of simulation-ready models under practical data limitations.

从建筑信息模型（BIM）数据中准确和可扩展地生成建筑能源模型（BEM）对于性能驱动型建筑设计至关重要。然而，现有的方法经常受到数据质量问题和严格的工作流程的限制，限制了自动化。本文提出了一个自动化和可扩展的BIM-to- bem （BIM2BEM）框架，该框架通过知识图集成实现，旨在支持从不完善的BIM数据生成模型的自动化和可扩展性。为了管理模型的复杂性，通过对空间关系、功能使用、热负荷相似性和暖通空调配置的多因素分析，得出了从BIM空间到热区基于分区的映射。应用于现实世界的复杂建筑，与全模型基线相比，该框架将模拟时间缩短了70%，同时将能源使用偏差保持在3%以内，暖通空调尺寸变化保持在10%以内。这些发现表明，所提出的框架可以增强BIM2BEM自动化，支持在实际数据限制下可扩展和灵活地生成仿真就绪模型。

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引用次数: 0

Diffusion-enhanced semantic segmentation for underground crack detection 基于扩散增强语义分割的地下裂缝检测

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

Automation in Construction

Pub Date : 2025-12-11 DOI: 10.1016/j.autcon.2025.106721

Neng Wang , Bowen Jiang , Camillo J. Taylor , Zili Li

Unlike surface structures, crack detection in underground environments faces greater challenges due to low illumination and complex structural context. This paper develops a diffusion-based model for crack segmentation in underground tunnel structures. Building on a framework that reformulates semantic segmentation as conditional image generation, an enhanced diffusion architecture guided by prior masks is proposed. Trained on a diverse dataset comprising crack images from the Dublin Port Tunnel, the European Organization for Nuclear Research, and the Bochum Crack Dataset, the proposed model outperforms benchmark models (e.g., Mask2Former), achieving a 9.1% increase in mIoU and 16.1% in F1-score in multi-scenario validation. In field testing, the proposed two-stage pipeline optimizes inference strategy by excluding crack-free patches (87.5% of the total), and tests real-world applicability, with an absolute mIoU gain of 0.063. Additionally, a post-processing strategy leveraging uncertainty maps further refines the segmentation results.

与地面结构不同，由于光照不足和复杂的结构环境，地下环境的裂缝检测面临着更大的挑战。本文建立了一种基于扩散的地下隧道结构裂缝分割模型。在将语义分割作为条件图像生成的框架的基础上，提出了一种基于先验掩码的增强扩散架构。在都柏林港口隧道、欧洲核研究组织和波鸿裂缝数据集的裂缝图像的不同数据集上进行训练，所提出的模型优于基准模型（例如Mask2Former），在多场景验证中实现了9.1%的mIoU和16.1%的f1分数的提高。在现场测试中，提出的两阶段管道通过排除无裂纹补丁（占总数的87.5%）来优化推理策略，并测试了现实世界的适用性，绝对mIoU增益为0.063。此外，利用不确定性映射的后处理策略进一步细化了分割结果。

引用次数: 0

From conventional brain–computer interfaces to gen-AI–powered systems for advancing construction safety management 从传统的脑机接口到推进建筑安全管理的人工智能驱动系统

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

Automation in Construction

Pub Date : 2025-12-11 DOI: 10.1016/j.autcon.2025.106708

Ah Khoon Hwee , Tan Kah Hui , Liu QingHua , Wang Jiao

Although the use of brain–computer interfaces (BCI) in construction safety management is expanding, no comprehensive review has yet examined how generative artificial intelligence (Gen-AI) can enhance BCI systems in this domain. A total of 292 peer-reviewed journal articles are analysed in this review. The state-of-the-art applications such as simulation, real-time monitoring, and human-robot collaboration and their associated limitations are reviewed and scrutinised. Unlike previous BCI safety reviews, this paper highlights the integrative role of Gen-AI in enhancing BCI systems through noise reduction, data augmentation, and self-supervised learning frameworks. Furthermore, a visual roadmap outlining short-, medium-, and long-term is proposed based on the challenges identified in Gen-AI enhanced BCI systems. The findings are expected to assist researchers in understanding the current state of BCI development and to provide a perspective from which future directions can be formulated to advance construction safety management.

