Automation in Construction最新文献_第2页

Distributed acoustic sensing for monitoring engineering infrastructure: Mechanisms, signal analytics, and applications 分布式声学传感监测工程基础设施：机制，信号分析和应用

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

Automation in Construction

Pub Date : 2026-01-17 DOI: 10.1016/j.autcon.2026.106784

Yuanyuan Li , Runze Zhao , Yin Liu , Hongnan Li , Qingrui Yue , Hongbing Chen

Vibration monitoring of engineering infrastructures is indispensable for structural safety and scientific maintenance. Distributed acoustic sensing (DAS) has been increasingly adopted in engineering field, owing to its attractive characteristics over conventional point-based transducers, including high spatial resolution, spatial continuity, non-invasiveness and superior stability. These advantages align well with instrumentation requirements for long-term and widely-distributed vibration monitoring in large-scale infrastructures. Accordingly, this paper provides a systematic review of DAS technique with respect to sensing mechanisms, deployment strategies, signal analysis, and typical applications. This review is structured around a complete operational workflow that explicates what the technology is, how it works, and what it enables in practice. Furthermore, current challenges and promising directions are discussed to envisage the widespread implementation of DAS systems, with the ultimate goal of automated monitoring for infrastructures. This review also aims to provide an exhaustive reference for researchers, professionals or engineering inspectors seeking state-of-the-art in DAS research.

工程基础设施的振动监测是保证结构安全、科学维护的重要手段。分布式声传感技术（DAS）以其高空间分辨率、空间连续性、非侵入性和优越的稳定性等优点，在工程领域得到了越来越多的应用。这些优点很好地满足了大型基础设施中长期和广泛分布的振动监测的仪器要求。因此，本文从传感机制、部署策略、信号分析和典型应用等方面对DAS技术进行了系统的综述。这个审查是围绕一个完整的操作工作流构建的，它解释了技术是什么，它是如何工作的，以及它在实践中支持什么。此外，讨论了当前的挑战和有希望的方向，以设想DAS系统的广泛实施，最终目标是基础设施的自动监测。本综述还旨在为研究人员、专业人员或工程检查员提供详尽的参考，以寻求最新的DAS研究。

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引用次数: 0

Artifact-driven LLM integration for mouseless design workflows 无鼠标设计工作流的工件驱动LLM集成

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

Automation in Construction

Pub Date : 2026-01-16 DOI: 10.1016/j.autcon.2026.106766

Ghang Lee , Sejin Park , Soo-in Yang

This paper investigates “mouseless design” feasibility, replacing traditional mouse-based interfaces with natural language interaction, in professional design practice. A three-month experiment tested LLMs for developing a sports complex project for competition. Through triangulation analysis of 2162 conversation turns, 1281 messages, and an 84-page design journal, this study established a quantitative baseline for LLM performance across professional design workflows. It revealed 86.9% unsuccessful individual interactions despite successful project completion and identified inconsistent spatial reasoning and geometry handling as the main weaknesses. Two methodological breakthroughs using conversational programming overcame these limitations: the “artifact-driven” approach repositioning LLMs as custom digital tool creators rather than direct design generators, and self-learning approaches extending complex BIM functionality. A statistical analysis (χ²(90) = 156, Cramer's V = 0.120) shows that terminology alignment serves as a success multiplier when combined with other strategies. These contributions provide empirical evidence for natural language-driven design while identifying critical requirements for successful AI integration.

