Automation in Construction最新文献_第6页

Design of deployable bridge using multistable miura-ori structure for emergency rescue 多稳态miura-ori结构应急救援可展开桥的设计

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-12 DOI: 10.1016/j.autcon.2025.106233

Wei Wang , Xu Li , Peng Yan , Hailin Huang , Bing Li

引用次数: 0

CircularBIM: Future needs at the convergence of building information modelling and the circular economy CircularBIM：建筑信息模型与循环经济融合的未来需求

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-09 DOI: 10.1016/j.autcon.2025.106250

Judith Amudjie , Albert P.C. Chan , Amos Darko , Caleb Debrah , Kofi Agyekum

{"title":"CircularBIM: Future needs at the convergence of building information modelling and the circular economy","authors":"Judith Amudjie , Albert P.C. Chan , Amos Darko , Caleb Debrah , Kofi Agyekum","doi":"10.1016/j.autcon.2025.106250","DOIUrl":"10.1016/j.autcon.2025.106250","url":null,"abstract":"<div><div>The progressions of industrial revolutions have enabled diverse digital technologies in architecture, engineering, construction and operation (AECO), with Building Information Modelling (BIM) gaining notable attention. Concurrently, the circular economy (CE) has emerged as a crucial strategy for addressing socio-economic issues such as waste, resource depletion, and climate change. However, limitations within BIM or CE implementations have led to these persisting socio-economic challenges. This paper presents a comprehensive state-of-the-art review on the convergence of BIM and CE (hereafter, CircularBIM), utilizing a mixed-method approach (bibliometric and systematic review techniques), analysing 89 relevant studies. Key research trends identified include life cycle assessments, deconstruction, BIM-based systems, waste management, and energy efficiency. This paper suggests future research should integrate recommender systems for CircularBIM, employ real-time performance integrated CircularBIM directory, increase expert studies and broaden parameters integration for CircularBIM. Ultimately, this paper aims to enhance CircularBIM implementation in the AECO sector, providing insights for all stakeholders.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"176 ","pages":"Article 106250"},"PeriodicalIF":9.6,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929429","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

Feature extraction for acoustic leakage detection in water pipelines 输水管道声泄漏检测的特征提取

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-09 DOI: 10.1016/j.autcon.2025.106248

Tengfei An , Liang Ma , Deen Li , Wenli Liu , Hanbin Luo

{"title":"Feature extraction for acoustic leakage detection in water pipelines","authors":"Tengfei An , Liang Ma , Deen Li , Wenli Liu , Hanbin Luo","doi":"10.1016/j.autcon.2025.106248","DOIUrl":"10.1016/j.autcon.2025.106248","url":null,"abstract":"<div><div>Leakage detection (LD) in water pipelines is crucial for reducing water wastage. Acoustic methods for pipeline monitoring are gaining increasing popularity. However, challenges like noise, reverberation, and time-varying factors in pipelines hinder feature extraction. To ameliorate this problem, this paper introduces a feature representation method named EF_Mel spectrogram and proposes a multi-dimensional fuzzy dispersion entropy (MDFDE) for feature extraction. The pipeline acoustic signal is transformed and projected to generate the EF_Mel spectrogram. Subsequently, the features of the EF_Mel spectrogram are extracted by MDFDE. Verification of the proposed approach's effectiveness is conducted using numerical simulation and pipeline experimental bench. The results demonstrate that the proposed feature extraction is more robust in signal length, time delay, and noise rejection, achieving accuracies of 95.62 % and 96.30 % for small and large leakages, respectively, with a false negative rate (FNR) of 0 %. This paper offers a novel insight into signal feature extraction for pipeline LD.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"176 ","pages":"Article 106248"},"PeriodicalIF":9.6,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929430","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

Lunar base infrastructure construction: Challenges and future directions 月球基地基础设施建设：挑战与未来方向

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-08 DOI: 10.1016/j.autcon.2025.106251

Yifeng Xia , Yuyue Gao , Wenbin Han , Xinyi Li , Cheng Zhou , Yan Zhou , Lieyun Ding

引用次数: 0

Rebar grasp detection using a synthetic model generator and domain randomization 利用综合模型生成器和领域随机化技术进行钢筋抓握检测

