In China, real estate has been developed rapidly in the past 30 years. According to the market competition and demand, the owners and developers realize that the project management, which applies the information of consulting, design, implementation, and operations, should be more standardized. With the maturity of construction technology and the continuous updating of digital technology, e.g., Building Information Modeling (BIM), the trend to enter into a digital world is becoming widely accepted by the AEC sectors. The perspective of owners and developers in project management includes cost, schedule, quality, and safety. While the schedule of a project is one of the most critical factors, the high turnover demand of construction market, the design depth in many cases have not met the construction requirements during the construction of the stage of projects. In the construction stage, 4D simulation can be carried out, which is based on the construction organization and design, so that the reasonable construction progress can advise the project manager. This paper aims to explore the original scheme of 4D simulation and illustrates the other main application of BIM in Chinese construction market that how to deliver a smart built asset for users.
{"title":"The Value of BIM for Project Management in a Smart Built Asset in China","authors":"Fang Fang, L. Tang, Ren Bin","doi":"10.1680/ICSIC.64669.251","DOIUrl":"https://doi.org/10.1680/ICSIC.64669.251","url":null,"abstract":"In China, real estate has been developed rapidly in the past 30 years. According to the market competition and demand, the owners and developers realize that the project management, which applies the information of consulting, design, implementation, and operations, should be more standardized. With the maturity of construction technology and the continuous updating of digital technology, e.g., Building Information Modeling (BIM), the trend to enter into a digital world is becoming widely accepted by the AEC sectors. The perspective of owners and developers in project management includes cost, schedule, quality, and safety. While the schedule of a project is one of the most critical factors, the high turnover demand of construction market, the design depth in many cases have not met the construction requirements during the construction of the stage of projects. In the construction stage, 4D simulation can be carried out, which is based on the construction organization and design, so that the reasonable construction progress can advise the project manager. This paper aims to explore the original scheme of 4D simulation and illustrates the other main application of BIM in Chinese construction market that how to deliver a smart built asset for users.","PeriodicalId":205150,"journal":{"name":"International Conference on Smart Infrastructure and Construction 2019 (ICSIC)","volume":"13 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":"129495269","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}
To obtain actual conditions of infrastructure assets and manage them more efficiently, extensive research efforts have been placed on structural health monitoring (SHM), especially those using data-driven methods. Recently, deep learning becomes a research hotspot in many application areas, including the SHM domain. Their performance largely relies on the quality and quantity of the training data, obtained either experimentally or numerically. Due to the time and expense restraints, field or laboratory test data are normally limited by the variation of structural conditions, while the quality of numerical simulation data is subjective to experts' modelling skills. Therefore, the actual performance of deep learning algorithms with limited training data needs to be studied, and the alternative ways to generate more training data need to be developed. In this work, we develop a new one-Dimensional Convolutional Neural Network (1D-CNN) for structural condition identification. A laboratory case study is conducted to evaluate the performance of the algorithm. A steel Warren truss bridge structure is constructed and instrumented with accelerometers and impact hammer. The vibration tests under seven different scenarios are conducted, and each scenario has five repeated test data. The algorithm is trained with different quantities of training data (from one test data to four test data for each scenario). The results show that condition identification results become reliable with at least three repeated test data. To overcome the challenge of limited monitoring data, we propose the potential application of Generative Adversarial Networks (GANs) to generate more reliable training data.
{"title":"Deep Learning Algorithms for Structural Condition Identification with Limited Monitoring Data","authors":"Tong Zhang, Ying Wang","doi":"10.1680/ICSIC.64669.421","DOIUrl":"https://doi.org/10.1680/ICSIC.64669.421","url":null,"abstract":"To obtain actual conditions of infrastructure assets and manage them more efficiently, extensive research efforts have been placed on structural health monitoring (SHM), especially those using data-driven methods. Recently, deep learning becomes a research hotspot in many application areas, including the SHM domain. Their performance largely relies on the quality and quantity of the training data, obtained either experimentally or numerically. Due to the time and expense restraints, field or laboratory test data are normally limited by the variation of structural conditions, while the quality of numerical simulation data is subjective to experts' modelling skills. Therefore, the actual performance of deep learning