Dominic Roberts, Mingzhu Wang, W. T. Calderon, M. Golparvar-Fard
{"title":"An Annotation Tool for Benchmarking Methods for Automated Construction Worker Pose Estimation and Activity Analysis","authors":"Dominic Roberts, Mingzhu Wang, W. T. Calderon, M. Golparvar-Fard","doi":"10.1680/ICSIC.64669.307","DOIUrl":"https://doi.org/10.1680/ICSIC.64669.307","url":null,"abstract":"","PeriodicalId":205150,"journal":{"name":"International Conference on Smart Infrastructure and Construction 2019 (ICSIC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124090764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This work is being funded by the Lloyd’s Register Foundation, EPSRC and Innovate UK through the Data-Centric Engineering programme of the Alan Turing Institute and through the Cambridge Centre for Smart Infrastructure and Construction. Funding for the monitoring installation was provided by EPSRC under the Ref. EP/N021614/1 grant and by Innovate UK under the Ref. 920035 grant.
{"title":"Monitoring Bridge Degradation Using Dynamic Strain, Acoustic Emission and Environmental Data","authors":"Haris Alexakis, A. Franza, S. Acikgoz, M. DeJong","doi":"10.17863/CAM.38694","DOIUrl":"https://doi.org/10.17863/CAM.38694","url":null,"abstract":"This work is being funded by the Lloyd’s Register Foundation, EPSRC and Innovate UK through the Data-Centric Engineering programme of the Alan Turing Institute and through the Cambridge Centre for Smart Infrastructure and Construction. Funding for the monitoring installation was provided by EPSRC under the Ref. EP/N021614/1 grant and by Innovate UK under the Ref. 920035 grant.","PeriodicalId":205150,"journal":{"name":"International Conference on Smart Infrastructure and Construction 2019 (ICSIC)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132519121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Failure of bolted connections in steel structures may result in catastrophic effects. Many algorithms in existing literature use modal information of a structure to identify damage in that structure, based on the data acquired from accelerometers which record the vibration time histories at different points on the structure. The location of these points may have significant effects on the quality of the acquired data, and thus the identified modal information. In this paper, a distance measure based Markov chain Monte Carlo algorithm is proposed to determine the optimal locations for the accelerometers, and the optimal location of the impact hammer if need. Different damage cases with various combinations of bolt failures are considered in this study. Failures at various levels are simulated by loosening the bolts in a predefined order. To compare the efficiency of the proposed method, the total effect of various damage cases on the accelerations at the optimal locations are calculated for the proposed method and a state-of-the-art method from the existing literature. The results demonstrate the efficiency of the proposed strategy in locating the accelerometers, which can produce data that are more sensitive to the bolted connection failures.
{"title":"Optimal Sensor Placement Strategy for the Identification of Local Bolted Connection Failures in Steel Structures","authors":"S. Biswal, Ying Wang","doi":"10.1680/ICSIC.64669.685","DOIUrl":"https://doi.org/10.1680/ICSIC.64669.685","url":null,"abstract":"Failure of bolted connections in steel structures may result in catastrophic effects. Many algorithms in existing literature use \u0000modal information of a structure to identify damage in that structure, based on the data acquired from accelerometers which record the vibration \u0000time histories at different points on the structure. The location of these points may have significant effects on the quality of the acquired data, \u0000and thus the identified modal information. In this paper, a distance measure based Markov chain Monte Carlo algorithm is proposed to \u0000determine the optimal locations for the accelerometers, and the optimal location of the impact hammer if need. Different damage cases with \u0000various combinations of bolt failures are considered in this study. Failures at various levels are simulated by loosening the bolts in a predefined \u0000order. To compare the efficiency of the proposed method, the total effect of various damage cases on the accelerations at the optimal locations \u0000are calculated for the proposed method and a state-of-the-art method from the existing literature. The results demonstrate the efficiency of the \u0000proposed strategy in locating the accelerometers, which can produce data that are more sensitive to the bolted connection failures.","PeriodicalId":205150,"journal":{"name":"International Conference on Smart Infrastructure and Construction 2019 (ICSIC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130913219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Integration of Regional and Asset Satellite Observations for Assessment of Infrastructure Resilience","authors":"K. Haria, M. Faragò, T. Dawood, M. Bush","doi":"10.1680/ICSIC.64669.029","DOIUrl":"https://doi.org/10.1680/ICSIC.64669.029","url":null,"abstract":"","PeriodicalId":205150,"journal":{"name":"International Conference on Smart Infrastructure and Construction 2019 (ICSIC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116677317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
O. Aslan, E. Gultepe, Issa J. Ramaji, Sharareh Kermanshachi
