Automation in Construction最新文献

英文中文

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 : 2026-01-29 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

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 : 2026-01-29 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

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 : 2026-01-28 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。

{"title":"A digital monitoring, delay detection and visualisation framework for construction projects: RealCONs","authors":"Kambiz Radman, Mostafa Babaeian Jelodar, Ruggiero Lovreglio","doi":"10.1016/j.autcon.2026.106781","DOIUrl":"10.1016/j.autcon.2026.106781","url":null,"abstract":"<div><div>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 (χ<sup>2</sup> = 28.93, <em>p</em> < .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.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"183 ","pages":"Article 106781"},"PeriodicalIF":11.5,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI-driven decision support system for construction cost forecasting and consultation using optimized deep learning and language models 人工智能驱动的建筑成本预测和咨询决策支持系统，使用优化的深度学习和语言模型

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

Automation in Construction

Pub Date : 2026-01-28 DOI: 10.1016/j.autcon.2026.106797

Jui-Sheng Chou, Mei-Yuan Lin, Nguyen-Ngan-Hanh Pham

Fluctuations in construction material prices significantly affect project budgets and bidding strategies via the Construction Cost Index (CCI). This paper develops an AI-driven decision-support system for construction cost forecasting and consultation, integrating deep learning and Large Language Models (LLMs) to enable intelligent CCI prediction. A multi-source data framework combines historical CCI records, macroeconomic indicators, and sentiment extracted from Traditional Chinese construction news. Time-series forecasting employs an Extended Long Short-Term Memory (xLSTM) network, while sentiment models are fine-tuned using Quantized Low-Rank Adaptation (QLoRA). Model hyperparameters for both the QLoRA-fine-tuned LLMs and the xLSTM forecasting models are optimized via the Pilgrimage Walk Optimization (PWO) algorithm, yielding two horizon-specific configurations for short- and medium-term forecasting. Experimental results demonstrate that integrating sentiment features and PWO-based tuning consistently improves forecasting accuracy relative to baseline models. The deployed platform integrates CCI forecasting, sentiment analytics, and retrieval-augmented consultation to provide interpretable forecasts that enhance cost control and decision-making in construction management.

建筑材料价格的波动通过建筑成本指数（CCI）显著影响项目预算和投标策略。本文开发了一个人工智能驱动的工程造价预测与咨询决策支持系统，将深度学习和大语言模型（llm）相结合，实现CCI的智能预测。一个多源数据框架结合了历史CCI记录、宏观经济指标和从中国传统建筑新闻中提取的情绪。时间序列预测采用扩展长短期记忆（xLSTM）网络，而情绪模型则使用量化低秩自适应（QLoRA）进行微调。qlora微调llm和xLSTM预测模型的模型超参数通过朝圣行走优化（ppo）算法进行优化，得到两种特定水平的短期和中期预测配置。实验结果表明，与基线模型相比，整合情感特征和基于pw的调优一致地提高了预测精度。部署的平台集成了CCI预测、情感分析和检索增强咨询，以提供可解释的预测，从而加强施工管理中的成本控制和决策。

{"title":"AI-driven decision support system for construction cost forecasting and consultation using optimized deep learning and language models","authors":"Jui-Sheng Chou, Mei-Yuan Lin, Nguyen-Ngan-Hanh Pham","doi":"10.1016/j.autcon.2026.106797","DOIUrl":"10.1016/j.autcon.2026.106797","url":null,"abstract":"<div><div>Fluctuations in construction material prices significantly affect project budgets and bidding strategies via the Construction Cost Index (CCI). This paper develops an AI-driven decision-support system for construction cost forecasting and consultation, integrating deep learning and Large Language Models (LLMs) to enable intelligent CCI prediction. A multi-source data framework combines historical CCI records, macroeconomic indicators, and sentiment extracted from Traditional Chinese construction news. Time-series forecasting employs an Extended Long Short-Term Memory (xLSTM) network, while sentiment models are fine-tuned using Quantized Low-Rank Adaptation (QLoRA). Model hyperparameters for both the QLoRA-fine-tuned LLMs and the xLSTM forecasting models are optimized via the Pilgrimage Walk Optimization (PWO) algorithm, yielding two horizon-specific configurations for short- and medium-term forecasting. Experimental results demonstrate that integrating sentiment features and PWO-based tuning consistently improves forecasting accuracy relative to baseline models. The deployed platform integrates CCI forecasting, sentiment analytics, and retrieval-augmented consultation to provide interpretable forecasts that enhance cost control and decision-making in construction management.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"183 ","pages":"Article 106797"},"PeriodicalIF":11.5,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Noise-robust self-supervised learning with frequency-bias decomposition for TBM muck particle size distribution prediction 基于频率偏差分解的噪声鲁棒自监督学习TBM渣土粒径分布预测

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

Automation in Construction

Pub Date : 2026-01-27 DOI: 10.1016/j.autcon.2026.106802

Guoqiang Huang , Chengjin Qin , Jie Lu , Pengcheng Xia , Haodi Wang , Chengliang Liu

