Automation in Construction最新文献_第3页

Visual Question Answering-based Referring Expression Segmentation for construction safety analysis

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-26 DOI: 10.1016/j.autcon.2025.106127

Dai Quoc Tran , Armstrong Aboah , Yuntae Jeon , Minh-Truyen Do , Mohamed Abdel-Aty , Minsoo Park , Seunghee Park

{"title":"Visual Question Answering-based Referring Expression Segmentation for construction safety analysis","authors":"Dai Quoc Tran , Armstrong Aboah , Yuntae Jeon , Minh-Truyen Do , Mohamed Abdel-Aty , Minsoo Park , Seunghee Park","doi":"10.1016/j.autcon.2025.106127","DOIUrl":"10.1016/j.autcon.2025.106127","url":null,"abstract":"<div><div>Despite advancements in computer vision techniques like object detection and segmentation, a significant gap remains in leveraging these technologies for hazard recognition through natural language processing. To address this gap, this paper proposes VQA-RESCon, an approach that combines Visual Question Answering (VQA) and Referring Expression Segmentation (RES) to enhance construction safety analysis. By leveraging the visual grounding capabilities of RES, our method not only identifies potential hazards through VQA but also precisely localizes and highlights these hazards within the image. The method utilizes a large “scenario-questions” dataset comprising 200,000 images and 16 targeted questions to train a vision-and-language transformer model. In addition, post-processing techniques were employed using the ClipSeg and Segment Anything Model. The validation results indicate that both the VQA and RES models demonstrate notable reliability and precision. The VQA model achieves an F1 score surpassing 90%, while the segmentation models achieve a Mean Intersection over Union of 57%.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106127"},"PeriodicalIF":9.6,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704016","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-scale GAN-driven GPR data inversion for monitoring urban road substructure

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-26 DOI: 10.1016/j.autcon.2025.106140

Feifei Hou , Xingyu Qian , Qiwen Meng , Jian Dong , Fei Lyu

{"title":"Multi-scale GAN-driven GPR data inversion for monitoring urban road substructure","authors":"Feifei Hou , Xingyu Qian , Qiwen Meng , Jian Dong , Fei Lyu","doi":"10.1016/j.autcon.2025.106140","DOIUrl":"10.1016/j.autcon.2025.106140","url":null,"abstract":"<div><div>Accurate monitoring and visualization of urban road substructure and targets are impeded by challenges in inverting Ground Penetrating Radar (GPR) data, especially under multiple inversion objectives and complex road conditions. To address this challenge, a deep learning-based multi-scale inversion approach, termed MSInv-GPR, is proposed, which builds on the Pix2pix Generative Adversarial Network (Pix2pixGAN) framework. This approach introduces dual-channel inputs to improve inversion accuracy, integrates a multi-scale convolution module along with an Efficient Multi-scale Attention (EMA) module to better capture characteristic waveforms, and incorporates a loss function strategy to strengthen adversarial training and accelerate convergence. Ablation studies validate that MSInv-GPR achieves Structural Similarity Index (SSIM) of 99.75 %, Peak Signal-to-Noise Ratio (PSNR) of 47.9014, and Mean Squared Error (MSE) of 12.5825 for 8-bit images, with 51.69 % improvement in Power Supply Modulation Ratio (PSMR) and an increase in discriminator loss from 0.1132 to 1.1603 compared to a baseline.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106140"},"PeriodicalIF":9.6,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704017","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

Machine learning for generative architectural design: Advancements, opportunities, and challenges

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-26 DOI: 10.1016/j.autcon.2025.106129

Xinwei Zhuang , Pinru Zhu , Allen Yang , Luisa Caldas

引用次数: 0

Bridging cross-domain and cross-resolution gaps for UAV-based pavement crack segmentation

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-26 DOI: 10.1016/j.autcon.2025.106141

Jinhuan Shan , Wei Jiang , Xiao Feng

{"title":"Bridging cross-domain and cross-resolution gaps for UAV-based pavement crack segmentation","authors":"Jinhuan Shan , Wei Jiang , Xiao Feng","doi":"10.1016/j.autcon.2025.106141","DOIUrl":"10.1016/j.autcon.2025.106141","url":null,"abstract":"<div><div>The acquisition of pavement distress images using UAVs presents unique challenges compared to ground-based methods due to differences in camera configurations, flight parameters, and lighting conditions. These factors introduce domain shifts that undermine the generalizability of segmentation models. To address these limitations, an interactive segmentation model, CDCR-ISeg, is proposed to bridge the gap between industrial requirements and existing methodologies. A dedicated dataset comprising 1500 pixel-wise annotated UAV images (UAV-CrackX4, X8, X16) was constructed, capturing various zoom levels and domain conditions to support the model's development. CDCR-ISeg incorporates super-resolution and domain adaptation techniques to enhance model generalization while reducing annotation efforts. Additionally, a vector map is introduced to improve boundary detection by embedding positive and negative clicks with reversed vector map directions. This approach effectively enables high-precision detection of pavement distress under diverse UAV parameter settings, addressing the critical challenges of adaptability and scalability in UAV-based pavement inspection.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106141"},"PeriodicalIF":9.6,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706358","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

