Automation in Construction最新文献_第7页

Local search-based online learning algorithm for shape and cross-section optimization of free-form single-layer reticulated shells 基于局部搜索的自由形式单层网壳形状和截面优化在线学习算法

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-27 DOI: 10.1016/j.autcon.2025.106144

Qiang Zeng , Makoto Ohsaki , Kazuki Hayashi , Shaojun Zhu , Xiaonong Guo

{"title":"Local search-based online learning algorithm for shape and cross-section optimization of free-form single-layer reticulated shells","authors":"Qiang Zeng , Makoto Ohsaki , Kazuki Hayashi , Shaojun Zhu , Xiaonong Guo","doi":"10.1016/j.autcon.2025.106144","DOIUrl":"10.1016/j.autcon.2025.106144","url":null,"abstract":"<div><div>Reasonable shape and cross-section design of free-form Single-Layer Reticulated Shells (SLRSs) are crucial for their superior static performance and material efficiency. However, traditional metaheuristics face high computational costs and are prone to converging to local optima when optimizing these factors simultaneously, often leading to necessity of carrying out decoupled design processes. This paper introduces a Local Search-based Online Learning Algorithm (LSOLA) for simultaneous shape and cross-section optimization of free-form SLRSs. LSOLA builds deep learning models in various sub-regions of the solution space and uses a hybrid query strategy to actively select promising samples, iteratively improving prediction accuracy near potentially optimal solutions for more efficient exploration. Numerical examples show that LSOLA delivers more diverse and superior solutions at lower computational costs compared to the existing global search-based online learning algorithms and metaheuristics. This paper also offers a reference for other optimization problems involving numerous variables and nonlinear constraints.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106144"},"PeriodicalIF":9.6,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706357","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-task deep reinforcement learning for dynamic scheduling of large-scale fleets in earthmoving operations 土方作业中大规模车队动态调度的多任务深度强化学习

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-27 DOI: 10.1016/j.autcon.2025.106123

Yunuo Zhang, Jun Zhang, Xiaoling Wang, Tuocheng Zeng

{"title":"Multi-task deep reinforcement learning for dynamic scheduling of large-scale fleets in earthmoving operations","authors":"Yunuo Zhang, Jun Zhang, Xiaoling Wang, Tuocheng Zeng","doi":"10.1016/j.autcon.2025.106123","DOIUrl":"10.1016/j.autcon.2025.106123","url":null,"abstract":"<div><div>Large-scale earthwork transportation encounters queuing congestion and dynamic uncertainties, while existing methods ignore complex traffic behaviors and exhibit limited responsiveness and generalization. This paper proposes a multi-task Deep Reinforcement Learning (DRL) framework for the dynamic scheduling of large fleets across supply sites and traffic networks. In the framework, multiple agents interact in complex environments modeled by discrete-event simulation, utilizing long short-term memory networks that consider queuing behaviors and dynamic trends of transportation systems to allocate rational materials, supply sites, and routes collaboratively, with an invariant update strategy to balance generalization and task-specific optimization during training. Experiments demonstrate that the model generates dynamic schedules within 7 min, reducing transportation time by 24 %. The trained agent can adapt to the changing transportation demand in complex construction environments and enhance transportation efficiency. This paper demonstrates the potential of DRL in scheduling more complex construction projects and promoting real-time lean control of modern logistics.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"174 ","pages":"Article 106123"},"PeriodicalIF":9.6,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706359","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

3D wireframe model reconstruction of buildings from multi-view images using neural implicit fields 基于神经隐式场的多视图建筑物三维线框模型重建

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-27 DOI: 10.1016/j.autcon.2025.106145

Weiwei Fan , Xinyi Liu , Yongjun Zhang , Dong Wei , Haoyu Guo , Dongdong Yue

引用次数: 0

Automated UAV image-to-BIM registration for planar and curved building façades using structure-from-motion and 3D surface unwrapping 利用 "从运动到结构 "和三维表面解包技术，实现平面和曲面建筑立面的无人机图像到 BIM 的自动注册

IF 9.6 1区工程技术

Automation in Construction Pub Date : 2025-03-26 DOI: 10.1016/j.autcon.2025.106148

Cheng Zhang , Yang Zou , Feng Wang , Johannes Dimyadi

引用次数: 0

Visual Question Answering-based Referring Expression Segmentation for construction safety analysis 基于视觉问答的建筑安全分析参考表达式分割

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 城市道路基础结构监测多尺度gan驱动GPR数据反演

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 生成式建筑设计的机器学习：进步、机遇和挑战

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 基于无人机的路面裂缝分割的跨域和跨分辨率弥合方法

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 不确定条件下基于b样条曲线和逐点增量策略的无人矿用电动铲开挖轨迹规划

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

Digital twin-enabled safety monitoring system for seamless worker-robot collaboration in construction 数字双体安全监控系统，实现施工中工人与机器人的无缝协作

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