Pub Date : 2023-03-01DOI: 10.1061/jccee5.cpeng-5077
Yanyu Wang, P. Tang, Kaijian Liu, Jiannan Cai, Ran Ren, Jacob J. Lin, Hubo Cai, Jiansong Zhang, N. El-Gohary, Mario Bergés, M. G. Fard
{"title":"Characterizing Data Sharing in Civil Infrastructure Engineering: Current Practice, Future Vision, Barriers, and Promotion Strategies","authors":"Yanyu Wang, P. Tang, Kaijian Liu, Jiannan Cai, Ran Ren, Jacob J. Lin, Hubo Cai, Jiansong Zhang, N. El-Gohary, Mario Bergés, M. G. Fard","doi":"10.1061/jccee5.cpeng-5077","DOIUrl":"https://doi.org/10.1061/jccee5.cpeng-5077","url":null,"abstract":"","PeriodicalId":50221,"journal":{"name":"Journal of Computing in Civil Engineering","volume":"209 1","pages":""},"PeriodicalIF":6.9,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77745556","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}
Visual inspection is one of the main approaches for annual bridge inspection. Light detection and ranging (LiDAR) scanning is a new technology, which is beneficial because it collects the point clouds and the third dimension of the scanned objects. Deep learning (DL)-based methods have attracted researchers’ attention for concrete surface defect detection. However, no point cloud–based DL method currently is available for semantic segmentation of bridge surface defects without converting the data set into other representations, which results in increasing the size of the data set. Moreover, most of the current point cloud–based concrete surface defect detection methods focus on only one type of defect. On the other hand, a data set plays a key role in DL. Therefore, the lack of publicly available point cloud data sets for bridge surface defects is one of the reasons for the lack of studies in this area. To address these issues, this paper created a publicly available point cloud data set for concrete bridge surface defect detection, and developed a point cloud–based semantic segmentation DL method to detect different types of concrete surface defects. Surface Normal Enhanced PointNet++ (SNEPointNet++) was developed for semantic segmentation of concrete bridge surface defects (i.e., cracks and spalls). SNEPointNet++ focuses on two main characteristics related to surface defects (i.e., normal vector and depth) and considers the issues related to the data set (i.e., imbalanced data set). The data set, which was collected from four concrete bridges and classified into three classes (cracks, spalls, and no defect), is made available for other researchers. The model was trained and evaluated using 60% and 20% of the data set, respectively. Testing on the remaining part of the data set resulted in 93% and 92% recall for cracks and spalls, respectively. Spalls of the segments deeper than 7 cm (severe spalls) can be detected with 99% recall.
{"title":"Point Cloud–Based Concrete Surface Defect Semantic Segmentation","authors":"Neshat Bolourian, Majid Nasrollahi, Fardin Bahreini, Amin Hammad","doi":"10.1061/jccee5.cpeng-5009","DOIUrl":"https://doi.org/10.1061/jccee5.cpeng-5009","url":null,"abstract":"Visual inspection is one of the main approaches for annual bridge inspection. Light detection and ranging (LiDAR) scanning is a new technology, which is beneficial because it collects the point clouds and the third dimension of the scanned objects. Deep learning (DL)-based methods have attracted researchers’ attention for concrete surface defect detection. However, no point cloud–based DL method currently is available for semantic segmentation of bridge surface defects without converting the data set into other representations, which results in increasing the size of the data set. Moreover, most of the current point cloud–based concrete surface defect detection methods focus on only one type of defect. On the other hand, a data set plays a key role in DL. Therefore, the lack of publicly available point cloud data sets for bridge surface defects is one of the reasons for the lack of studies in this area. To address these issues, this paper created a publicly available point cloud data set for concrete bridge surface defect detection, and developed a point cloud–based semantic segmentation DL method to detect different types of concrete surface defects. Surface Normal Enhanced PointNet++ (SNEPointNet++) was developed for semantic segmentation of concrete bridge surface defects (i.e., cracks and spalls). SNEPointNet++ focuses on two main characteristics related to surface defects (i.e., normal vector and depth) and considers the issues related to the data set (i.e., imbalanced data set). The data set, which was collected from four concrete bridges and classified into three classes (cracks, spalls, and no defect), is made available for other researchers. The model was trained and evaluated using 60% and 20% of the data set, respectively. Testing on the remaining part of the data set resulted in 93% and 92% recall for cracks and spalls, respectively. Spalls of the segments deeper than 7 cm (severe spalls) can be detected with 99% recall.","PeriodicalId":50221,"journal":{"name":"Journal of Computing in Civil Engineering","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135742656","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}
