Automation in Construction最新文献

英文中文

Semantic segmentation for crack detection via generative knowledge distillation

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-04-24 DOI: 10.1016/j.autcon.2025.106201

Seungbo Shim

Recently, deep learning has garnered significant attention for its potential to detect damage in infrastructure. This approach requires a vast dataset for optimal performance; however, acquiring large-scale training data remains challenging. To overcome this challenge, this paper proposes a new technique for enhancing crack detection accuracy by synthesizing virtual crack images through generative algorithms. To this end, generative adversarial networks are used for generating new insights for crack images, and these insights are subsequently integrated into crack detection models using knowledge distillation. The proposed method obviates the need for additional crack images and enriches the diversity of the dataset. This approach yields a 5.09% crack intersection over union and a 3.51% improvement in the F1-score across 17 neural network models, outperforming traditional supervised learning methods. The proposed method is expected to gain widespread adoption in the future to address data scarcity challenges and enhance the safety of infrastructure maintenance.

引用次数: 0

Real-time in-tube concrete level tracking during concrete-filled steel tubular arch bridge construction using infrared thermography and computer vision

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-04-24 DOI: 10.1016/j.autcon.2025.106227

Chongsheng Cheng , Jie Yu , Zhengsong Xiang , Shaorui Wang , Haonan Cai , Jianting Zhou , Hong Zhang

Automated remote monitoring of the concrete pouring process in concrete-filled steel tubular (CFST) arch bridges is a challenging task due to long distances, oblique camera angles, and occlusion, which hinder the accurate and continuous tracking of the process using existing computer vision (CV)-based methods. This paper proposed an integrated CV system for real-time, automated tracking and localization of the in-tube concrete pumping level with infrared thermography. The main contributions include: (1) Proposing a PNP-based orthographic rectification method to accurately correct the scale distortion of oblique infrared images for arch bridge structures. (2) Developing an improved Kalman filter method for stably tracking the concrete pumping level in infrared images with a low signal-to-noise ratio. The results show that the proposed system can achieve mm-level accuracy for the scaled model in indoor experiments, and its effectiveness is evaluated for an actual construction process at a distance of a hundred meters.

{"title":"Real-time in-tube concrete level tracking during concrete-filled steel tubular arch bridge construction using infrared thermography and computer vision","authors":"Chongsheng Cheng , Jie Yu , Zhengsong Xiang , Shaorui Wang , Haonan Cai , Jianting Zhou , Hong Zhang","doi":"10.1016/j.autcon.2025.106227","DOIUrl":"10.1016/j.autcon.2025.106227","url":null,"abstract":"<div><div>Automated remote monitoring of the concrete pouring process in concrete-filled steel tubular (CFST) arch bridges is a challenging task due to long distances, oblique camera angles, and occlusion, which hinder the accurate and continuous tracking of the process using existing computer vision (CV)-based methods. This paper proposed an integrated CV system for real-time, automated tracking and localization of the in-tube concrete pumping level with infrared thermography. The main contributions include: (1) Proposing a PNP-based orthographic rectification method to accurately correct the scale distortion of oblique infrared images for arch bridge structures. (2) Developing an improved Kalman filter method for stably tracking the concrete pumping level in infrared images with a low signal-to-noise ratio. The results show that the proposed system can achieve mm-level accuracy for the scaled model in indoor experiments, and its effectiveness is evaluated for an actual construction process at a distance of a hundred meters.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"175 ","pages":"Article 106227"},"PeriodicalIF":9.6,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863813","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

Integrating wooden altarpieces into H-BIM: Geometric profiling, complex artworks, and digital heritage mapping

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-04-23 DOI: 10.1016/j.autcon.2025.106213

