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.
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© 2022 Elsevier Ltd
All Science Journal Classification (ASJC) codes
- Civil and Structural Engineering