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Strain Prediction of a Bridge Deploying Autoregressive Models with ARIMA and Machine Learning Algorithms

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Engineering Applications of Neural Networks (EANN 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1826))

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Abstract

Lately, there is an increasing demand for resilient infrastructure assets. To support the documentation of resilience, Structural Health Monitoring (SHM) data is a necessity, as well as traffic loads. Those diagnosis and function data can be the basis for the prognosis of future prediction for the performance of the assets. In this research, the authors present an approach based on nineteen (19) Machine Learning (ML) techniques for the prediction of the future strain values in a Dutch Highway Bridge depending on previous measurements of the strain. For the evaluation of the algorithms the indices Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and R square (R2) were chosen and Fast Fourier Transform (FFT) and Averaged Standardized Values were chosen for the time series pre-processing. The results are extremely promising, with almost every algorithm to predict the fluctuations of strain values and the indexes are quite satisfactory. The results ensure that those who are responsible for the maintenance of the bridge or for its repairs, could use these models to determine which time that should take action.

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Authors and Affiliations

  1. Department of Civil Engineering-Lab of Mathematics and Informatics (ISCE), Democritus University of Thrace, 67100, Xanthi, Greece

    Anastasios Panagiotis Psathas, Lazaros Iliadis & Antonios Papaleonidas

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  1. Anastasios Panagiotis Psathas
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  2. Lazaros Iliadis
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  3. Antonios Papaleonidas
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Correspondence to Anastasios Panagiotis Psathas .

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Editors and Affiliations

  1. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  3. University of Leon, León, Spain

    Serafin Alonso

  4. Teesside University, Middlesbrough, UK

    Chrisina Jayne

  5. University of the West of England, Bristol, UK

    Elias Pimenidis

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Psathas, A.P., Iliadis, L., Papaleonidas, A. (2023). Strain Prediction of a Bridge Deploying Autoregressive Models with ARIMA and Machine Learning Algorithms. In: Iliadis, L., Maglogiannis, I., Alonso, S., Jayne, C., Pimenidis, E. (eds) Engineering Applications of Neural Networks. EANN 2023. Communications in Computer and Information Science, vol 1826. Springer, Cham. https://doi.org/10.1007/978-3-031-34204-2_34

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  • DOI: https://doi.org/10.1007/978-3-031-34204-2_34

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