本文在专业设计实践中探讨“无鼠标设计”的可行性，用自然语言交互取代传统的基于鼠标的界面。一项为期三个月的实验测试了llm为比赛开发体育综合体项目的能力。通过对2162个会话回合、1281条消息和84页的设计期刊进行三角分析，本研究为LLM在专业设计工作流程中的表现建立了定量基线。它揭示了86.9%不成功的个人互动，尽管成功完成了项目，并确定了不一致的空间推理和几何处理是主要弱点。对话式编程的两个方法论突破克服了这些限制：“工件驱动”方法将llm重新定位为定制的数字工具创建者，而不是直接的设计生成器，以及扩展复杂BIM功能的自我学习方法。统计分析（χ2(90) = 156, Cramer's V = 0.120）表明，术语对齐与其他策略结合使用时，可以起到成功乘数的作用。这些贡献为自然语言驱动的设计提供了经验证据，同时确定了成功的人工智能集成的关键需求。

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引用次数: 0

Virtual reality-based experimental analysis of personality and cognitive traits on task performance and safety in novice tower crane operators 基于虚拟现实的塔机新手任务绩效与安全的人格与认知特征实验分析

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

Automation in Construction

Pub Date : 2026-01-14 DOI: 10.1016/j.autcon.2026.106776

Seungkeun Yeom , Juui Kim , Seungwon Seo , Seongkyun Ahn , Choongwan Koo , Taehoon Hong

This paper investigates how personality traits and psychological-cognitive states influence task performance, safety, and physiological responses of novice tower crane operators through a virtual reality (VR) simulation integrated with continuous biometric monitoring. Fifty participants completed object lifting, obstacle navigation, and precision placement tasks while personality profiles and biosignals (ECG, EDA) were collected and analyzed using principal component analysis,cluster-based classification, and additional statistical methods. High extraversion and situational awareness enhanced speed and accuracy, whereas high openness, stress sensitivity, and acrophobia led to longer durations and reduced accuracy. High conscientiousness shortened task times by 19.12% but increased collisions by approximately threefold, revealing a trade-off between efficiency and safety. By integrating behavioral, cognitive, and physiological data, this work advances technology-enabled, data-driven safety management. The proposed approach enables automated operator risk profiling, intelligent task allocation, and proactive safety interventions for high-rise construction projects involving crane operations.

本文通过结合连续生物特征监测的虚拟现实（VR）模拟，研究了人格特征和心理认知状态如何影响塔式起重机新手的任务绩效、安全性和生理反应。50名参与者完成了物体举起、障碍物导航和精确放置任务，同时收集了性格特征和生物信号（ECG、EDA），并使用主成分分析、基于聚类的分类和其他统计方法进行了分析。高外向性和情境意识提高了速度和准确性，而高开放性、压力敏感性和恐高症导致持续时间更长和准确性降低。高度的责任心将任务时间缩短了19.12%，但将碰撞增加了大约三倍，揭示了效率和安全之间的权衡。通过整合行为、认知和生理数据，这项工作推进了技术驱动、数据驱动的安全管理。所提出的方法为涉及起重机操作的高层建筑项目实现了自动操作员风险分析、智能任务分配和主动安全干预。

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引用次数: 0

Efficient UAV trajectory optimization for fine-detailed 3D building reconstruction 面向精细三维建筑重建的高效无人机轨迹优化

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

Automation in Construction

Pub Date : 2026-01-14 DOI: 10.1016/j.autcon.2026.106775

Tianrui Shen, Lai Kang, Yingmei Wei, Shanshan Wan, Haixuan Wang, Chao Zuo

Using images captured by UAVs for high-fidelity 3D building reconstruction in architectural engineering is popular and effective nowadays; however, planning a flight trajectory that maximizes reconstruction quality with minimal flight time remains a critical challenge. This paper proposes a universal co-optimization framework that bridges reconstruction objectives with flight dynamics through an integrated planning paradigm. The proposed approach performs initial flight planning by solving a Traveling Salesman Problem over candidate viewpoints and updating them according to the unit-length contribution criterion. The adaptive radius is determined, and subsequently, the sphere-based corridor is constructed to enforce the trajectory passing all updated viewpoints within the corresponding spatial tolerances. Next, an optimal control problem is formulated and solved using a nonlinear solver to obtain the final flight trajectory satisfying both dynamic and safety constraints. Experimental comparisons with state-of-the-art methods on three public scenes and two real scenes captured by ourselves demonstrate that the proposed approach significantly improves flight efficiency, reducing travel distance and flight duration by approximately 10% to 40% with comparable or superior reconstruction quality.