Accurately predicting muck particle size distribution (PSD) of Tunnel Boring Machine (TBM) is constrained by the cumbersome process of manual annotation and environmental noise. This paper investigates robust prediction of muck PSD curve under noisy TBM operation conditions, while reducing reliance on manual annotations. A noise-robust self-supervised learning method with frequency-bias decomposition is proposed, which integrates contrastive pre-training based on noise augmentation, frequency-domain bias decomposition, and hybrid edge-aware loss function. The experiments show that with only 10% annotation, it achieves performance comparable to existing models trained on 90% annotation, with a maximum particle size MAPE of 6.7% and Rosin-Rammler parameter errors between 10 and 20%. These results demonstrate a low-cost, accurate, and noise-robust approach for muck monitoring, substantially reducing the need for manual annotation and improving prediction reliability. Future work will combine muck PSD with multi-modal TBM excavation data to support intelligent tunneling decision-making.

准确预测隧道掘进机的渣土粒径分布受到人工标注过程繁琐和环境噪声的制约。本文研究了在有噪声的TBM运行条件下的渣土PSD曲线的鲁棒预测，同时减少了对人工标注的依赖。将基于噪声增强、频域偏置分解和混合边缘感知损失函数的对比预训练相结合，提出了一种基于频域偏置分解的噪声鲁棒自监督学习方法。实验表明，在仅使用10%标注的情况下，该模型的性能与使用90%标注训练的现有模型相当，最大粒径MAPE为6.7%，Rosin-Rammler参数误差在10% ~ 20%之间。这些结果证明了一种低成本、准确、抗噪声的渣土监测方法，大大减少了人工注释的需要，提高了预测的可靠性。未来的工作将结合泥质PSD和多模态TBM开挖数据，以支持智能隧道决策。

{"title":"Noise-robust self-supervised learning with frequency-bias decomposition for TBM muck particle size distribution prediction","authors":"Guoqiang Huang , Chengjin Qin , Jie Lu , Pengcheng Xia , Haodi Wang , Chengliang Liu","doi":"10.1016/j.autcon.2026.106802","DOIUrl":"10.1016/j.autcon.2026.106802","url":null,"abstract":"<div><div>Accurately predicting muck particle size distribution (PSD) of Tunnel Boring Machine (TBM) is constrained by the cumbersome process of manual annotation and environmental noise. This paper investigates robust prediction of muck PSD curve under noisy TBM operation conditions, while reducing reliance on manual annotations. A noise-robust self-supervised learning method with frequency-bias decomposition is proposed, which integrates contrastive pre-training based on noise augmentation, frequency-domain bias decomposition, and hybrid edge-aware loss function. The experiments show that with only 10% annotation, it achieves performance comparable to existing models trained on 90% annotation, with a maximum particle size MAPE of 6.7% and Rosin-Rammler parameter errors between 10 and 20%. These results demonstrate a low-cost, accurate, and noise-robust approach for muck monitoring, substantially reducing the need for manual annotation and improving prediction reliability. Future work will combine muck PSD with multi-modal TBM excavation data to support intelligent tunneling decision-making.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"183 ","pages":"Article 106802"},"PeriodicalIF":11.5,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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 : 2026-01-27 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。

{"title":"Multi-objective scientific approach to problem solving-inspired optimization integrated with the finite element method for automated structural design","authors":"Dinh-Nhat Truong , Jui-Sheng Chou","doi":"10.1016/j.autcon.2026.106770","DOIUrl":"10.1016/j.autcon.2026.106770","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"183 ","pages":"Article 106770"},"PeriodicalIF":11.5,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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 : 2026-01-27 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。

{"title":"Automated diagnosis of bridge expansion joint defects using voiceprint features and deep learning","authors":"Yixuan Chen , Hongzhe Zhao , Yichao Xu , Yufeng Zhang , Jian Zhang","doi":"10.1016/j.autcon.2025.106739","DOIUrl":"10.1016/j.autcon.2025.106739","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"183 ","pages":"Article 106739"},"PeriodicalIF":11.5,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Weld seam extraction and path generation for robotic welding of steel structures based on 3D vision 基于三维视觉的钢结构机器人焊接焊缝提取与路径生成

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

Automation in Construction

Pub Date : 2026-01-27 DOI: 10.1016/j.autcon.2026.106792

Jinxin Yi , Xuan Kong , Hao Tang , Jie Zhang , Zhenming Chen , Lu Deng

Recent advances in computer vision have provided new solutions for intelligent welding. However, existing vision-based weld seam extraction techniques exhibit limited adaptability to various workpieces in unstructured environments. Therefore, this paper proposes a three-dimensional vision-based method tailored for weld seam extraction and path generation. The proposed method synergizes a deep learning-based point cloud segmentation technique with an improved multi-scale point cloud registration algorithm to reconstruct the complete point cloud model of all weld regions in the workpieces. Subsequently, the welding paths and torch poses are calculated using an optimized multi-plane fitting algorithm integrated with geometry model of weld seam. Experimental validation on four workpieces demonstrates that the proposed method achieves good accuracy and outperforms the existing techniques in terms of efficiency and applicability, offering a robust solution for automated welding of steel structures.