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-08 DOI: 10.1016/j.autcon.2025.106252

Tao Sun , Beining Han , Szymon Rusinkiewicz , Yi Shao

{"title":"Rebar grasp detection using a synthetic model generator and domain randomization","authors":"Tao Sun , Beining Han , Szymon Rusinkiewicz , Yi Shao","doi":"10.1016/j.autcon.2025.106252","DOIUrl":"10.1016/j.autcon.2025.106252","url":null,"abstract":"<div><div>The increasing demand for automated rebar cage assembly in the construction industry highlights the need for flexible rebar grasping solutions. This paper proposes a grasp detection method that enables robotic arms to autonomously grasp rebars from the top layer of stacks, eliminating the need for complex delivery systems. To support this, a synthetic dataset pipeline incorporating domain randomization is developed, which facilitates robust rebar instance segmentation without the need for labor-intensive real-world data collection. Within this pipeline, a fully-parameterized rebar generator is proposed to eliminate the reliance on manual modeling in data generation, allowing an infinite generation of rebar datasets with realistic and diverse appearances and shapes. Real-world experiments demonstrated a segmentation accuracy of 87.9 for rebars in the top layer and a 91.6 % grasping success rate on the first attempt, validating the proposed methods. Additionally, an ablation study highlighted the significance of rebar stacking, lighting, and camera pose variations in improving the model performance in real-world scenarios.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"176 ","pages":"Article 106252"},"PeriodicalIF":9.6,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143923447","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 digital transformation for pedestrian suspension bridges using hybrid semantic structure from motion 基于运动混合语义结构的行人悬索桥自动数字化转换

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-08 DOI: 10.1016/j.autcon.2025.106232

Yeongseo Park , Jaehyuk Lee , Kevin Han , Hyungchul Yoon

{"title":"Automated digital transformation for pedestrian suspension bridges using hybrid semantic structure from motion","authors":"Yeongseo Park , Jaehyuk Lee , Kevin Han , Hyungchul Yoon","doi":"10.1016/j.autcon.2025.106232","DOIUrl":"10.1016/j.autcon.2025.106232","url":null,"abstract":"<div><div>Digital transformation is employed to create digital models that reflect the current state of infrastructure. Conventional semantic structure from motion methods effectively generated digital models of bridges segmented by components through semantic segmentation. However, these methods encounter significant challenges in the digital transformation of pedestrian suspension bridges: the inaccurate modeling of cables due to the inherent limitations of raster data in representing cables. To address these issues, this paper proposes a hybrid semantic structure from motion framework for pedestrian suspension bridges by integrating segmented cables in vector format with other raster format data. The performance of the proposed system was validated through full-scale experiments on a pedestrian suspension bridge in South Korea, achieving a 26.49% improvement in accuracy compared to conventional methods. By automating key aspects of digital transformation, the proposed framework is expected to provide solutions for the management of bridge infrastructure, enhancing safety and operational resilience.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"176 ","pages":"Article 106232"},"PeriodicalIF":9.6,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143923448","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

Transformer-based large vision model for universal structural damage segmentation 基于变压器的通用结构损伤分割大视觉模型

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-08 DOI: 10.1016/j.autcon.2025.106256

Yang Xu , Chuao Zhang , Hui Li

{"title":"Transformer-based large vision model for universal structural damage segmentation","authors":"Yang Xu , Chuao Zhang , Hui Li","doi":"10.1016/j.autcon.2025.106256","DOIUrl":"10.1016/j.autcon.2025.106256","url":null,"abstract":"<div><div>Current structural damage segmentation models are often trained based on substantial pixel-level labels for specific structural components and damage types. To address this issue, this paper establishes a transformer-based large vision model for universal structural damage segmentation, incorporating a pre-trained transformer-based frozen backbone and a fine-tuned CNN-based segmentation head. A synthetic loss function of correlation loss and contrastive loss is proposed. A self-supervised correlation learning procedure is designed to ensure cross-level feature alignment. The contrastive loss across student-teacher networks is designed to learn intra-instance similarity and inter-instance separability. A contrastive learning strategy is employed to fine-tune the segmentation head by exponential moving average with momentum updating. The proposed method is validated on a multi-scale image dataset for cable-supported bridges, concrete bridges, and post-earthquake buildings. The recognition accuracy, generalization ability, robustness under complex background, and superiority to conventional supervised and unsupervised segmentation models are demonstrated.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"176 ","pages":"Article 106256"},"PeriodicalIF":9.6,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143923446","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