利用无人机捕获的图像进行高保真的三维建筑重建是目前建筑工程中较为流行和有效的方法。然而，规划一个飞行轨迹，以最小的飞行时间最大限度地提高重建质量仍然是一个关键的挑战。本文提出了一个通用的协同优化框架，通过综合规划范式将重建目标与飞行动力学联系起来。该方法通过求解候选视点上的旅行推销员问题，并根据单位长度贡献准则对候选视点进行更新，从而实现初始飞行计划。确定自适应半径，然后构建基于球体的廊道，使轨迹在相应的空间容差范围内通过所有更新的视点。其次，建立了最优控制问题，并利用非线性求解器求解，得到了同时满足动力和安全约束的最终飞行轨迹。在三个公共场景和我们自己捕获的两个真实场景上与最先进的方法进行的实验比较表明，所提出的方法显着提高了飞行效率，将飞行距离和飞行时间减少了约10%至40%，并且具有相当或更好的重建质量。

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引用次数: 0

Condition-aware AI framework for automated structural health monitoring 用于自动结构健康监测的状态感知AI框架

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

Automation in Construction

Pub Date : 2026-01-14 DOI: 10.1016/j.autcon.2025.106748

Hamed Hasani, Francesco Freddi

This study presents an AI-powered framework for automated structural health monitoring that integrates modal identification, anomaly detection, and damage localization under varying environmental and operational conditions. The approach combines stochastic subspace identification with frequency–spatial domain decomposition for automated modal extraction and a condition-aware anomaly detector based on a conditional variational autoencoder. A secondary SSA–OC-SVM module verifies and localizes damage. The methodology is validated on a laboratory-scale structure through 500 one-hour tests under temperature variations up to 35 °C and diverse loading conditions. The identified modes exhibit MAC = 0.99–1.00, confirming reliable automated identification. The CVAE reconstructs healthy-state modal frequencies with MAPE = 0.23%, RMSE = 0.027 Hz, and

R^{2}

= 0.836, effectively distinguishing environmental effects (

\leq 0.27

pp) from genuine structural changes. The integrated framework further accurately localizes all induced damage scenarios across nine structural zones, demonstrating high accuracy, robustness, and scalability for next-generation SHM automation.

本研究提出了一个人工智能驱动的自动化结构健康监测框架，该框架集成了模态识别、异常检测和不同环境和操作条件下的损伤定位。该方法结合了随机子空间识别与频率-空间域分解的自动模态提取，以及基于条件变分自编码器的状态感知异常检测器。辅助SSA-OC-SVM模块对损坏进行验证和定位。该方法在实验室规模的结构上进行了500次一小时的测试，在温度变化高达35°C和各种负载条件下进行了验证。识别模式的MAC值为0.99-1.00，表明自动识别是可靠的。CVAE重构健康态模态频率，MAPE = 0.23%, RMSE = 0.027 Hz, R2 = 0.836，有效区分了环境影响（≤0.27 pp）和真实结构变化。集成框架进一步准确地定位了9个结构区域的所有诱发损伤场景，为下一代SHM自动化展示了高精度、鲁棒性和可扩展性。

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引用次数: 0

Domain-adaptive instance segmentation for far-field object monitoring using SAM-based weak supervision and noisy student self-training 基于sam的弱监督和噪声学生自训练的远场目标监测领域自适应实例分割

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

Automation in Construction

Pub Date : 2026-01-13 DOI: 10.1016/j.autcon.2026.106772

Minkyu Koo , Taegeon Kim , Minhyun Lee , Kinam Kim , Hongjo Kim

Automating construction site monitoring through deep learning–based segmentation presents challenges due to the high cost of pixel-wise annotations. This paper introduces a weakly and self-supervised learning framework that enhances segmentation accuracy while reducing annotation burden. Human-annotated bounding-box ground truth is used as prompts for the Segment Anything Model (SAM) to generate high-quality polygon mask labels, which are further refined through self-training. Compared to fully supervised learning models, the framework integrates Transfer Learning, Pseudo-Label Refinement, and the Noisy Student technique, improving mask mean Average Precision (Mask mAP) by 3–63% across seven target domains and achieving a Mask mAP of 72.27%. The approach also outperforms existing weakly supervised techniques, including BoxSnake and BoxTeacher, by 18% and 25.95%, respectively, and exceeds the performance of point-based methods such as PointWSSIS by 48.78%.

