Automation in Construction最新文献

英文中文

Fusing enhanced YOLO and knowledge graph-based large language models for automatic risk perception in tower crane operations 融合增强的YOLO和基于知识图的大型语言模型进行塔机作业风险自动感知

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

Automation in Construction

Pub Date : 2026-03-01 Epub Date: 2026-02-05 DOI: 10.1016/j.autcon.2026.106823

Lingxiao Wang , Jingfeng Yuan , Shu Su , Hongxing Ding , Yu Bai , Miroslaw J. Skibniewski

Tower crane operations in construction are inherently hazardous due to complex and dynamic site environments. Enhancing operators' perceptual and cognitive capabilities is essential for ensuring safety and improving situational awareness. This paper presents an integrated framework that combines an improved YOLOv8 model with a Knowledge Graph (KG)-enhanced large language model to achieve proactive and intelligent safety management. The improved YOLOv8 incorporates attention-based optimization to improve detection accuracy for small targets in tower crane perspectives. A domain-specific safety KG is constructed to represent critical entities, relationships, and operational contexts, and is aligned with a fine-tuned GPT model, enabling semantic reasoning and context-aware hazard interpretation. The integrated system links visual perception with structured knowledge reasoning to provide real-time and interpretable safety feedback. This approach enhances the perception, understanding, and decision-making capabilities of tower crane operators, transforming safety management from reactive monitoring to proactive and intelligent control in complex construction environments.

由于施工现场环境复杂多变，塔式起重机作业本身就具有危险性。提高操作员的感知和认知能力对于确保安全和提高态势感知至关重要。本文提出了一个集成框架，将改进的YOLOv8模型与知识图（KG）增强的大型语言模型相结合，实现主动智能安全管理。改进的YOLOv8集成了基于注意力的优化，以提高塔吊视角下对小目标的检测精度。构建一个特定于领域的安全KG来表示关键实体、关系和操作上下文，并与精细调整的GPT模型保持一致，支持语义推理和上下文感知的危险解释。集成系统将视觉感知与结构化知识推理联系起来，提供实时和可解释的安全反馈。该方法增强了塔机操作员的感知、理解和决策能力，将复杂施工环境中的安全管理从被动监控转变为主动智能控制。

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引用次数: 0

Improved Boundary-Aware Mask R-CNN using stereo vision for automated rebar inspection 改进的边界感知掩膜R-CNN使用立体视觉自动钢筋检测

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.2026.106801

Weijian Zhao , Ruoshui Xing , Cuiting Wei , Bochao Sun , Tianren Jiang , Qiliang Zhao

Rebar inspection is a critical but labor-intensive task in concrete construction quality control. This paper develops an improved Boundary-Aware Mask R-CNN (BA-Mask R-CNN) that incorporates a path-enhanced feature extraction network and a boundary-squeeze module to enhance segmentation performance. Trained on a self-constructed dataset of 3450 images, the proposed model achieves a mean Average Precision (mAP) of 91.84%, a mean Intersection over Union (mIoU) of 93.78%, an F1-score of 96.79%, and a Precision of 96.08%, outperforming the baseline Mask R-CNN by 6.69%, 7.35%, 4.36%, and 5.75%, respectively. The probability distributions of rebar diameters (8–22 mm) were obtained from multiple rotational viewpoints, and the corresponding mean values were subsequently computed. The proposed method accurately measures the mean diameters and spacing of rebars in double-layer meshes, with all measurement errors falling within standard engineering tolerances.

钢筋检验是混凝土施工质量控制中的一项关键而费力的工作。本文开发了一种改进的边界感知掩码R-CNN (BA-Mask R-CNN)，它结合了一个路径增强的特征提取网络和一个边界挤压模块来提高分割性能。在自建3450张图像的数据集上训练，该模型的平均精度（mAP）为91.84%，平均交集比（mIoU）为93.78%，f1得分为96.79%，精度为96.08%，分别比基线Mask R-CNN高6.69%,7.35%，4.36%和5.75%。在多个旋转视点得到钢筋直径（8 ~ 22 mm）的概率分布，并计算相应的均值。该方法能准确测量双层网格中钢筋的平均直径和间距，测量误差均在标准工程公差范围内。