Pub Date : 2023-03-01DOI: 10.1061/jccee5.cpeng-5138
Yang Ye, Tianyu Zhou, Jing Du
{"title":"Robot-Assisted Immersive Kinematic Experience Transfer for Welding Training","authors":"Yang Ye, Tianyu Zhou, Jing Du","doi":"10.1061/jccee5.cpeng-5138","DOIUrl":"https://doi.org/10.1061/jccee5.cpeng-5138","url":null,"abstract":"","PeriodicalId":50221,"journal":{"name":"Journal of Computing in Civil Engineering","volume":"39 1","pages":""},"PeriodicalIF":6.9,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86174113","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}
Pub Date : 2023-03-01DOI: 10.1061/jccee5.cpeng-5065
Liu Yang, Hubo Cai
{"title":"Cost-Efficient Image Semantic Segmentation for Indoor Scene Understanding Using Weakly Supervised Learning and BIM","authors":"Liu Yang, Hubo Cai","doi":"10.1061/jccee5.cpeng-5065","DOIUrl":"https://doi.org/10.1061/jccee5.cpeng-5065","url":null,"abstract":"","PeriodicalId":50221,"journal":{"name":"Journal of Computing in Civil Engineering","volume":"13 1","pages":""},"PeriodicalIF":6.9,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75730543","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}
Pub Date : 2023-03-01DOI: 10.1061/jccee5.cpeng-5068
Navid Kayhani, Angela Schoellig, Brenda 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 work proposes a perception-aware genetic algorithm-based tag placement planner (PGA-TaPP) to determine the optimal tag configuration using four-dimensional (4D) building information models (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.
{"title":"Perception-Aware Tag Placement Planning for Robust Localization of UAVs in Indoor Construction Environments","authors":"Navid Kayhani, Angela Schoellig, Brenda McCabe","doi":"10.1061/jccee5.cpeng-5068","DOIUrl":"https://doi.org/10.1061/jccee5.cpeng-5068","url":null,"abstract":"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 work proposes a perception-aware genetic algorithm-based tag placement planner (PGA-TaPP) to determine the optimal tag configuration using four-dimensional (4D) building information models (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.","PeriodicalId":50221,"journal":{"name":"Journal of Computing in Civil Engineering","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135957216","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}
Pub Date : 2023-03-01DOI: 10.1061/(asce)cp.1943-5487.0001050
Ryan P. Longman, Yiye Xu, Qi Sun, Yelda Turkan, Mariapaola Riggio
A digital twin (DT) can be defined as a multiphysics, multiscale model in which a digital model, such as a building information model (BIM), is updated based on data obtained from a physical system, such as sensor data, results from probabilistic simulations, and material/structural models. This study describes sensor data integration within a BIM as the first critical step toward the implementation of DTs to support structural health monitoring (SHM). In particular, the study defines a methodological approach used to integrate the as-built geometry of existing buildings, as well as their material properties and sensor data into a digital model to assist in accessing sensor data to assess a building’s structural performance. A mass-timber structural system consisting of post-tensioned cross-laminated timber (CLT) self-centering shear walls at the George W. Peavy Forest Science Center (“Peavy Hall”) at Oregon State University was used as a case study to test the proposed approach. The BIM of the shear walls was developed using a Scan-to-BIM approach by converting light detection and ranging point clouds into a BIM. Sensors in the building recorded environmental and structural parameters influencing the long-term performance of the shear walls. Measurands included relative humidity, air and wood temperature, wood moisture content, displacements, and deformations of shear walls. The precise placement of these sensors and the possibility to associate the measured parameters of these entities within a BIM is hypothesized to assist with data management by adding a spatial element to data and analysis results. In addition, the integration into the IFC-BIM platform of a material- and phenomena-specific warning tool allows to promptly identify areas of concern in the monitored building. This can support facility managers in planning inspection and maintenance activities and eventually could lead to the prolonged service life of a building.