The financial burden due to pavement damage on road networks is a major handicap to the economic development of a country. According to an ASCE report, this issue may cost as much as $67 billion per year. Regularly planned condition assessments and repairs of pavement can mitigate any derived costs and increase traffic safety. However, due to the large extent of civil infrastructure networks, required periodic inspections and assessments can be expensive and time-consuming. Further compounding the issue is that the majority of damage assessment mechanisms rely on human visual analysis, which can be prone to potential user bias and errors. In this study, we present a framework to automate roadway assessment by implementing a Convolutional Neural Network (CNN) that classifies various types of cracks in pavements. CNNs are a special type of deep artificial neural networks that demonstrate high accuracy and efficiency in image-based machine learning tasks. One of the main advantages of CNNs is that they can automatically learn the salient features of an image dataset without any prior knowledge or pre-processing by the user. Thus, the need for feature engineering is obviated and thereby eases the deployment of our assessment framework. Our framework was developed and tested on a balanced dataset containing 400 color images and consisting of four types of pavement damage: (1) longitudinal, (2) transverse, (3) alligator, and (4) pothole cracks. We apply image augmentation using a bundle of transformations to improve the crack classification accuracy of our CNN. The classification accuracy of the four types of cracks was found to be 76.2%. Demonstrating that the proposed CNN model can predict crack types without any user intervention at a good level of accuracy. To improve the robustness and accuracy of our assessment framework, we will analyze more types of cracks, using a larger dataset size in future studies.
{"title":"Using Artifical Intelligence for Automating Pavement Condition Assessment","authors":"O. Aslan, E. Gultepe, Issa J. Ramaji, Sharareh Kermanshachi","doi":"10.1680/ICSIC.64669.337","DOIUrl":"https://doi.org/10.1680/ICSIC.64669.337","url":null,"abstract":"The financial burden due to pavement damage on road networks is a major handicap to the economic development of a country. According to an ASCE report, this issue may cost as much as $67 billion per year. Regularly planned condition assessments and repairs of pavement can mitigate any derived costs and increase traffic safety. However, due to the large extent of civil infrastructure networks, required periodic inspections and assessments can be expensive and time-consuming. Further compounding the issue is that the majority of damage assessment mechanisms rely on human visual analysis, which can be prone to potential user bias and errors. In this study, we present a framework to automate roadway assessment by implementing a Convolutional Neural Network (CNN) that classifies various types of cracks in pavements. CNNs are a special type of deep artificial neural networks that demonstrate high accuracy and efficiency in image-based machine learning tasks. One of the main advantages of CNNs is that they can automatically learn the salient features of an image dataset without any prior knowledge or pre-processing by the user. Thus, the need for feature engineering is obviated and thereby eases the deployment of our assessment framework. Our framework was developed and tested on a balanced dataset containing 400 color images and consisting of four types of pavement damage: (1) longitudinal, (2) transverse, (3) alligator, and (4) pothole cracks. We apply image augmentation using a bundle of transformations to improve the crack classification accuracy of our CNN. The classification accuracy of the four types of cracks was found to be 76.2%. Demonstrating that the proposed CNN model can predict crack types without any user intervention at a good level of accuracy. To improve the robustness and accuracy of our assessment framework, we will analyze more types of cracks, using a larger dataset size in future studies.","PeriodicalId":205150,"journal":{"name":"International Conference on Smart Infrastructure and Construction 2019 (ICSIC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127652698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Infrastructure Readiness for the Anticipated Transformative Changes in Transportation","authors":"Y. Huang, S. Jiang, M. Jafari, P. Jin","doi":"10.1680/ICSIC.64669.481","DOIUrl":"https://doi.org/10.1680/ICSIC.64669.481","url":null,"abstract":"","PeriodicalId":205150,"journal":{"name":"International Conference on Smart Infrastructure and Construction 2019 (ICSIC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134016636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Pereira, D. Orfeo, W. Ezequelle, D. Burns, Tian Xia, D. Huston
Digital three-dimensional (3-D) information concerning the location and condition of subsurface urban infrastructure is emerging as a potential new paradigm for aiding in the assessment, construction, emergency response, management, and planning of these vital assets. Subsurface infrastructure encompasses utilities (water, stormwater, wastewater, gas, electricity, telecommunications, steam, etc.), geotechnical formations, and the built underground (including tunnels, subways, garages and subsurface buildings). Traditional approaches for collecting location information include merging as-built drawings, historical records, and dead reckoning; and combining with information gathered by above-ground geophysical instruments, such as ground penetrating radars, magnetometers and acoustic sensors. This paper presents results of efforts aimed at using photogrammetric and augmented reality (AR) techniques to aid collecting, processing, and presenting 3-D location information.