Juan Moyano, Eva Martínez, Juan E. Nieto-Julián, María Fernández-Alconchel

In the study and conservation of Cultural Heritage, various disciplines contribute to the research aimed at extracting information from both historical objects and heritage buildings. The contribution of this work is part of an interdisciplinary process to model, register, and evaluate complex models of knowledge. Evaluate a complex model, where there are works of art made of wood, painting, and sculpture. The results demonstrate a process of analysis and geometric characterisation of the shapes, in which most of the profiles are worked with a new methodology of the Best Fit Model (BFMP), and in which its analysis represents a deviation between a range of 7 and 9 mm. The development of low-relief models is based on the Poisson reconstruction equation, applied through a variable workflow using multiple software tools. Furthermore, an Entity Information Matrix (EIM) is introduced, enhancing the exchange and classification of architectural data. This study supports the integration of real-world 3D scans into BIM environments, providing a replicable model particularly suited for the digitization of altarpieces and façades.

{"title":"Integrating wooden altarpieces into H-BIM: Geometric profiling, complex artworks, and digital heritage mapping","authors":"Juan Moyano, Eva Martínez, Juan E. Nieto-Julián, María Fernández-Alconchel","doi":"10.1016/j.autcon.2025.106213","DOIUrl":"10.1016/j.autcon.2025.106213","url":null,"abstract":"<div><div>In the study and conservation of Cultural Heritage, various disciplines contribute to the research aimed at extracting information from both historical objects and heritage buildings. The contribution of this work is part of an interdisciplinary process to model, register, and evaluate complex models of knowledge. Evaluate a complex model, where there are works of art made of wood, painting, and sculpture. The results demonstrate a process of analysis and geometric characterisation of the shapes, in which most of the profiles are worked with a new methodology of the Best Fit Model (BFMP), and in which its analysis represents a deviation between a range of 7 and 9 mm. The development of low-relief models is based on the Poisson reconstruction equation, applied through a variable workflow using multiple software tools. Furthermore, an Entity Information Matrix (EIM) is introduced, enhancing the exchange and classification of architectural data. This study supports the integration of real-world 3D scans into BIM environments, providing a replicable model particularly suited for the digitization of altarpieces and façades.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"175 ","pages":"Article 106213"},"PeriodicalIF":9.6,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143858991","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

Self-adaptive seam detection framework for unmanned structural steel welding robots in unstructured environments

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-04-23 DOI: 10.1016/j.autcon.2025.106221

Yuzhen He , Zhaoqi Huang , Haotian Liu , Jingang Ye , Yujie Lu , Xianzhong Zhao

Existing vision-based seam detection frameworks for structural steel welding robots perform well in static, predefined workspaces but struggle in dynamic, unstructured real production environments. Therefore, a self-adaptive seam detection framework is proposed that enables welding robots to interpret the actual welding environment through an estimation of the target workpiece pose. An enhanced pose estimation algorithm is developed to generate a reliable workpiece pose estimation based on solely one RGB image. Based on this real-time workpiece pose, the system autonomously determined the necessary robot movements toward an optimal position and orientation for subsequent high-precision structured light sensor measurements. The seam extraction and welding trajectory planning were then completed through an automated process. Experimental results demonstrate that the proposed framework not only enables cost-effective, fully automated weld seam detection in dynamic unstructured environments, but also achieves 72 % higher efficiency than conventional methods while eliminating human intervention.

现有的基于视觉的钢结构焊接机器人焊缝检测框架在静态、预定义的工作空间中表现良好，但在动态、非结构化的实际生产环境中却举步维艰。因此，我们提出了一种自适应焊缝检测框架，使焊接机器人能够通过对目标工件姿态的估计来解释实际的焊接环境。我们开发了一种增强型姿态估计算法，可仅根据一张 RGB 图像生成可靠的工件姿态估计。根据这一实时工件姿态，系统可自主确定必要的机器人运动，使其达到最佳位置和方向，以便进行后续的高精度结构光传感器测量。焊缝提取和焊接轨迹规划随后通过自动化流程完成。实验结果表明，所提出的框架不仅能在动态非结构化环境中实现经济高效的全自动焊缝检测，而且在消除人工干预的同时，效率比传统方法高出 72%。

