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Journal of Computing in Civil Engineering最新文献

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Improved Bat Algorithm Based on Doppler Effect for Optimal Design of Special Truss Structures 基于多普勒效应的改进Bat算法在特殊桁架结构优化设计中的应用
IF 6.9 2区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2022-11-01 DOI: 10.1061/(asce)cp.1943-5487.0001042
A. Kaveh, Seyed Milad Hosseini
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引用次数: 8
Deep Learning-Based Building Attribute Estimation from Google Street View Images for Flood Risk Assessment Using Feature Fusion and Task Relation Encoding 基于特征融合和任务关系编码的基于深度学习的Google街景图像洪水风险评估建筑物属性估计
IF 6.9 2区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2022-11-01 DOI: 10.1061/(asce)cp.1943-5487.0001025
Fu-Chen Chen, Abhishek Subedi, M. Jahanshahi, David R. Johnson, E. Delp
{"title":"Deep Learning-Based Building Attribute Estimation from Google Street View Images for Flood Risk Assessment Using Feature Fusion and Task Relation Encoding","authors":"Fu-Chen Chen, Abhishek Subedi, M. Jahanshahi, David R. Johnson, E. Delp","doi":"10.1061/(asce)cp.1943-5487.0001025","DOIUrl":"https://doi.org/10.1061/(asce)cp.1943-5487.0001025","url":null,"abstract":"","PeriodicalId":50221,"journal":{"name":"Journal of Computing in Civil Engineering","volume":"183 1","pages":""},"PeriodicalIF":6.9,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77176874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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: 4
Machine Learning Control for Floating Offshore Wind Turbine Individual Blade Pitch Control 浮式海上风力发电机桨距控制的机器学习控制
IF 6.9 2区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2022-11-01 DOI: 10.1061/(asce)cp.1943-5487.0001043
James Velino, Sungku Kang, Michael B. Kane
{"title":"Machine Learning Control for Floating Offshore Wind Turbine Individual Blade Pitch Control","authors":"James Velino, Sungku Kang, Michael B. Kane","doi":"10.1061/(asce)cp.1943-5487.0001043","DOIUrl":"https://doi.org/10.1061/(asce)cp.1943-5487.0001043","url":null,"abstract":"","PeriodicalId":50221,"journal":{"name":"Journal of Computing in Civil Engineering","volume":"16 1","pages":""},"PeriodicalIF":6.9,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91297148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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: 1
Analyzing Safety Risk Imposed by Jobsite Debris to Nearby Built Environments Using Geometric Digital Twins and Vision-Based Deep Learning 利用几何数字孪生和基于视觉的深度学习分析工地碎片对附近建筑环境造成的安全风险
IF 6.9 2区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2022-11-01 DOI: 10.1061/(asce)cp.1943-5487.0001044
M. Kamari, Jaeyoon Kim, Youngjib Ham
{"title":"Analyzing Safety Risk Imposed by Jobsite Debris to Nearby Built Environments Using Geometric Digital Twins and Vision-Based Deep Learning","authors":"M. Kamari, Jaeyoon Kim, Youngjib Ham","doi":"10.1061/(asce)cp.1943-5487.0001044","DOIUrl":"https://doi.org/10.1061/(asce)cp.1943-5487.0001044","url":null,"abstract":"","PeriodicalId":50221,"journal":{"name":"Journal of Computing in Civil Engineering","volume":"26 1","pages":""},"PeriodicalIF":6.9,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88887728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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: 3
Network-Level Guardrail Extraction Based on 3D Local Features from Mobile LiDAR Sensor 基于移动LiDAR传感器三维局部特征的网络级护栏提取
IF 6.9 2区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2022-11-01 DOI: 10.1061/(asce)cp.1943-5487.0001049
Qing Hou, Chengbo Ai, Neil Boudreau
{"title":"Network-Level Guardrail Extraction Based on 3D Local Features from Mobile LiDAR Sensor","authors":"Qing Hou, Chengbo Ai, Neil Boudreau","doi":"10.1061/(asce)cp.1943-5487.0001049","DOIUrl":"https://doi.org/10.1061/(asce)cp.1943-5487.0001049","url":null,"abstract":"","PeriodicalId":50221,"journal":{"name":"Journal of Computing in Civil Engineering","volume":"45 1","pages":""},"PeriodicalIF":6.9,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74945341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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: 1
Perception-aware Tag Placement Planning for Robust Localization of UAVs in Indoor Construction Environments 基于感知的室内建筑环境下无人机鲁棒定位标签放置规划
IF 6.9 2区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2022-10-27 DOI: 10.1061/JCCEE5/CPENG-5068
Navid Kayhani, Angela P. Schoellig, B. McCabe
Tag-based visual-inertial localization is a lightweight method for enabling autonomous data collection missions of low-cost unmanned aerial vehicles (UAVs) in indoor construction environments. However, finding the optimal tag configuration (i.e., number, size, and location) on dynamic construction sites remains challenging. This paper proposes a perception-aware genetic algorithm-based tag placement planner (PGA-TaPP) to determine the optimal tag configuration using 4D-BIM, considering the project progress, safety requirements, and UAV's localizability. The proposed method provides a 4D plan for tag placement by maximizing the localizability in user-specified regions of interest (ROIs) while limiting the installation costs. Localizability is quantified using the Fisher information matrix (FIM) and encapsulated in navigable grids. The experimental results show the effectiveness of our method in finding an optimal 4D tag placement plan for the robust localization of UAVs on under-construction indoor sites.
