Automation in Construction最新文献_第10页

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 : 2026-02-01 Epub 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

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 : 2026-02-01 Epub 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 : 2026-02-01 Epub 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

Digital twin synchronization in closed-loop HVAC control for building operations 建筑运行闭环暖通空调控制中的数字双同步

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

Automation in Construction

Pub Date : 2026-02-01 Epub Date: 2025-12-23 DOI: 10.1016/j.autcon.2025.106723

Jabeom Koo , Sungmin Yoon

Synchronization is essential for maintaining reliable and consistent digital twin environments in building operations. However, achieving accurate synchronization remains challenging due to limitations in virtual model representation and operational uncertainties. This paper proposes a digital twin synchronization (DTS) method based on a bidirectional synchronization framework that enhances digital twins' ability to simulate physical behaviors and mitigate uncertainties during operation. A field implementation was conducted in actual building operations, applying the DTS method to a chilled water pump differential pressure control loop. The results show that the bidirectional approach outperforms conventional unidirectional synchronization by reducing sensor errors and model uncertainties and improving overall simulator reliability. The proposed DTS method enables more consistent and reliable digital twin operation by improving synchronization accuracy and reducing inconsistencies. The DTS approach reduced the digital twin simulator's overall MAPE from 5.32 % to 0.88 %, demonstrating its effectiveness in accurately replicating physical and control behaviors.

同步对于在建筑操作中维护可靠和一致的数字孪生环境至关重要。然而，由于虚拟模型表示和操作不确定性的限制，实现精确同步仍然具有挑战性。提出了一种基于双向同步框架的数字孪生同步（DTS）方法，增强了数字孪生对物理行为的模拟能力，减轻了运行过程中的不确定性。在实际建筑作业中进行了现场实施，将DTS方法应用于冷冻水泵压差控制回路。结果表明，双向同步方法通过减少传感器误差和模型不确定性，提高模拟器整体可靠性，优于传统的单向同步方法。提出的DTS方法通过提高同步精度和减少不一致性，使数字孪生操作更加一致和可靠。DTS方法将数字孪生模拟器的总体MAPE从5.32%降低到0.88%，证明了其在准确复制物理和控制行为方面的有效性。

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引用次数: 0

Digital twin–driven temperature field optimization in tunnel freezing restoration using particle swarm optimization 基于粒子群算法的隧道冻结修复温度场数字双驱动优化

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

Automation in Construction

Pub Date : 2026-02-01 Epub Date: 2026-01-02 DOI: 10.1016/j.autcon.2025.106747

Jie Zhou , Chao Ban , Chengjun Liu , Zeyao Li , Huade Zhou , Hsinming Shang

The distribution and evolution of temperature field are key concerns in freezing restoration projects, while traditional methods face limitations due to sparse sensor placement and simplified simulation inputs. More effective and accurate methods are needed to determine the temperature field. A PSO-based digital twin model was developed and validated with a tunnel freezing restoration project in Bangkok, Thailand. By integrating real-time field temperature data, the model enables dynamic optimization of parameters, enhancing the accuracy. Single-parameter optimization achieves fast convergence, ideal for early-stage calibration, while multi-parameter optimization improves performance under complex conditions. In these cases, PSO demonstrates better performance compared with GA and DE. When using multiple measurement points, the model may encounter local optima. The hybrid optimization strategy (GA-PSO) provides an effective pathway to mitigate the issue of local optima. This paper demonstrates the model feasibility and effectiveness, offering a practical approach for dynamic temperature management in complex freezing environments.

温度场的分布和演变是冻结恢复工程的关键问题，而传统的方法由于传感器布置的稀疏和模拟输入的简化而面临局限性。需要更有效和准确的方法来确定温度场。建立了基于pso的数字孪生模型，并通过泰国曼谷的隧道冻结修复项目进行了验证。该模型通过集成实时现场温度数据，实现了参数的动态优化，提高了精度。单参数优化实现了快速收敛，非常适合早期校准，而多参数优化提高了复杂条件下的性能。在这些情况下，粒子群算法比遗传算法和遗传算法表现出更好的性能。当使用多个测量点时，模型可能会遇到局部最优。混合优化策略（GA-PSO）为解决局部最优问题提供了有效途径。本文论证了该模型的可行性和有效性，为复杂冰冻环境下的动态温度管理提供了一种实用的方法。