由于像素级标注的高成本，通过基于深度学习的分割自动化施工现场监控提出了挑战。本文引入了一种弱自监督学习框架，在降低标注负担的同时提高了分割精度。将人类标注的边界框地面真值作为SAM （Segment Anything Model）的提示符，生成高质量的多边形掩码标签，并通过自我训练进一步细化。与完全监督学习模型相比，该框架集成了迁移学习、伪标签细化和噪声学生技术，在7个目标域将mask mean Average Precision （mask mAP）提高了3-63%，实现了72.27%的mask mAP。该方法也比现有的弱监督技术（包括BoxSnake和BoxTeacher）分别高出18%和25.95%，并且比基于点的方法（如PointWSSIS）的性能高出48.78%。

引用次数: 0

Controllable reference-based semantic crack-image generation using diffusion model for intelligent infrastructure inspection 基于扩散模型的可控制参考语义裂缝图像生成，用于基础设施智能检测

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

Automation in Construction

Pub Date : 2026-01-13 DOI: 10.1016/j.autcon.2025.106759

Wenshang Yan , Hongnan Li

Improving the accuracy and robustness of deep-learning-based crack-segmentation models remains a significant challenge, primarily because of the insufficient quantity and diversity of the available pixel-level annotated data. To address this issue, this paper proposes a controllable Crack Reference-based Diffusion Model (CRDM). The proposed model can accurately synthesize realistic cracks on crack-free background images by leveraging predefined masks and reference images. Notably, it effectively transfers crack features from reference images to generated images, while maintaining high semantic accuracy. Extensive experiments are performed to demonstrate the advantages of CRDM in producing high-quality, diverse, crack images with precise controllability. The dataset augmented with the CRDM-generated images improves the performance of crack-segmentation models by ∼1 % IoU, across various scenarios. Further performance gains are achieved through our refined label-filtering strategy. The proposed CRDM exhibits strong potential for crack-segmentation tasks, effectively reducing the time and cost of data annotation and acquisition.

提高基于深度学习的裂缝分割模型的准确性和鲁棒性仍然是一个重大挑战，主要是因为可用的像素级注释数据的数量和多样性不足。针对这一问题，本文提出了一种基于裂纹参考的可控扩散模型（CRDM）。该模型可以利用预定义的蒙版和参考图像，在无裂纹背景图像上准确合成真实的裂纹。值得注意的是，它有效地将裂缝特征从参考图像转移到生成图像，同时保持了较高的语义准确性。大量的实验证明了CRDM在产生高质量、多样化、具有精确可控性的裂纹图像方面的优势。使用crdm生成的图像增强的数据集在各种场景下将裂缝分割模型的性能提高了约1% IoU。通过我们改进的标签过滤策略，进一步提高了性能。所提出的CRDM在裂缝分割任务中表现出强大的潜力，有效地减少了数据标注和获取的时间和成本。

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引用次数: 0

Human–AI communication parameters for reproducible text-to-image workflows in AEC design across academia and practice 学术界和实践中AEC设计中可重复文本到图像工作流程的人类-人工智能通信参数

