TY - JOUR
T1 - Long-term structural response prediction models for concrete structures using weather data, fiber-optic sensing, and convolutional neural network
AU - Seon Park, Hyo
AU - Hong, Taehoon
AU - Lee, Dong Eun
AU - Kwan Oh, Byung
AU - Glisic, Branko
N1 - Funding Information:
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science, ICT & Future Planning, MSIP) (NRF-2021R1A2C33008989 and No. 2018R1A5A1025137). We would like to thank Steve Hancock and Turner Construction Company; Ryan Woodward and Ted Zoli, HNTB Corporation; Dong Lee and A.G. Construction Corporation; Steven Mancini and Timothy R. Wintermute, Vollers Excavating & Construction, Inc.; SMARTEC SA, Switzerland; Micron Optics, Inc., Atlanta, GA. In addition the following personnel, departments, and offices from Princeton University supported and helped realization of the project: Geoffrey Gettelfinger, James P. Wallace, Miles Hersey, Paul Prucnal, Yanhua Deng, Mable Fok; Faculty and staff of Department of Civil and Environmental Engineering and our students: Dorotea Sigurdardottir, Hiba Abdel-Jaber, David Hubbell, Maryanne Wachter, Jessica Hsu, George Lederman, Kenneth Liew, Chienchuan Chen, Allison Halpern, Morgan Neal, Daniel Reynolds, Konstantinos Bakis, and Daniel Schiffner.
Funding Information:
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science, ICT & Future Planning, MSIP) (NRF-2021R1A2C33008989 and No. 2018R1A5A1025137). We would like to thank Steve Hancock and Turner Construction Company; Ryan Woodward and Ted Zoli, HNTB Corporation; Dong Lee and A.G. Construction Corporation; Steven Mancini and Timothy R. Wintermute, Vollers Excavating & Construction, Inc.; SMARTEC SA, Switzerland; Micron Optics, Inc. Atlanta, GA. In addition the following personnel, departments, and offices from Princeton University supported and helped realization of the project: Geoffrey Gettelfinger, James P. Wallace, Miles Hersey, Paul Prucnal, Yanhua Deng, Mable Fok; Faculty and staff of Department of Civil and Environmental Engineering and our students: Dorotea Sigurdardottir, Hiba Abdel-Jaber, David Hubbell, Maryanne Wachter, Jessica Hsu, George Lederman, Kenneth Liew, Chienchuan Chen, Allison Halpern, Morgan Neal, Daniel Reynolds, Konstantinos Bakis, and Daniel Schiffner.
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/9/1
Y1 - 2022/9/1
N2 - This study proposes a long-term strain prediction model for concrete structures using weather data. In the proposed model, the relationship between weather and the strain data for a concrete structure is defined by a convolutional neural network (CNN), which is a machine learning technique, based on the strong correlation between the two types of data. The weather data collected from a weather station located near the monitored structure are used in the input layer of the CNN; the strain data measured by fiber-optic sensors (FOSs) at the structure are used in the output layer of the CNN. The trained CNN can predict the strain using only weather data in the case of sensor malfunctions or data loss. Various types of weather data, including the air temperature, relative humidity, and wind speed, are used to determine the environmental factors that are valid for predicting the long-term deformation of concrete structures. Six prediction models are proposed, in which the three types of weather data are used individually or jointly in the input layer of the CNN. The proposed models are applied to predict the strain of a footbridge located at Princeton University. To build the prediction models, the strain data measured at the bridge over a long-term period and the weather data obtained from a nearby local weather station are used. The performance of the prediction models is verified through long-term strain prediction. Furthermore, the prediction performance of the analyzed models is compared, and the weather data types that are significant for predicting the long-term deformation of concrete structures are elucidated.
AB - This study proposes a long-term strain prediction model for concrete structures using weather data. In the proposed model, the relationship between weather and the strain data for a concrete structure is defined by a convolutional neural network (CNN), which is a machine learning technique, based on the strong correlation between the two types of data. The weather data collected from a weather station located near the monitored structure are used in the input layer of the CNN; the strain data measured by fiber-optic sensors (FOSs) at the structure are used in the output layer of the CNN. The trained CNN can predict the strain using only weather data in the case of sensor malfunctions or data loss. Various types of weather data, including the air temperature, relative humidity, and wind speed, are used to determine the environmental factors that are valid for predicting the long-term deformation of concrete structures. Six prediction models are proposed, in which the three types of weather data are used individually or jointly in the input layer of the CNN. The proposed models are applied to predict the strain of a footbridge located at Princeton University. To build the prediction models, the strain data measured at the bridge over a long-term period and the weather data obtained from a nearby local weather station are used. The performance of the prediction models is verified through long-term strain prediction. Furthermore, the prediction performance of the analyzed models is compared, and the weather data types that are significant for predicting the long-term deformation of concrete structures are elucidated.
KW - Air temperature
KW - Concrete structure
KW - Convolutional neural network
KW - Fiber-optic strain sensor
KW - Long-term monitoring
KW - Relative humidity
KW - Structural health monitoring
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U2 - 10.1016/j.eswa.2022.117152
DO - 10.1016/j.eswa.2022.117152
M3 - Article
AN - SCOPUS:85129466337
SN - 0957-4174
VL - 201
JO - Expert Systems with Applications
JF - Expert Systems with Applications
M1 - 117152
ER -