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引用次数: 0

UAV-based quantitative crack measurement for bridges integrating four-point laser metric calibration and mamba segmentation 基于无人机的桥梁裂缝定量测量，集成四点激光度量校准和曼巴分割

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

Automation in Construction

Pub Date : 2026-02-01 Epub Date: 2026-01-12 DOI: 10.1016/j.autcon.2026.106774

Jinghuan Zhang , Wang Chen , Jian Zhang

Crack width is an indicator of durability loss and serviceability in concrete bridges. Although UAV-based inspection is adopted, variable standoff distance and oblique imaging hinder valid, millimeter-level quantification. This paper presents a framework for crack identification and measurement. (1) A UAV-mounted four-point laser ranging device establishes a scale for each frame. Combined with homography and a Jacobian-based local length metric, the pixel-to-physical factor becomes a function of position and direction, which reduces scale drift across viewpoints. (2) CrackMamba-Net is designed to couple state space modeling with boundary sensitive representations, enhancing crack edge continuity and boundary clarity under fine and low contrast conditions. (3) Topology-preserving skeleton refinement with PCA-guided, distance-weighted linear correction estimates the local orientation; width is then measured along the refined normal and converted to physical units. Field and on-bridge experiments show linear agreement with references and low bias, supporting traceable, engineering-consistent crack quantification at the millimeter scale.

裂缝宽度是混凝土桥梁耐久性损失和使用性能的指标。虽然采用了基于无人机的检测，但可变距离和倾斜成像阻碍了有效的毫米级量化。本文提出了一种裂纹识别和测量的框架。(1)无人机上的四点激光测距装置为每一帧建立标尺。结合单应性和基于雅可比矩阵的局部长度度量，像素-物理因子成为位置和方向的函数，从而减少了视点之间的尺度漂移。(2) CrackMamba-Net旨在将状态空间建模与边界敏感表示相结合，在精细和低对比度条件下增强裂纹边缘的连续性和边界清晰度。(3)基于pca制导、距离加权线性修正的保持拓扑骨架优化估计局部方向；然后沿着精炼法线测量宽度，并转换为物理单位。现场和桥上实验表明，该方法与参考文献线性一致，且偏差低，支持可追溯的、工程上一致的毫米尺度裂纹量化。

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引用次数: 0

No-reference image quality assessment via degraded-content inference for sewer inspection images 基于退化内容推理的无参考图像质量评价

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

Automation in Construction

Pub Date : 2026-02-01 Epub Date: 2025-12-17 DOI: 10.1016/j.autcon.2025.106727

Xingyu Chen, Zegen Wang, Jianghai He, Yaowen Ran, Mi Chen, Jiayi Hu, Chaoyue Li

Sewer inspection images captured via CCTV often experience significant quality degradation due to complex pipeline environments and the motion of inspection robots. To address the challenges in quality assessment and support subsequent enhancement or detection tasks, this paper proposes DI-IQA, a no-reference image quality assessment model tailored for sewer inspection scenarios. DI-IQA introduces a degraded content inference (DCI) module based on GANs, guided by dark channel prior and luminance consistency losses, and an image quality regression (IQR) module that integrates features from the generator, discriminator, degraded images, and discrepancy images. Besides, two datasets were constructed for training: the Degraded Sewer Inspection Image Dataset (5350 image pairs) for DCI module, and the Sewer Inspection IQA Dataset (1000 images) for IQR module. Experiments show DI-IQA achieves PLCC 0.934 and SROCC 0.931 on the Sewer Inspection IQA Dataset, demonstrating outstanding performance, and up to PLCC 0.976 and SROCC 0.973 on natural image benchmarks.

由于复杂的管道环境和检测机器人的运动，通过闭路电视捕获的下水道检查图像通常会出现严重的质量下降。为了解决质量评估中的挑战并支持后续的增强或检测任务，本文提出了针对下水道检测场景量身定制的无参考图像质量评估模型DI-IQA。DI-IQA引入了一个基于gan的退化内容推理（DCI）模块，该模块以暗通道先验和亮度一致性损失为指导，以及一个图像质量回归（IQR）模块，该模块集成了生成器、鉴别器、退化图像和差异图像的特征。此外，构建了两个数据集用于训练：DCI模块的退化下水道检查图像数据集（5350对图像）和IQR模块的下水道检查IQA数据集（1000张图像）。实验表明，DI-IQA在下水道检查IQA数据集上达到PLCC 0.934和SROCC 0.931，表现出优异的性能，在自然图像基准上达到PLCC 0.976和SROCC 0.973。