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

Automation in Construction

Pub Date : 2026-01-13 DOI: 10.1016/j.autcon.2026.106767

Pedro Meira-Rodríguez , Vicente López-Chao

Generative artificial intelligence (AI) is increasingly incorporated into architecture, engineering, and construction (AEC) workflows, yet its adoption has advanced faster than the development of robust communication frameworks that ensure reproducibility, controllability, and methodological transparency. Academic research often emphasizes exploratory prototypes or technical advances, whereas professional practice depends on empirically tested input combinations that seldom follow systematic documentation. This review examines 190 academic publications (2000–2025) and 812 practitioner cases to identify the core human–AI communication variables structuring image-based generative workflows across platforms such as Midjourney, DALL-E, and Stable Diffusion. By synthesizing these variables into a cross-platform taxonomy, the paper reframes them as design levers and reproducible parameters for AEC design at an early stage. In doing so, the paper advances the goals of automation, standardization, and traceability in AEC workflows by demonstrating that reproducibility in generative design depends not only on model performance but on the communicability and documentation of user–model interactions.

生成式人工智能（AI）越来越多地融入到架构、工程和施工（AEC）工作流程中，但它的采用比确保可重复性、可控性和方法透明度的健壮通信框架的发展更快。学术研究通常强调探索性原型或技术进步，而专业实践依赖于经验测试的输入组合，很少遵循系统文档。本文审查了190篇学术出版物（2000-2025）和812个实践案例，以确定跨平台（如Midjourney， DALL-E和Stable Diffusion）构建基于图像的生成工作流的核心人类-人工智能交流变量。通过将这些变量综合到跨平台分类中，本文将它们重新定义为AEC设计早期阶段的设计杠杆和可重复参数。在此过程中，本文通过证明生成式设计的再现性不仅取决于模型性能，还取决于用户模型交互的可沟通性和文档化，推进了AEC工作流程中自动化、标准化和可追溯性的目标。

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引用次数: 0

Mapping digital twin applications in infrastructure and the built environment across research types, methods, sectors, phases, and scales 绘制跨研究类型、方法、部门、阶段和规模的基础设施和建筑环境中的数字孪生应用

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

Automation in Construction

Pub Date : 2026-01-13 DOI: 10.1016/j.autcon.2026.106778

Soheila Kookalani , Stephen Green , Peihang Luo , Hamidreza Alavi , Erika Parn , Zhaojie Sun , Ioannis Brilakis

Digital Twin technologies are increasingly used in infrastructure and the built environment to create dynamic, data-driven models of physical assets and processes. This review analyses recent advancements across sectors such as tunnels, bridges, roads, buildings, construction management, and urban planning, covering all life-cycle phases from design to operation. Integrating Digital Twins with Building Information Modelling, Internet of Things sensors, and Artificial Intelligence enhances real-time monitoring, decision-making, and asset performance. Key methods include monitoring, modelling, and simulation, which improve resource use and proactive maintenance. However, adoption faces challenges such as poor data interoperability, high costs, and technical complexity in merging multiple technologies. Ethical and governance issues around data privacy and security also persist. The review identifies future research needs in improving interoperability, expanding predictive analytics, and assessing large-scale impacts. It highlights Digital Twins' potential to improve resilience, efficiency, and sustainability, stressing the need for policy support and stakeholder collaboration.

数字孪生技术越来越多地用于基础设施和建筑环境，以创建物理资产和流程的动态数据驱动模型。本综述分析了隧道、桥梁、道路、建筑、施工管理和城市规划等领域的最新进展，涵盖了从设计到运营的所有生命周期阶段。将数字孪生与建筑信息模型、物联网传感器和人工智能相结合，增强实时监控、决策和资产绩效。关键方法包括监控、建模和仿真，这些方法可以改善资源利用和主动维护。然而，采用面临着诸如数据互操作性差、成本高以及合并多种技术时的技术复杂性等挑战。围绕数据隐私和安全的道德和治理问题也依然存在。该综述确定了未来在改进互操作性、扩展预测分析和评估大规模影响方面的研究需求。报告强调了数字孪生在提高韧性、效率和可持续性方面的潜力，强调了政策支持和利益相关者合作的必要性。

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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 : 2026-01-13 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