引用次数: 0

Self-hosted multimodal large language models for speech-driven perception and navigation in construction robotics 建筑机器人中语音驱动感知和导航的自托管多模态大语言模型

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

Automation in Construction

Pub Date : 2026-03-01 Epub Date: 2026-01-31 DOI: 10.1016/j.autcon.2026.106805

Muhammad Anas Gopee , Samuel A. Prieto , Borja García de Soto

This paper explores self-hosted, open-weight multimodal large language models (LLMs) as a speech–vision interface for robot perception and navigation. Using structured function calling, the approach enables transparent control without application-specific training. Two data-local deployment modes are compared: a smaller on-robot model and a larger edge-server model. Both convert spoken commands and images into structured function calls for image capture, analysis, and waypoint navigation. Laboratory evaluations show that the edge-server model outperforms the on-robot model in speed and reliability, particularly in visual tasks, while the on-robot model performs competitively in simple workflows. Findings support hybrid deployment strategies that combine edge-hosted and on-robot models to balance performance and resilience, enabling responsive, cloud-independent robotic operation in construction environments.

本文探讨了自托管，开放权重的多模态大语言模型（llm）作为机器人感知和导航的语音视觉接口。使用结构化函数调用，该方法无需特定于应用程序的培训即可实现透明控制。比较了两种数据本地部署模式：较小的机器人模型和较大的边缘服务器模型。两者都将口头命令和图像转换为结构化函数调用，用于图像捕获、分析和航点导航。实验室评估表明，边缘服务器模型在速度和可靠性方面优于机器人模型，特别是在视觉任务中，而机器人模型在简单工作流程中具有竞争力。研究结果支持混合部署策略，将边缘托管和机器人模型结合起来，以平衡性能和弹性，从而在建筑环境中实现响应迅速、独立于云的机器人操作。

引用次数: 0

Semantic naming convention-based automated BIM generation of precast concrete components from 2D CAD drawings 基于语义命名约定的2D CAD图纸自动生成预制混凝土构件的BIM

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

Automation in Construction

Pub Date : 2026-03-01 Epub Date: 2026-02-03 DOI: 10.1016/j.autcon.2026.106804

Eunbeen Jeong , Seoyoung Jung , Insu Jung , Kyujin Ko , Junyoung Jang

The manual conversion of 2D CAD drawings to 3D BIM objects for Precast Concrete (PC) components is a time-consuming, error-prone bottleneck that hinders project efficiency. Existing automation studies have struggled with the complex, non-standard geometries typical of PC components. This paper presents a framework that automates BIM generation using a Semantic Naming Convention (SNC) to encode design information into 2D CAD attributes. Developed in the Dynamo visual programming environment, the system automatically creates fabrication-level BIM objects for PC columns, beams, and slabs. The framework's versatility was validated by successfully processing 100% of production drawings from three different manufacturers. A Charrette test showed the automated approach reduced modeling time by an average of 78.9%, improved model accuracy by 12.3, and cut human errors by over 90% compared to manual methods. This framework provides a practical solution to enhance productivity and model quality in the PC construction sector.

手工将2D CAD图纸转换为3D BIM对象用于预制混凝土（PC）组件是一个耗时且容易出错的瓶颈，阻碍了项目效率。现有的自动化研究一直在努力解决PC组件典型的复杂、非标准几何形状问题。本文提出了一个框架，该框架使用语义命名约定（SNC）自动生成BIM，将设计信息编码为2D CAD属性。该系统在Dynamo可视化编程环境中开发，可自动为PC柱、梁和板创建制造级BIM对象。通过成功处理来自三个不同制造商的100%生产图纸，验证了该框架的多功能性。Charrette测试表明，与手动方法相比，自动化方法平均减少了78.9%的建模时间，提高了12.3的模型精度，并将人为错误减少了90%以上。该框架提供了一个实用的解决方案，以提高生产力和模型质量的PC建设部门。

引用次数: 0

Construction site object detection with active transfer learning and weighted adaptive uncertainty-diversity sampling using a small imbalanced dataset 基于主动迁移学习和加权自适应不确定性多样性采样的小型不平衡数据集建筑工地目标检测