algorithms with limited training data needs to be studied, and the alternative ways to generate more training data need to be developed. In this work, we develop a new one-Dimensional Convolutional Neural Network (1D-CNN) for structural condition identification. A laboratory case study is conducted to evaluate the performance of the algorithm. A steel Warren truss bridge structure is constructed and instrumented with accelerometers and impact hammer. The vibration tests under seven different scenarios are conducted, and each scenario has five repeated test data. The algorithm is trained with different quantities of training data (from one test data to four test data for each scenario). The results show that condition identification results become reliable with at least three repeated test data. To overcome the challenge of limited monitoring data, we propose the potential application of Generative Adversarial Networks (GANs) to generate more reliable training data.","PeriodicalId":205150,"journal":{"name":"International Conference on Smart Infrastructure and Construction 2019 (ICSIC)","volume":"55 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":"113938704","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":"Reassessment of Crossrail Tottenham Court Road Station Excavation Design Using the Observational Method Optimistic Approach A","authors":"Ying Chen, D. Nicholson, G. Biscontin","doi":"10.1680/ICSIC.64669.437","DOIUrl":"https://doi.org/10.1680/ICSIC.64669.437","url":null,"abstract":"","PeriodicalId":205150,"journal":{"name":"International Conference on Smart Infrastructure and Construction 2019 (ICSIC)","volume":"1 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":"115404355","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}
S. A. Plumb, M. Watt, C. M. Ellis, T. Sajwaj, S. G. Ross, P. Graham, N. Metje, D. Chapman, E. Stewart, A. D. Quinn, L. von der Tann
{"title":"Sensor and Satellite Asset Alert and Management System (SSAAMS)","authors":"S. A. Plumb, M. Watt, C. M. Ellis, T. Sajwaj, S. G. Ross, P. Graham, N. Metje, D. Chapman, E. Stewart, A. D. Quinn, L. von der Tann","doi":"10.1680/ICSIC.64669.085","DOIUrl":"https://doi.org/10.1680/ICSIC.64669.085","url":null,"abstract":"","PeriodicalId":205150,"journal":{"name":"International Conference on Smart Infrastructure and Construction 2019 (ICSIC)","volume":"75 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":"115812182","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}
André Paul Neto-Bradley, Ruchi Choudhary, A. Bazaz
{"title":"Tailoring Residential Energy Provision Strategies in Fast-Growing Cities using Targeted Data Collection","authors":"André Paul Neto-Bradley, Ruchi Choudhary, A. Bazaz","doi":"10.1680/ICSIC.64669.151","DOIUrl":"https://doi.org/10.1680/ICSIC.64669.151","url":null,"abstract":"","PeriodicalId":205150,"journal":{"name":"International Conference on Smart Infrastructure and Construction 2019 (ICSIC)","volume":"17 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":"130045918","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":"Image-Based Multiview Change Detection in Concrete Structures","authors":"A. Buatik, I. Pasityothin, K. Chaiyasarn","doi":"10.1680/ICSIC.64669.693","DOIUrl":"https://doi.org/10.1680/ICSIC.64669.693","url":null,"abstract":"","PeriodicalId":205150,"journal":{"name":"International Conference on Smart Infrastructure and Construction 2019 (ICSIC)","volume":"99 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120970648","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":"Prefabricated Secondary Units for Overcoming the Shortage of Houses: A Case Study of New Zealand","authors":"Milad Moradibistouni, B. Vale, N. Isaacs","doi":"10.1680/ICSIC.64669.291","DOIUrl":"https://doi.org/10.1680/ICSIC.64669.291","url":null,"abstract":"","PeriodicalId":205150,"journal":{"name":"International Conference on Smart Infrastructure and Construction 2019 (ICSIC)","volume":"48 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":"129209521","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}
Junhua Xiao, Dong Liang, Xinzhong Nong, Nan Wu, Jinrong Song
{"title":"Prediction Models of Service Performance Degradation for Metro Shield Tunnels","authors":"Junhua Xiao, Dong Liang, Xinzhong Nong, Nan Wu, Jinrong Song","doi":"10.1680/ICSIC.64669.513","DOIUrl":"https://doi.org/10.1680/ICSIC.64669.513","url":null,"abstract":"","PeriodicalId":205150,"journal":{"name":"International Conference on Smart Infrastructure and Construction 2019 (ICSIC)","volume":"38 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":"131706388","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}
C. Chiu, B. Janković-Nišić, L. Pocock, L. Murphy, R. Alkhatib, J. Cantone, O. EriOlu, J. Downs
{"title":"Optimising Strategic Decision Making In Water Networks","authors":"C. Chiu, B. Janković-Nišić, L. Pocock, L. Murphy, R. Alkhatib, J. Cantone, O. EriOlu, J. Downs","doi":"10.1680/ICSIC.64669.021","DOIUrl":"https://doi.org/10.1680/ICSIC.64669.021","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":"131731185","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":"Development of BIM-Sensor Integrated Platform for MEP Piping Maintenance","authors":"Y. Jing, Chao Chen, L. Tang, H. Xiong, Y. X. Wang","doi":"10.1680/ICSIC.64669.055","DOIUrl":"https://doi.org/10.1680/ICSIC.64669.055","url":null,"abstract":"","PeriodicalId":205150,"journal":{"name":"International Conference on Smart Infrastructure and Construction 2019 (ICSIC)","volume":"244 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":"133424314","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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