数字孪生(DT)可以定义为多物理场、多尺度模型，其中数字模型(如建筑信息模型(BIM))基于从物理系统获得的数据(如传感器数据、概率模拟结果和材料/结构模型)进行更新。本研究将BIM中的传感器数据集成描述为实施DTs以支持结构健康监测(SHM)的第一个关键步骤。特别是，该研究定义了一种方法方法，用于将现有建筑的建成几何形状、材料特性和传感器数据整合到数字模型中，以帮助访问传感器数据以评估建筑的结构性能。俄勒冈州立大学George W. Peavy森林科学中心(“Peavy大厅”)的一个由后张交叉层压木材(CLT)自中心剪力墙组成的大木结构系统被用作案例研究，以测试所提出的方法。剪力墙的BIM是通过将光探测和测距点云转换为BIM，使用扫描到BIM的方法开发的。建筑中的传感器记录了影响剪力墙长期性能的环境和结构参数。测量包括相对湿度、空气和木材温度、木材含水量、位移和剪力墙变形。假设这些传感器的精确放置以及将这些实体的测量参数关联到BIM中的可能性，可以通过向数据和分析结果添加空间元素来协助数据管理。此外，在IFC-BIM平台中集成了针对特定材料和现象的预警工具，可以迅速识别受监控建筑中的问题区域。这可以帮助设施管理人员规划检查和维护活动，并最终延长建筑物的使用寿命。
{"title":"Digital Twin for Monitoring In-Service Performance of Post-Tensioned Self-Centering Cross-Laminated Timber Shear Walls","authors":"Ryan P. Longman, Yiye Xu, Qi Sun, Yelda Turkan, Mariapaola Riggio","doi":"10.1061/(asce)cp.1943-5487.0001050","DOIUrl":"https://doi.org/10.1061/(asce)cp.1943-5487.0001050","url":null,"abstract":"A digital twin (DT) can be defined as a multiphysics, multiscale model in which a digital model, such as a building information model (BIM), is updated based on data obtained from a physical system, such as sensor data, results from probabilistic simulations, and material/structural models. This study describes sensor data integration within a BIM as the first critical step toward the implementation of DTs to support structural health monitoring (SHM). In particular, the study defines a methodological approach used to integrate the as-built geometry of existing buildings, as well as their material properties and sensor data into a digital model to assist in accessing sensor data to assess a building’s structural performance. A mass-timber structural system consisting of post-tensioned cross-laminated timber (CLT) self-centering shear walls at the George W. Peavy Forest Science Center (“Peavy Hall”) at Oregon State University was used as a case study to test the proposed approach. The BIM of the shear walls was developed using a Scan-to-BIM approach by converting light detection and ranging point clouds into a BIM. Sensors in the building recorded environmental and structural parameters influencing the long-term performance of the shear walls. Measurands included relative humidity, air and wood temperature, wood moisture content, displacements, and deformations of shear walls. The precise placement of these sensors and the possibility to associate the measured parameters of these entities within a BIM is hypothesized to assist with data management by adding a spatial element to data and analysis results. In addition, the integration into the IFC-BIM platform of a material- and phenomena-specific warning tool allows to promptly identify areas of concern in the monitored building. This can support facility managers in planning inspection and maintenance activities and eventually could lead to the prolonged service life of a building.","PeriodicalId":50221,"journal":{"name":"Journal of Computing in Civil Engineering","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135693612","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}
Pub Date : 2023-03-01DOI: 10.1061/jccee5.cpeng-5041
Varun Kasireddy, B. Akinci
{"title":"Encoding 3D Point Contexts for Self-Supervised Spall Classification Using 3D Bridge Point Clouds","authors":"Varun Kasireddy, B. Akinci","doi":"10.1061/jccee5.cpeng-5041","DOIUrl":"https://doi.org/10.1061/jccee5.cpeng-5041","url":null,"abstract":"","PeriodicalId":50221,"journal":{"name":"Journal of Computing in Civil Engineering","volume":"56 1","pages":""},"PeriodicalIF":6.9,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82170374","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}
Pub Date : 2023-03-01DOI: 10.1061/(asce)cp.1943-5487.0001063
Hang Li, Jiansong Zhang
{"title":"Improving IFC-Based Interoperability between BIM and BEM Using Invariant Signatures of HVAC Objects","authors":"Hang Li, Jiansong Zhang","doi":"10.1061/(asce)cp.1943-5487.0001063","DOIUrl":"https://doi.org/10.1061/(asce)cp.1943-5487.0001063","url":null,"abstract":"","PeriodicalId":50221,"journal":{"name":"Journal of Computing in Civil Engineering","volume":"247 1","pages":""},"PeriodicalIF":6.9,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73065028","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}
Pub Date : 2023-01-26eCollection Date: 2023-01-01DOI: 10.1017/awf.2022.6
Elizabeth S Paul, Rowena Ma Packer, Paul D McGreevy, Emily Coombe, Elsa Mendl, Vikki Neville