尽管脑机接口（BCI）在建筑安全管理中的应用正在扩大，但尚未有全面的综述研究生成式人工智能（Gen-AI）如何增强该领域的BCI系统。本综述共分析了292篇同行评议的期刊文章。最先进的应用，如仿真，实时监控，人机协作及其相关的限制进行了审查和审查。与以往的脑机接口安全性综述不同，本文强调了Gen-AI通过降噪、数据增强和自我监督学习框架在增强脑机接口系统方面的综合作用。此外，根据Gen-AI增强BCI系统中确定的挑战，提出了一份概述短期、中期和长期的可视化路线图。研究结果有望帮助研究人员了解脑机接口的发展现状，并为制定未来的发展方向提供一个视角，以推进建筑安全管理。

引用次数: 0

Real-time on-site structural safety assessment of metro tunnel linings via WSN and edge computing 基于无线传感器网络和边缘计算的地铁隧道衬砌结构安全实时现场评估

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

Automation in Construction

Pub Date : 2025-12-10 DOI: 10.1016/j.autcon.2025.106711

Dongming Zhang , Jianhui Yu , Mingliang Zhou , Hongwei Huang , Linghan Ouyang

Edge computing mitigates the latency of traditional “on-site acquisition–off-site processing” workflows, enabling real-time structural safety assessment. This paper develops an edge-computing-based safety assessment system for shield tunnel linings, integrating sensors, inspection technologies, wireless sensor networks, and edge gateways. Coordinated gateway–sensor communication enables on-site data fusion and standardized processing for variable-weight fuzzy assessment. A lookup-table-based membership function achieved a 79× acceleration, 74 % lower memory use, and 34 % lower energy consumption. A lightweight multithreaded architecture improved image pre-processing by 60 %, while an optimized Kalman filter reduced latency and energy by 20 % and 36 %, respectively. A simplified Seasonal-Trend decomposition using Loess (STL)-ARIMA model enhanced forecasting efficiency by 14 %. Validated on Shanghai Metro Line 2 in China, the system enabled zone-level assessments within 243 s. By integrating edge-compatible algorithms with domain-specific structural knowledge, the framework provides a scalable, energy-efficient, and adaptive solution for long-term intelligent maintenance of shield tunnels and similar infrastructure systems.

边缘计算减少了传统“现场采集-非现场处理”工作流程的延迟，实现了实时结构安全评估。本文开发了一种基于边缘计算的盾构隧道衬砌安全评估系统，该系统集成了传感器、检测技术、无线传感器网络和边缘网关。网关-传感器协同通信，实现现场数据融合和标准化处理，实现变权模糊评价。基于查询表的成员函数实现了79x的加速、74%的内存使用和34%的能耗。轻量级多线程架构将图像预处理提高了60%，而优化的卡尔曼滤波器将延迟和能量分别降低了20%和36%。采用黄土(STL)-ARIMA模型简化季节趋势分解，预报效率提高14%。该系统在中国上海地铁2号线进行了验证，在243秒内实现了区域级评估。通过将边缘兼容算法与特定领域的结构知识相结合，该框架为盾构隧道和类似基础设施系统的长期智能维护提供了可扩展、节能和自适应的解决方案。

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引用次数: 0

Generalizable deep sequence models for 4D trajectory prediction of tower crane loads 塔机载荷四维轨迹预测的广义深度序列模型

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

Automation in Construction

Pub Date : 2025-12-10 DOI: 10.1016/j.autcon.2025.106696

Mohammad Hossein Kazemi , Yuqing Hu , Yi Wu , John I. Messner , Scarlett R. Miller