基于标签的视觉惯性定位是实现低成本无人机在室内建筑环境中自主数据采集任务的一种轻量级方法。然而,在动态施工现场找到最佳的标签配置(即数量、大小和位置)仍然具有挑战性。本文提出了一种基于感知遗传算法的标签放置规划器(PGA-TaPP),结合项目进度、安全要求和无人机的可定位性,利用4D-BIM确定最优标签配置。该方法通过最大化用户指定的兴趣区域(roi)的可定位性,同时限制安装成本,为标签放置提供了4D计划。利用Fisher信息矩阵(FIM)量化定位能力,并将其封装在可导航网格中。实验结果表明,该方法能够有效地找到最优的4D标签放置方案,用于无人机在施工室内场地的鲁棒定位。
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引用次数: 2
Hierarchical Representation and Deep Learning-Based Method for Automatically Transforming Textual Building Codes into Semantic Computable Requirements 基于层次表示和深度学习的文本建筑规范自动转换为语义可计算需求的方法
IF 6.9 2区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2022-09-01 DOI: 10.1061/(asce)cp.1943-5487.0001014
Ruichuan Zhang, N. El-Gohary
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引用次数: 6
Deep Learning-Based Analytics of Multisource Heterogeneous Bridge Data for Enhanced Data-Driven Bridge Deterioration Prediction 基于深度学习的多源异构桥梁数据分析,增强数据驱动桥梁劣化预测
IF 6.9 2区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2022-09-01 DOI: 10.1061/(asce)cp.1943-5487.0001018
Kaijian Liu, N. El-Gohary
{"title":"Deep Learning-Based Analytics of Multisource Heterogeneous Bridge Data for Enhanced Data-Driven Bridge Deterioration Prediction","authors":"Kaijian Liu, N. El-Gohary","doi":"10.1061/(asce)cp.1943-5487.0001018","DOIUrl":"https://doi.org/10.1061/(asce)cp.1943-5487.0001018","url":null,"abstract":"","PeriodicalId":50221,"journal":{"name":"Journal of Computing in Civil Engineering","volume":"46 1","pages":""},"PeriodicalIF":6.9,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78153490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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: 3
Multiobjective Optimization of Reality Capture Plans for Computer Vision-Driven Construction Monitoring with Camera-Equipped UAVs 计算机视觉驱动的无人机施工监控实景捕捉方案多目标优化
IF 6.9 2区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2022-09-01 DOI: 10.1061/(asce)cp.1943-5487.0001032
A. Ibrahim, M. G. Fard, K. El-Rayes
{"title":"Multiobjective Optimization of Reality Capture Plans for Computer Vision-Driven Construction Monitoring with Camera-Equipped UAVs","authors":"A. Ibrahim, M. G. Fard, K. El-Rayes","doi":"10.1061/(asce)cp.1943-5487.0001032","DOIUrl":"https://doi.org/10.1061/(asce)cp.1943-5487.0001032","url":null,"abstract":"","PeriodicalId":50221,"journal":{"name":"Journal of Computing in Civil Engineering","volume":"19 1","pages":""},"PeriodicalIF":6.9,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73208869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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: 7
AdaLN: A Vision Transformer for Multidomain Learning and Predisaster Building Information Extraction from Images AdaLN:一种多领域学习和灾前建筑信息提取的视觉转换器
IF 6.9 2区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2022-09-01 DOI: 10.1061/(asce)cp.1943-5487.0001034
Yunhui Guo, Chaofeng Wang, Stella X. Yu, F. McKenna, K. Law
: Satellite and street view images are widely used in various disciplines as a source of information for understanding the built environment. In natural hazard engineering, high-quality building inventory data sets are crucial for the simulation of hazard impacts and for supporting decision-making. Screening the building stocks to gather the information for simulation and to detect potential structural defects that are vulnerable to natural hazards is a time-consuming and labor-intensive task. This paper presents an automated method for extracting building information through the use of satellite and street view images. The method is built upon a novel transformer-based deep neural network we developed. Specifically, a multidomain learning approach is employed to develop a single compact model for multiple image-based deep learning information extraction tasks using multiple data sources (e.g., satellite and street view images). Our multidomain Vision Transformer is designed as a unified architecture that can be effectively deployed for multiple classification tasks. The effectiveness of the proposed approach is demonstrated in a case study in which we use pretrained models to collect regional-scale building information that is related to natural hazard risks. DOI: 10.1061/(ASCE)CP.1943-5487.0001034. © 2022 American Society of Civil Engineers.
:卫星和街景图像被广泛应用于各个学科,作为了解建筑环境的信息来源。在自然灾害工程中,高质量的建筑清单数据集对于模拟灾害影响和支持决策至关重要。筛选建筑物库存以收集模拟信息并检测易受自然灾害影响的潜在结构缺陷是一项耗时且劳动密集型的任务。本文提出了一种利用卫星和街景图像自动提取建筑物信息的方法。该方法是建立在我们开发的一种新颖的基于变压器的深度神经网络之上的。具体来说,采用多领域学习方法开发了一个单一的紧凑模型,用于使用多个数据源(例如卫星和街景图像)的多个基于图像的深度学习信息提取任务。我们的多域视觉转换器被设计成一个统一的体系结构,可以有效地部署在多个分类任务中。在一个案例研究中,我们使用预训练模型来收集与自然灾害风险相关的区域尺度建筑信息,证明了所提出方法的有效性。DOI: 10.1061 /(第3期)cp.1943 - 5487.0001034。©2022美国土木工程师学会。
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引用次数: 5
期刊
Journal of Computing in Civil Engineering
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