Brachycephalic dog breeds are highly popular, yet their conformation-related disorders represent a major welfare concern. It has been suggested that the current popularity of such breeds can be explained by their cute, infant-like facial appearances. The concept of 'kindchenschema' refers to the observation that certain physical features of infant humans and other animals can automatically stimulate positive and nurturant feelings in adult observers. But the proposal that brachycephalic dogs possess heightened 'kindchenschema' facial features, even into adulthood, has never been formally investigated. Here, we hypothesised that relative muzzle shortening across a range of breeds would be associated with known 'kindchenschema' facial features, including a relatively larger forehead, larger eyes and smaller nose. Relative fronto-facial feature sizes in exemplar photographs of adult dogs from 42 popular breeds were measured and associated with existing data on the relative muzzle length and height-at-withers of the same breeds. Our results show that, in adulthood, shorter-muzzled breeds have relatively larger (taller) foreheads and relatively larger eyes (i.e. area of exposed eyeball relative to overall face area) than longer-muzzled breeds, and that this effect is independent of breed size. In sum, brachycephalic dog breeds do show exaggeration of some, but not all, known fronto-facial 'kindchenschema' features, and this may well contribute to their apparently cute appearance and to their current popularity as companion animals. We conclude that the challenge of addressing conformation-related disorders in companion dogs needs to take account of the cute, 'kindchenschema' looks that many owners are likely to be attracted to.
{"title":"That brachycephalic look: Infant-like facial appearance in short-muzzled dog breeds.","authors":"Elizabeth S Paul, Rowena Ma Packer, Paul D McGreevy, Emily Coombe, Elsa Mendl, Vikki Neville","doi":"10.1017/awf.2022.6","DOIUrl":"10.1017/awf.2022.6","url":null,"abstract":"<p><p>Brachycephalic dog breeds are highly popular, yet their conformation-related disorders represent a major welfare concern. It has been suggested that the current popularity of such breeds can be explained by their cute, infant-like facial appearances. The concept of 'kindchenschema' refers to the observation that certain physical features of infant humans and other animals can automatically stimulate positive and nurturant feelings in adult observers. But the proposal that brachycephalic dogs possess heightened 'kindchenschema' facial features, even into adulthood, has never been formally investigated. Here, we hypothesised that relative muzzle shortening across a range of breeds would be associated with known 'kindchenschema' facial features, including a relatively larger forehead, larger eyes and smaller nose. Relative fronto-facial feature sizes in exemplar photographs of adult dogs from 42 popular breeds were measured and associated with existing data on the relative muzzle length and height-at-withers of the same breeds. Our results show that, in adulthood, shorter-muzzled breeds have relatively larger (taller) foreheads and relatively larger eyes (i.e. area of exposed eyeball relative to overall face area) than longer-muzzled breeds, and that this effect is independent of breed size. In sum, brachycephalic dog breeds do show exaggeration of some, but not all, known fronto-facial 'kindchenschema' features, and this may well contribute to their apparently cute appearance and to their current popularity as companion animals. We conclude that the challenge of addressing conformation-related disorders in companion dogs needs to take account of the cute, 'kindchenschema' looks that many owners are likely to be attracted to.</p>","PeriodicalId":50221,"journal":{"name":"Journal of Computing in Civil Engineering","volume":"8 1","pages":"e5"},"PeriodicalIF":1.2,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10936394/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80321690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1061/(asce)cp.1943-5487.0001052
A. Ojha, Yizhi Liu, Shayan Shayesteh, Houtan Jebelli, William Sitzabee
{"title":"Affordable Multiagent Robotic System for Same-Level Fall Hazard Detection in Indoor Construction Environments","authors":"A. Ojha, Yizhi Liu, Shayan Shayesteh, Houtan Jebelli, William Sitzabee","doi":"10.1061/(asce)cp.1943-5487.0001052","DOIUrl":"https://doi.org/10.1061/(asce)cp.1943-5487.0001052","url":null,"abstract":"","PeriodicalId":50221,"journal":{"name":"Journal of Computing in Civil Engineering","volume":"3 1","pages":""},"PeriodicalIF":6.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89504569","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}
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