Predicting the trajectory of suspended loads is essential for proactive collision avoidance and improving situational awareness during tower crane operations. Existing approaches suffer from limited generalizability, simplified motion assumptions, and lack of real-time deployment. This paper presents a data-driven framework for 4D (3D + time) trajectory prediction using deep sequence models trained on realistic crane-operation data. A Unity-based simulation environment is developed to emulate rotary and linear encoders, and 29 participants operate the crane across randomized pick-and-place tasks, producing diverse motion trajectories. Six architectures — including LSTM-based Seq2Seq models with different attention mechanisms, ConvLSTM networks, and Temporal Convolutional Networks — under varying prediction horizons, temporal context, sampling rates, and sensor noise are evaluated. The Seq2Seq model with Temporal Attention achieves the best performance, with a mean 3D displacement error of 0.45 m on unseen logistic scenarios. A high-performing model is integrated into a real-time digital twin to provide feedback for operator training.

在塔吊运行过程中，预测吊载轨迹对于主动避免碰撞和提高态势感知能力至关重要。现有的方法受限于有限的通用性，简化的运动假设，以及缺乏实时部署。本文提出了一种数据驱动的四维（3D +时间）轨迹预测框架，该框架采用基于实际起重机操作数据训练的深度序列模型。开发了一个基于unity的仿真环境来模拟旋转和线性编码器，29名参与者在随机拾取和放置任务中操作起重机，产生不同的运动轨迹。在不同的预测范围、时间背景、采样率和传感器噪声下，评估了六种架构，包括基于lstm的具有不同注意机制的Seq2Seq模型、ConvLSTM网络和Temporal Convolutional networks。具有时间注意力的Seq2Seq模型达到了最好的性能，在不可见的物流场景下平均3D位移误差为0.45 m。将高性能模型集成到实时数字孪生模型中，为操作员培训提供反馈。

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引用次数: 0

Design, multi-scale structural analysis, and construction of modular prefabricated 3D-printed concrete residence 模块化预制3d打印混凝土住宅的设计、多尺度结构分析和施工

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

Automation in Construction

Pub Date : 2025-12-09 DOI: 10.1016/j.autcon.2025.106705

Hailong Wang, Yiqing Shi, Xiaoyan Sun, Xiqiang Lin, Yinlin Ye

Residential building construction is increasingly challenged by labour shortages and limited funding. Modular prefabricated 3D printing concrete (3DPC) technology, integrated with digital design and automated construction, offers an efficient and standardised solution to the construction of residential buildings, and has seen emerging applications worldwide. This paper presents the process from architectural and structural design based on one digital model to 3D printing and assembly of a modular prefabricated 3DPC residence, incorporating a multi-scale numerical simulation method for structural analysis. The study identifies the primary limitation of modular 3DPC as the size constraints imposed by printing and transportation equipment. A case study in Hebei Province, China, demonstrates the practicality. Results indicate that under the most unfavourable conditions, the maximum stresses are within 1.3 % of the limit values, ensuring a high safety margin. The 3DPC residence demonstrates time and cost savings, with digital design optimizing coordination among structural, architectural, and MEP systems.

住房建设日益受到劳动力短缺和资金有限的挑战。模块化预制3D打印混凝土（3DPC）技术与数字设计和自动化施工相结合，为住宅建筑的建造提供了高效、标准化的解决方案，并在全球范围内得到了广泛的应用。本文介绍了模块化预制3DPC住宅从基于一个数字模型的建筑和结构设计到3D打印和组装的过程，并采用多尺度数值模拟方法进行结构分析。该研究确定了模块化3DPC的主要限制是打印和运输设备对尺寸的限制。以河北省为例，验证了该方法的实用性。结果表明，在最不利的条件下，最大应力在限值的1.3%以内，保证了较高的安全裕度。3DPC住宅展示了时间和成本的节省，数字化设计优化了结构、建筑和MEP系统之间的协调。

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引用次数: 0