{"title":"Photogrammetry and Augmented Reality for Underground Infrastructure Sensing, Mapping and Assessment","authors":"M. Pereira, D. Orfeo, W. Ezequelle, D. Burns, Tian Xia, D. Huston","doi":"10.1680/ICSIC.64669.169","DOIUrl":"https://doi.org/10.1680/ICSIC.64669.169","url":null,"abstract":"Digital three-dimensional (3-D) information concerning the location and condition of subsurface urban infrastructure is emerging as a potential new paradigm for aiding in the assessment, construction, emergency response, management, and planning of these vital assets. Subsurface infrastructure encompasses utilities (water, stormwater, wastewater, gas, electricity, telecommunications, steam, etc.), geotechnical formations, and the built underground (including tunnels, subways, garages and subsurface buildings). Traditional approaches for collecting location information include merging as-built drawings, historical records, and dead reckoning; and combining with information gathered by above-ground geophysical instruments, such as ground penetrating radars, magnetometers and acoustic sensors. This paper presents results of efforts aimed at using photogrammetric and augmented reality (AR) techniques to aid collecting, processing, and presenting 3-D location information.","PeriodicalId":205150,"journal":{"name":"International Conference on Smart Infrastructure and Construction 2019 (ICSIC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131834224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Seda Sendir Torisu, N. Faustin, Mohammed Elshafie, M. Black, K. Soga, R. Mair
{"title":"Monitoring of Shaft Excavations in Clay","authors":"Seda Sendir Torisu, N. Faustin, Mohammed Elshafie, M. Black, K. Soga, R. Mair","doi":"10.1680/ICSIC.64669.655","DOIUrl":"https://doi.org/10.1680/ICSIC.64669.655","url":null,"abstract":"","PeriodicalId":205150,"journal":{"name":"International Conference on Smart Infrastructure and Construction 2019 (ICSIC)","volume":"104 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134426391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper describes the ambition of the Environment Agency to develop and trial predictive condition-based monitoring systems for its mechanical and electrical flood risk management equipment. The aims, objective and research methods for this project are described. The challenge of developing any predictive capability for flood risk management assets which, typically have a low frequency of operation and therefore a paucity of data, is discussed. Some practical suggestions to overcome this specific challenge are presented.
{"title":"Monitoring Pumping Station Performance for Maintenance Optimisation","authors":"O. Tarrant, K. Solts, S. Čarman, Y. Ugradar","doi":"10.1680/ICSIC.64669.649","DOIUrl":"https://doi.org/10.1680/ICSIC.64669.649","url":null,"abstract":"This paper describes the ambition of the Environment Agency to develop and trial predictive condition-based monitoring systems for its mechanical and electrical flood risk management equipment. The aims, objective and research methods for this project are described. The challenge of developing any predictive capability for flood risk management assets which, typically have a low frequency of operation and therefore a paucity of data, is discussed. Some practical suggestions to overcome this specific challenge are presented.","PeriodicalId":205150,"journal":{"name":"International Conference on Smart Infrastructure and Construction 2019 (ICSIC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134465687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jinli Zhang, Dawei Zhang, Xin Liu, R. Liu, G. Zhong
{"title":"A Framework of on-site Construction Safety Management Using Computer Vision and Real-Time Location System","authors":"Jinli Zhang, Dawei Zhang, Xin Liu, R. Liu, G. Zhong","doi":"10.1680/ICSIC.64669.327","DOIUrl":"https://doi.org/10.1680/ICSIC.64669.327","url":null,"abstract":"","PeriodicalId":205150,"journal":{"name":"International Conference on Smart Infrastructure and Construction 2019 (ICSIC)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133855620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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