计算机视觉的最新进展为智能焊接提供了新的解决方案。然而，现有的基于视觉的焊缝提取技术对非结构化环境中各种工件的适应性有限。因此，本文提出了一种针对焊缝提取和路径生成的三维视觉方法。该方法将基于深度学习的点云分割技术与改进的多尺度点云配准算法相结合，重建工件中所有焊缝区域的完整点云模型。然后，结合焊缝几何模型，采用优化后的多平面拟合算法计算焊接路径和焊枪位姿。在4个工件上进行的实验验证表明，该方法具有良好的精度，在效率和适用性方面优于现有的方法，为钢结构自动化焊接提供了可靠的解决方案。

引用次数: 0

LLM-enabled multi-agent framework for natural language interaction with graph-based digital twins 支持llm的多代理框架，用于与基于图的数字孪生进行自然语言交互

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

Automation in Construction

Pub Date : 2026-01-27 DOI: 10.1016/j.autcon.2026.106791

Yuandong Pan , Mudan Wang , Linjun Lu , Rabindra Lamsal , Erika Pärn , Sisi Zlatanova , Ioannis Brilakis

Digital twins are increasingly used in the Architecture, Engineering, and Construction (AEC) industry, but their adoption is often hindered by the need for specialised knowledge, such as database querying. This paper presents Graph-DT-GPT, a multi-agent framework that integrates Large Language Models (LLMs) with graph-based digital twins to enable natural language interaction. The framework is designed with modular agents, including decision, query generation, and answer extraction, and grounds all LLMs’ outputs in structured graph data to improve response reliability and reduce hallucinations. The framework is evaluated on two use cases: a city-level graph with over 40,000 building nodes and room-level apartment layout graphs. Graph-DT-GPT achieves 100% and 95.5% answer correctness using Claude Sonnet 4.5 and GPT-4o, respectively, in the city-scale case, and 100% correctness in the room-level case, significantly outperforming baseline methods including LangChain Neo4j pipelines by approximately 40% and 10%, respectively. These results demonstrate its scalability and potential to enhance accessible, accurate information retrieval in AEC digital twin applications.

数字双胞胎越来越多地用于架构、工程和建筑（AEC）行业，但它们的采用往往受到对专业知识（如数据库查询）的需求的阻碍。本文介绍了Graph-DT-GPT，这是一个多智能体框架，它将大型语言模型（llm）与基于图的数字双胞胎集成在一起，以实现自然语言交互。该框架采用模块化代理设计，包括决策、查询生成和答案提取，并将llm的所有输出基于结构化图数据，以提高响应可靠性并减少幻觉。该框架在两个用例上进行评估：包含超过40,000个建筑节点的城市级图和房间级公寓布局图。使用Claude Sonnet 4.5和gpt - 40， Graph-DT-GPT在城市规模的情况下分别达到100%和95.5%的答案正确性，在房间级别的情况下达到100%的正确性，显著优于包括LangChain Neo4j管道在内的基线方法，分别约为40%和10%。这些结果证明了它的可扩展性和潜力，以提高可访问的，准确的信息检索在AEC数字孪生应用。

{"title":"LLM-enabled multi-agent framework for natural language interaction with graph-based digital twins","authors":"Yuandong Pan , Mudan Wang , Linjun Lu , Rabindra Lamsal , Erika Pärn , Sisi Zlatanova , Ioannis Brilakis","doi":"10.1016/j.autcon.2026.106791","DOIUrl":"10.1016/j.autcon.2026.106791","url":null,"abstract":"<div><div>Digital twins are increasingly used in the Architecture, Engineering, and Construction (AEC) industry, but their adoption is often hindered by the need for specialised knowledge, such as database querying. This paper presents Graph-DT-GPT, a multi-agent framework that integrates Large Language Models (LLMs) with graph-based digital twins to enable natural language interaction. The framework is designed with modular agents, including decision, query generation, and answer extraction, and grounds all LLMs’ outputs in structured graph data to improve response reliability and reduce hallucinations. The framework is evaluated on two use cases: a city-level graph with over 40,000 building nodes and room-level apartment layout graphs. Graph-DT-GPT achieves 100% and 95.5% answer correctness using Claude Sonnet 4.5 and GPT-4o, respectively, in the city-scale case, and 100% correctness in the room-level case, significantly outperforming baseline methods including LangChain Neo4j pipelines by approximately 40% and 10%, respectively. These results demonstrate its scalability and potential to enhance accessible, accurate information retrieval in AEC digital twin applications.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"183 ","pages":"Article 106791"},"PeriodicalIF":11.5,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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 : 2026-01-27 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。

{"title":"Control strategies for Cellular Automata-based generative design in architecture and urbanism","authors":"Yiming Liu, Christiane M. Herr","doi":"10.1016/j.autcon.2025.106754","DOIUrl":"10.1016/j.autcon.2025.106754","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"183 ","pages":"Article 106754"},"PeriodicalIF":11.5,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

首页上一页

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

Automation in Construction

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