Intelligent siltation diagnosis for drainage pipelines using weak-form analysis and theory-guided neural networks in geo-infrastructure 基于弱形式分析和理论指导神经网络的排水管道淤积智能诊断

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-08 DOI: 10.1016/j.autcon.2025.106246

Danyang Di , Yu Bai , Hongyuan Fang , Bin Sun , Niannian Wang , Bin Li

{"title":"Intelligent siltation diagnosis for drainage pipelines using weak-form analysis and theory-guided neural networks in geo-infrastructure","authors":"Danyang Di , Yu Bai , Hongyuan Fang , Bin Sun , Niannian Wang , Bin Li","doi":"10.1016/j.autcon.2025.106246","DOIUrl":"10.1016/j.autcon.2025.106246","url":null,"abstract":"<div><div>Siltation diagnosis of drainage pipelines is crucial for preventing urban flooding. However, the existing intelligent siltation diagnosis algorithms often exhibits limitations in handling multivariate data sequences and extracting multifaceted features, leading to partial distortion in outputs. To address these shortcomings, a neural network architecture consisting of inception network (BCI), residual network, multichannel long short-term memory network (MLSTM), and deep neural network (DNN) is constructed. It employs multichannel technique and bidirectional causal dilation convolution kernels with varying dilation factor steps to extract multiscale features. Weak-form analysis and theory-guided loss function error correction method are introduced to further enhance the accuracy of diagnosis. Then, a knowledge-algorithm collaborative driven model for pipeline siltation diagnosis is proposed. Its accuracy and robustness are verified by testing against typical prediction models with differing types of noise. Results underscore the method's potential for accurately detecting diverse municipal infrastructure defects, implying applicability in geo-infrastructure scenarios.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"176 ","pages":"Article 106246"},"PeriodicalIF":9.6,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143917239","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 PPE compliance monitoring in industrial environments using deep learning-based detection and pose estimation 在工业环境中使用基于深度学习的检测和姿态估计的自动化PPE合规性监控

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-08 DOI: 10.1016/j.autcon.2025.106231

Leopoldo López , Jonay Suárez-Ramírez , Miguel Alemán-Flores , Nelson Monzón

{"title":"Automated PPE compliance monitoring in industrial environments using deep learning-based detection and pose estimation","authors":"Leopoldo López , Jonay Suárez-Ramírez , Miguel Alemán-Flores , Nelson Monzón","doi":"10.1016/j.autcon.2025.106231","DOIUrl":"10.1016/j.autcon.2025.106231","url":null,"abstract":"<div><div>This paper presents an AI framework for automated detection of personal protective equipment (PPE) compliance in complex construction and industrial environments. Ensuring health and safety standards is essential for protecting workers engaged in construction, repair, or inspection activities. The framework leverages deep learning techniques for worker detection and pose estimation to enable accurate PPE identification under challenging conditions. The framework components are replaceable, and employ the InternImage-L detector for worker detection, ViTPose for pose estimation, and YOLOv7 for PPE recognition. A duplicate removal stage, combined with pose information, ensures PPE items are accurately assigned to individual workers. The approach addresses challenges like shadows, partial occlusions, or densely grouped workers. Evaluated on diverse datasets from real-world industrial settings, the framework achieves competitive precision and recall, particularly for critical PPE like helmets and vests, demonstrating robustness for safety monitoring and proactive risk management.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"176 ","pages":"Article 106231"},"PeriodicalIF":9.6,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143923445","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

Automating embodied and operational carbon assessment in urban sustainable development 城市可持续发展中隐含和可操作碳评估的自动化

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-05-07 DOI: 10.1016/j.autcon.2025.106245

Siavash Ghorbany , Ming Hu , Siyuan Yao , Matthew Sisk , Chaoli Wang

引用次数: 0