Prediction of long-term strain in concrete structure using convolutional neural networks, air temperature and time stamp of measurements

Byung Kwan Oh, Hyo Seon Park, Branko Glisic

Research output: Contribution to journalArticlepeer-review

Abstract

A data prediction method for long-term strain measurements from concrete structures based on the strong correlation between air temperature and structural response is proposed. A convolutional neural network (CNN) is employed to capture and define the relationship between the structural response and air temperature. The CNN is trained using measurements of air temperature and strain collected before the data interruption. To reflect the time-dependent long-term behavior of a concrete structure, the air temperature and corresponding time information are simultaneously utilized in the input layer of the proposed CNN. The trained CNN is then used to estimate the strain in the structure using only the air temperature data from the weather station in the event of a data loss from the structure's sensors. The presented method is validated using long-term data from fiber optic sensors embedded in a concrete footbridge at Princeton University and air temperature data from a nearby weather station.

Original languageEnglish (US)
Article number103665
JournalAutomation in Construction
Volume126
DOIs
StatePublished - Jun 2021

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Civil and Structural Engineering
  • Building and Construction

Keywords

  • Air temperature
  • Convolutional neural network
  • Data prediction
  • Fiber optic strain sensor
  • Long-term monitoring of concrete structure
  • Recovery
  • Reliability verification
  • Structural health monitoring

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