Time-dependent structural response estimation method for concrete structures using time information and convolutional neural networks

Byung Kwan Oh, Hyo Seon Park, Branko Glisic

Research output: Contribution to journalArticlepeer-review

Abstract

In this study, a structural response estimation method for evaluating the long-term strain, which can serve as the basis of assessing structural safety of concrete structures, is presented. Characteristics of short-term deformation caused by environmental influences and long-term inelastic deformation caused by combination of rheological effects and environmental influences are identified. To reflect these time-dependent short- and long-term deformation characteristics in the proposed method, the strain and corresponding time information collected from a structural health monitoring (SHM) are used. In the proposed method, the relationship between a strain response measured from a concrete structure and the corresponding time information including the year, month, day, and hour is defined by a convolutional neural network (CNN), which is a deep learning technique. The method assumes that the main sources of the strain in the structure are environmental influences, such as temperature and humidity, which have daily and seasonal periodicity, and rheological effects in concrete, such as creep and shrinkage, but it also assumes that the quantitative information on these sources is not available (e.g., environmental temperature and humidity, and rheological strain were not reliably measured). Thus, the CNN method developed in this study is trained only with strain and time data collected over several years, and used to estimate strain values within a specific timeframe, e.g., when the safety evaluation of the concrete structure is required or in case of SHM system failure or data loss. The presented method was developed and validated using SHM data from a real structure instrumented with fiber optic strain sensors. In addition, exploration of the constitution of the input of the CNN identified the type of time information that is the most effective in the long-term strain estimation of the developed method.

Original languageEnglish
Article number115193
JournalEngineering Structures
Volume275
DOIs
Publication statusPublished - 2023 Jan 15

Bibliographical note

Funding Information:
This research was supported by the Research Grant of Jeonju University in 2022. 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, No. 2018R1A5A1025137, and NRF-2022R1C1C1009871). 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 research was supported by the Research Grant of Jeonju University in 2022. 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, No. 2018R1A5A1025137, and NRF-2022R1C1C1009871). 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

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering

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