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Autoregressive Deep Learning Models for Bridge Strain Prediction

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Engineering Applications of Neural Networks (EANN 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1600))

Abstract

Infrastructures are often subjected to harsh loading scenarios and severe environmental conditions, not anticipated during design, which result in their long-term structural deterioration. Thus, monitoring and maintaining the urban infrastructure is critical for the resilience. In this research, the authors present a modeling approach, for the prediction of future strain values in a Dutch Highway Bridge, based on eleven Deep Learning (DL) algorithms. Previous strain measurements were used as input. The performance of the developed Machine Learning model was evaluated using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and R square (R2) indices. The Fast Fourier Transform (FFT) was employed for the pre-processing of the involved time series. The obtained results are extremely promising for predicting the performance under design loads. All algorithms have proven their capacity to successfully predict the fluctuations of strain values. The authorities responsible for the function and management of the bridge, can feel confident to rely on these models in order to schedule in time proper maintenance works.

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Acknowledgments

We acknowledge support of this work by the project “Risk and Resilience Assessment Center – Prefecture of East Macedonia and Thrace - Greece.” (MIS 5047293) which is implemented under the Action “Reinforcement of the Research and Innovation Infrastructure”, funded by the Operational Programme “Competitiveness, Entrepreneurship and Innovation” (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund).

Author information

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 & Dimitris Bountas

  2. Department of Civil Engineering-Laboratory of Reinforced Concrete and Seismic Design of Structures, Democritus University of Thrace, 67100, Xanthi, Greece

    Dimitra V. Achillopoulou & Nikoleta K. Stamataki

  3. Department of Civil and Environmental Engineering, University of Surrey, Guildford, UK

    Dimitra V. Achillopoulou

  4. Department of Civil Engineering-Lab of Project Management, Democritus University of Thrace, 67100, Xanthi, Greece

    Ioannis M. Dokas

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  1. Anastasios Panagiotis Psathas
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  2. Lazaros Iliadis
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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. Teesside University, Middlesbrough, UK

    Chrisina Jayne

  3. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Anastasios Tefas

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

    Elias Pimenidis

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Psathas, A.P. et al. (2022). Autoregressive Deep Learning Models for Bridge Strain Prediction. In: Iliadis, L., Jayne, C., Tefas, A., Pimenidis, E. (eds) Engineering Applications of Neural Networks. EANN 2022. Communications in Computer and Information Science, vol 1600. Springer, Cham. https://doi.org/10.1007/978-3-031-08223-8_13

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  • DOI: https://doi.org/10.1007/978-3-031-08223-8_13

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