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引用次数: 0

Efficient instance segmentation framework for UAV-based pavement distress detection

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-04-22 DOI: 10.1016/j.autcon.2025.106195

Jiakai Zhou , Yang Wang , Wanlin Zhou

Pavement distress detection is critical for ensuring road safety. Recently, Unmanned Aerial Vehicles (UAVs) become an efficient means of capturing large-scale pavement images. However, traditional pavement distress detection methods face challenges with UAV images: object detection lacks pixel-level information, while semantic segmentation fails to differentiate between individual instances. This paper introduces PDIS-Net, an instance segmentation framework specifically designed for UAV-based pavement distress detection. PDIS-Net first employs a fully dynamic convolution kernel generation strategy, predicting both kernel positions and weights. These kernels are then optimized via metric learning and kernel fusion. Finally, these high-quality kernels are convolved with feature maps to produce accurate instance masks. Experimental results on the UAPD-Instance dataset reveal that PDIS-Net achieves a mean average precision (mAP) of 78.1% at 30.8 FPS, outperforming other methods by 15.4%. Furthermore, real-world tests validate the robustness and effectiveness of PDIS-Net in highway pavement distress detection, highlighting its potential for practical deployment.

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引用次数: 0

Parallel simulation and prediction techniques for digital twins in urban underground spaces

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-04-21 DOI: 10.1016/j.autcon.2025.106212

Haofeng Gong , Dong Su , Shiqi Zeng , Xiangsheng Chen

With the advancement of intelligent systems and the increasing utilization of underground spaces, digital twin technology has become pivotal in enhancing the efficiency and safety of subterranean operations and maintenance. The concept of parallel simulation and prediction (PSP) technology, a critical component in the realization of digital twins, along with its associated research challenges, requires further elucidation. This paper offers a comprehensive overview of current research on PSP within the context of digital twins for urban underground spaces. The 62 papers meeting the inclusion and exclusion criteria are categorized into two key areas: model updating and future evolution prediction. Key challenges identified include the need for regular updates in geometric models, the demand for real-time predictive analytics, and data-related issues in information modeling. Finally, future research directions are outlined, focusing on the automatic interpretation of detection data, self-updating digital twin models, and multi-source heterogeneous data integration technologies.

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引用次数: 0

Integration of BIM and ontologies for pumped storage hydropower design change management in EPC projects

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-04-21 DOI: 10.1016/j.autcon.2025.106189

Shihang Zhang , Sherong Zhang , Han Liu , Xiaohua Wang , Zhiyong Zhao , Chao Wang , Lei Yan

Design changes are an inevitable multidisciplinary issue in the Engineering, Procurement, and Construction (EPC) projects for pumped storage hydropower systems. However, semantic heterogeneity poses significant challenges making Building Information Modeling (BIM) workflows for design change management time-consuming and error-prone. To address this issue, this paper proposes an ontology integration approach that unifies decentralized knowledge of Industry Foundation Classes (IFC) and the BIM Collaboration Format (BCF). A Semantic Design Change Management System (SDCMS) is developed for EPC contractors and validated through a case study. The results indicate that the proposed approach achieves significant improvements in system efficiency and data standardization. This paper highlights the potential for knowledge reuse to automate BIM workflows and provides practical insights into renewable energy construction management in the context of energy transition and carbon neutrality.

设计变更是抽水蓄能水电系统工程、采购和施工（EPC）项目中不可避免的多学科问题。然而，语义异构性带来了巨大挑战，使得设计变更管理的建筑信息建模（BIM）工作流程耗时且容易出错。为解决这一问题，本文提出了一种本体集成方法，将工业基础类（IFC）和 BIM 协作格式（BCF）的分散知识统一起来。本文为 EPC 承包商开发了语义设计变更管理系统（SDCMS），并通过案例研究进行了验证。结果表明，所提出的方法在系统效率和数据标准化方面取得了显著改善。本文强调了知识重用在实现 BIM 工作流程自动化方面的潜力，并为能源转型和碳中和背景下的可再生能源施工管理提供了实用见解。