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引用次数: 0

Scalable road infrastructure monitoring using embedded fiber Bragg grating sensors based on wavelet scattering-long short-term memory autoencoder 基于小波散射-长短期记忆自编码器的嵌入式光纤光栅传感器可扩展道路基础设施监测

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

Automation in Construction

Pub Date : 2026-02-01 Epub Date: 2025-12-18 DOI: 10.1016/j.autcon.2025.106724

Ali Golmohammadi , Vahid Yaghoubi , Navid Hasheminejad , Nasser Ghaderi , Wim Van den bergh , David Hernando

Managing and extracting insights from the large volumes of data generated by optical fiber sensor networks is a major challenge. This paper presents an intelligent, scalable framework for real-time road health monitoring using fiber Bragg grating (FBG) sensor data. The proposed framework reduces reliance on manual data handling and cuts storage needs by over 99 % by constructing a compact health indicator (HI). Data preprocessing and fusion reduce volume and variability, while a wavelet scattering network (WSN) extracts damage-sensitive features that are encoded via a long short-term memory (LSTM) autoencoder to represent the health state. Temperature data is integrated to distinguish structural damage from environmental effects. The approach is evaluated through laboratory fatigue tests and synthetic damage data generated from healthy-state field measurements. Results demonstrate accurate, efficient monitoring with potential for edge deployment, enabling low-cost, real-time, long-term structural health management and representing a significant step toward automated, resource-efficient infrastructure maintenance.

从光纤传感器网络产生的大量数据中管理和提取见解是一项重大挑战。本文提出了一个智能的、可扩展的框架，用于使用光纤布拉格光栅（FBG）传感器数据进行实时道路健康监测。该框架通过构建紧凑的运行状况指标（HI），减少了对人工数据处理的依赖，并将存储需求减少了99%以上。数据预处理和融合减少了体积和可变性，而小波散射网络（WSN）提取损伤敏感特征，通过长短期记忆（LSTM）自编码器编码来表示健康状态。温度数据被整合以区分结构损伤和环境影响。通过实验室疲劳测试和健康状态现场测量产生的综合损伤数据对该方法进行了评估。结果表明，准确、高效的监测具有边缘部署的潜力，可以实现低成本、实时、长期的结构健康管理，是向自动化、资源节能型基础设施维护迈出的重要一步。

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引用次数: 0

AI-powered real-time system for automated concrete slump prediction via video analysis 人工智能驱动的实时系统，通过视频分析自动预测混凝土坍落度

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

Automation in Construction

Pub Date : 2026-02-01 Epub Date: 2026-01-13 DOI: 10.1016/j.autcon.2026.106777

Youngmin Kim , Giyeong Oh , Kwangsoo Youm , Youngjae Yu

Concrete workability is essential to construction quality, and the slump test remains the most widely used on-site method for its assessment. However, traditional slump testing is manual, time-consuming, and highly operator-dependent, limiting its suitability for continuous or real-time monitoring during placement. SlumpGuard is an AI-powered vision system that analyzes the natural discharge flow from a mixer-truck chute using a single fixed camera. The system performs automatic chute detection, pouring-event identification, and video-based slump classification, enabling quality monitoring without sensors, hardware installation, or manual intervention. The system design is presented, along with a site-replicated dataset comprising over 6000 video clips, and extensive evaluations demonstrating reliable chute localization, accurate pouring detection, and robust slump prediction under diverse field conditions. An expert study further reveals substantial disagreement in human visual estimates, underscoring the need for automated assessment. Demonstration videos are available at this URL.

混凝土和易性对施工质量至关重要，坍落度试验是目前现场应用最广泛的混凝土和易性评价方法。然而，传统的坍落度测试是手动的，耗时且高度依赖于操作人员，限制了其在放置过程中连续或实时监测的适用性。SlumpGuard是一种人工智能视觉系统，可以使用单个固定摄像头分析混合卡车溜槽的自然排出流。该系统可自动进行溜槽检测、倾倒事件识别和基于视频的滑塌度分类，无需传感器、硬件安装或人工干预即可实现质量监控。介绍了系统设计，以及包含6000多个视频片段的现场复制数据集，以及广泛的评估，证明了在不同现场条件下可靠的滑槽定位，准确的浇注检测和可靠的坍落度预测。一项专家研究进一步揭示了人类视觉评估的实质性分歧，强调了自动化评估的必要性。演示视频可在此URL获得。

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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 : 2026-02-01 Epub 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