Excavation trajectory planning for unmanned mining electric shovel using B-spline curves and point-by-point incremental strategy under uncertainty

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-25 DOI: 10.1016/j.autcon.2025.106135

Zhengguo Hu , Shibin Lin , Xiuhua Long , Yong Pang , Xiwang He , Xueguan Song

{"title":"Excavation trajectory planning for unmanned mining electric shovel using B-spline curves and point-by-point incremental strategy under uncertainty","authors":"Zhengguo Hu , Shibin Lin , Xiuhua Long , Yong Pang , Xiwang He , Xueguan Song","doi":"10.1016/j.autcon.2025.106135","DOIUrl":"10.1016/j.autcon.2025.106135","url":null,"abstract":"<div><div>The intelligence of electric shovels plays a critical role in improving excavation efficiency and safety. A key challenge in intelligent excavation is generating an optimal excavation trajectory while considering material uncertainty. Therefore, an Unmanned mining Electric Shovel Trajectory Planning method based on the Point-by-point Incremental B-spline Curve under Uncertainty (UESTP-PIBCU) is proposed in this paper. The method establishes the dynamic model of the electric shovel working mechanism and the excavation resistance model, analyzes excavation resistance uncertainty parameters using interval possibility theory. Then, a multi-objective trajectory planning model considering excavation resistance uncertainty is established, and the optimal excavation trajectory is obtained through optimization. Experimental results demonstrate that UESTP-PIBCU outperforms commonly used methods in excavation efficiency and dipper fill ratio, and operational efficiency is effectively improved. Future research will explore the impact of multi-source uncertainties on excavation trajectories, to enhance the reliability and robustness of the intelligent electric shovel system.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106135"},"PeriodicalIF":9.6,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697065","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

Modeling heterogeneous spatiotemporal pavement data for condition prediction and preventive maintenance in digital twin-enabled highway management

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-25 DOI: 10.1016/j.autcon.2025.106134

Linjun Lu, Alix Marie d'Avigneau, Yuandong Pan, Zhaojie Sun, Peihang Luo, Ioannis Brilakis

{"title":"Modeling heterogeneous spatiotemporal pavement data for condition prediction and preventive maintenance in digital twin-enabled highway management","authors":"Linjun Lu, Alix Marie d'Avigneau, Yuandong Pan, Zhaojie Sun, Peihang Luo, Ioannis Brilakis","doi":"10.1016/j.autcon.2025.106134","DOIUrl":"10.1016/j.autcon.2025.106134","url":null,"abstract":"<div><div>Pavement preventive maintenance is one of the most fundamental use cases when deploying digital twins (DTs) for highway infrastructure management. To achieve this, it is essential to accurately predict the pavement conditions in future years. This paper developed a Spatial-Temporal Graph Attention network (STGAT) that can effectively capitalize on both spatial and temporal dependencies while addressing inherent heterogeneity in pavement data for more accurate condition predictions. On top of this, a structured assessment procedure was introduced to determine the need for preventive maintenance on road sections based on the STGAT predictions. A case study on the highway network in the United Kingdom was conducted to evaluate the method's performance. The results showed that the proposed method can achieve superior accuracy for pavement condition prediction and subsequent preventive maintenance assessment compared to existing methods, thus signifying its potential to improve the effectiveness of DTs for highway infrastructure management.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106134"},"PeriodicalIF":9.6,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Digital twin-enabled safety monitoring system for seamless worker-robot collaboration in construction

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-25 DOI: 10.1016/j.autcon.2025.106147

Xiao Lin, Ziyang Guo, Xinxiang Jin, Hongling Guo

引用次数: 0

Embedded machine vision sensor with portable imaging device and high durability

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-24 DOI: 10.1016/j.autcon.2025.106143

Pengfei Wu , Han Yuan , Bingchuan Bai , Bo Lu , Weijie Li , Xuefeng Zhao

引用次数: 0

Safety-constrained Deep Reinforcement Learning control for human–robot collaboration in construction

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-23 DOI: 10.1016/j.autcon.2025.106130

Kangkang Duan , Zhengbo Zou

引用次数: 0

Thickness optimisation in 3D printed concrete structures

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-23 DOI: 10.1016/j.autcon.2025.106076

Romain Mesnil, Pedro Sarkis Rosa, Léo Demont

引用次数: 0