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

Automation in Construction

Pub Date : 2026-03-01 Epub Date: 2026-02-03 DOI: 10.1016/j.autcon.2026.106819

Karunakar Reddy Mannem , Eyob Mengiste , Dhanu Vardhan , Borja García de Soto , Fernando Bacao

This paper presents an active learning framework for robust object detection in dynamic construction environments, addressing the challenges of limited labeled data and high annotation costs. The framework integrates YOLOv10 with a weighted, adaptive uncertainty–diversity sampling strategy and employs transfer learning to mitigate cold-start issues and accelerate model convergence. The adaptive fusion mechanism dynamically weights multiple uncertainty measures (classification confidence, class entropy, and bounding box variance) while incorporating sample diversity to prioritize the most informative data. Experiments achieved mAP50 of 0.885 and mAP50–95 of 0.730 by Cycle 8. Rare classes showed notable gains: Circular ducts improved from 0.25 to 0.60 mAP50, and Drywall panels from 0.35 to 0.65. The approach reduced labeling effort by 30–40% compared to random sampling, showing its potential to improve the development of construction element recognition algorithms, including small items that are often not visible, such as safety equipment, building parts, and construction tools.

本文提出了一种用于动态建筑环境中鲁棒目标检测的主动学习框架，解决了有限的标记数据和高标注成本的挑战。该框架将YOLOv10与加权自适应不确定性多样性采样策略集成，并采用迁移学习来缓解冷启动问题并加速模型收敛。自适应融合机制动态加权多个不确定性度量（分类置信度、类熵和边界盒方差），同时结合样本多样性来优先考虑信息量最大的数据。经Cycle 8实验，mAP50为0.885，mAP50 - 95为0.730。稀有类别表现出显著的进步：圆形管道从0.25提高到0.60 mAP50，干墙板从0.35提高到0.65。与随机抽样相比，该方法减少了30-40%的标记工作，显示了其改善建筑元素识别算法发展的潜力，包括通常不可见的小项目，如安全设备、建筑部件和建筑工具。

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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 : 2026-03-01 Epub 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

Control strategies for Cellular Automata-based generative design in architecture and urbanism 基于元胞自动机的生成式建筑与城市设计控制策略

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.2025.106754

Yiming Liu, Christiane M. Herr

As Artificial Intelligence transforms design through decentralised and self-organising generative systems, Cellular Automata (CA) exemplify a foundational yet underexplored paradigm capable of bridging rule-based emergence and computational creativity in architecture and urbanism. Driven by simple local rules, CA produce spatially responsive and systemic patterns well-suited to capturing the dynamics of complex interrelated systems, making them valuable for generative design exploration. This review systematically investigates control strategies for guiding CA-based generative processes. It identifies temporal logic methods for adjusting CA behaviour through bibliometric analysis. The review further demonstrates control factors, computational control, and human-mediated control, analysing their impact on the adaptability of CA design processes at each stage through the content-based synthesis. The results reveal the advantages of different control strategies in guiding goal-directed CA generation. This study advances the understanding of CA-based design mechanisms and highlights opportunities to develop intelligent control, process-oriented design tools integrating data-driven and AI technologies.

随着人工智能通过分散和自组织的生成系统改变设计，细胞自动机（CA）体现了一种基础但尚未得到充分探索的范式，能够在建筑和城市规划中连接基于规则的出现和计算创造力。在简单的局部规则的驱动下，CA产生空间响应和系统模式，非常适合捕获复杂相互关联系统的动态，使它们对生成设计探索有价值。本文系统地研究了指导基于ca的生成过程的控制策略。它通过文献计量分析确定了调整CA行为的时间逻辑方法。本文进一步论证了控制因素、计算控制和人为控制，并通过基于内容的综合分析了它们对每个阶段CA设计过程适应性的影响。结果揭示了不同控制策略在指导目标导向CA生成方面的优势。本研究促进了对基于ca的设计机制的理解，并强调了开发集成数据驱动和人工智能技术的智能控制、面向过程的设计工具的机会。

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引用次数: 0

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 : 2026-03-01 Epub 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

Knowledge-augmented multi-modal data fusion and reasoning for automated crane lift monitoring 基于知识增强的多模态数据融合与推理的自动起重机升力监测