引用次数: 0

Integrating domain-specific knowledge and fine-tuned general-purpose large language models for question-answering in construction engineering management

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-04-21 DOI: 10.1016/j.autcon.2025.106206

Shenghua Zhou , Xuefan Liu , Dezhi Li , Tiantian Gu , Keyan Liu , Yifan Yang , Mun On Wong

General-purpose Large Language Models (GLLMs) for Question-Answering (QA) of Construction Engineering Management (CEM) usually lack CEM knowledge and fine-tuning datasets, leading to unsatisfactory performance. Hence, this paper integrates the CEM External Knowledge Base (CEM-EKB) with out-of-domain fine-tuned GLLMs for CEM-QA. It encompasses (i) devising a process to develop the CEM-EKB with 235 documents, (ii) conducting out-of-domain fine-tuning to enhance GLLMs' abilities, (iii) integrating CEM-EKB with fine-tuned GLLMs, (iv) building CEM-QA test datasets with 5050 Multiple-Choice Questions (MCQs) and 100 Case-Based Questions (CBQs), and (v) comparing GLLMs' performance. The results indicate that CEM knowledge-incorporated fine-tuned GLLMs surpass original GLLMs by an average of 27.1 % in professional examinations, with an average improvement of 27.5 % across 7 CEM subdomains and 22.05 % for CBQs. This paper contributes to devising an effective, reusable, and updatable CEM-EKB; revealing the feasibility of out-of-domain datasets for fine-tuning; and sharing a large-scale CEM-QA test dataset.

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引用次数: 0

Robust crack detection in complex slab track scenarios using STC-YOLO and synthetic data with highly simulated modeling

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-04-21 DOI: 10.1016/j.autcon.2025.106219

Wenbo Hu , Xianhua Liu , Zhizhang Zhou , Weidong Wang , Zheng Wu , Zhengwei Chen

Crack detection in slab tracks plays a crucial role in accident prevention. Existing algorithms primarily operate on monotonous concrete backgrounds and often struggle with data scarcity and complex scenes. This paper proposes a parametric slab track model replicating real-world inspection conditions through high-fidelity virtual simulation, enabling realistic synthetic crack data generation. The subsequently developed STC-YOLO network utilizes these synthetic images to enhance fine crack detection in complex slab track scenes. Results show that STC-YOLO trained on synthetic data (4:1 virtual-to-real ratio) achieves over 20 % improvements in both mAP and recall compared to using no virtual images, outperforming traditional augmentation methods like horizontal flipping and color dithering. Moreover, STC-YOLO exhibits over 6 % higher mAP than the baseline algorithm and surpasses five state-of-the-art object detection networks. The proposed algorithm greatly reduces the cost of data acquisition.

引用次数: 0

Deep learning-based point cloud completion for MEP components

IF 9.6 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Automation in Construction

Pub Date : 2025-04-19 DOI: 10.1016/j.autcon.2025.106218

Hongzhe Yue , Qian Wang , Yangzhi Yan , Guanying Huang

Point clouds are increasingly leveraged for as-built model reconstruction of facilities. However, point clouds of Mechanical, Electrical, and Plumbing (MEP) systems often experience extensive occlusions, which heavily affect the performance of model reconstruction. To address this challenge, this paper explores deep learning (DL)-based point cloud completion algorithms to complete occluded MEP point clouds. Due to the limited availability of datasets, parametric BIM modeling and occlusion simulation are used to generate synthetic point cloud datasets of MEP components. Based on generated datasets, the effectiveness of five different DL algorithms and five distinct training strategies for point cloud completion are investigated. The results indicate that: (1) The PoinTr model with a pre-training strategy achieved the best Chamfer Distance (CD) and F-score, demonstrating effective completion even with 75 % missing point clouds. 2) Applying the proposed point cloud completion method to three practical tasks further demonstrates the algorithm's applicability.

引用次数: 0

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