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

Automation in Construction

Pub Date : 2026-03-01 Epub Date: 2026-02-07 DOI: 10.1016/j.autcon.2026.106822

Junlin Wang , Songbo Hu , Yihai Fang , Hongling Guo

Crane lift operations are critical to construction productivity, yet their monitoring remains largely manual, fragmented, and inefficient. This paper introduces a knowledge-augmented, multi-modal data fusion and reasoning framework for the automated tracking and analysis of crane lifts. The proposed approach integrates a domain ontology to fuse computer vision, sensor signals, and schedule data, enabling a hierarchical hybrid reasoning pipeline that infers transient behaviours, segments complete operations, and maps them to scheduled tasks via similarity metrics. In a two-day field experiment, the system accurately recognised eight distinct behaviours with an overall accuracy of 0.911 and an average F1 score of 0.907, segmented lift cycles with a median duration error under 10 s, and mapped operations to scheduled orders with an F1 score of 0.944 and accuracy of 0.905. These results demonstrate the technical feasibility and robustness of the framework, which transforms low-level data into high-level, context-rich knowledge to support productivity assessment and workflow optimisation in construction environments.

起重机起重作业对施工效率至关重要，但其监控仍然主要是手动的、分散的和低效的。介绍了一种基于知识增强、多模态数据融合和推理的起重机自动跟踪分析框架。该方法集成了领域本体，融合了计算机视觉、传感器信号和调度数据，实现了分层混合推理管道，可以推断瞬态行为、分段完成操作，并通过相似性度量将它们映射到调度任务。在为期两天的现场实验中，该系统准确识别了8种不同的行为，总体精度为0.911，平均F1分数为0.907，分段举升周期的平均持续时间误差小于10秒，并将作业映射到计划订单，F1分数为0.944，精度为0.905。这些结果证明了该框架的技术可行性和鲁棒性，该框架将低级数据转换为高级、上下文丰富的知识，以支持建筑环境中的生产力评估和工作流优化。

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引用次数: 0

From geometry to graph: Automation of building performance modeling via convex graph encoding 从几何到图形：通过凸图编码实现建筑性能建模的自动化

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.2026.106815

Yihui Li , Jun Xiao , Hao Zhou , Borong Lin

Early-stage design decisions strongly influence a building’s lifetime energy performance, yet the transformation from architectural geometry to analysis-ready models remains fragmented. This paper presents CUGER, an automated framework that converts complex 3D building geometries into physically consistent heterogeneous graphs for building energy analysis. CUGER integrates a convex optimization-based segmentation algorithm with structured graph encoding to directly transform CAD geometries into simulation- and learning-ready representations. Evaluated on 266 architecturally diverse building models, the framework achieved an average geometric-to-graph conversion success rate above 80% with strong topological consistency (

R^{2} = 0.82

). Building on the generated graphs, a heterogeneous ZoneGNN model enables zone-level energy prediction of single building, achieving accurate and stable performance (

R^{2} = 0.88 \pm 0.01

) and outperforming MLP baselines, particularly under limited training data. Overall, CUGER establishes a topology-preserving and fully automatable bridge between architectural modeling, performance simulation, and data-driven prediction for early-stage building design.

早期阶段的设计决策强烈地影响着建筑的终身能源性能，然而从建筑几何到分析就绪模型的转变仍然是碎片化的。本文介绍了CUGER，这是一个自动化框架，可将复杂的3D建筑几何形状转换为物理上一致的异构图形，用于建筑能源分析。CUGER将基于凸优化的分割算法与结构化图形编码集成在一起，直接将CAD几何图形转换为仿真和学习准备的表示。通过对266个不同建筑模型的评估，该框架实现了80%以上的几何到图形的平均转换成功率，具有很强的拓扑一致性（R2=0.82）。在生成的图的基础上，异构ZoneGNN模型可以实现单个建筑的区域级能量预测，实现准确和稳定的性能（R2=0.88±0.01），并且优于MLP基线，特别是在有限的训练数据下。总的来说，CUGER在建筑建模、性能模拟和早期建筑设计的数据驱动预测之间建立了一个拓扑保持和完全自动化的桥梁。

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