Deep Learning of Structural Changes in Historical Buildings: The Case Study of the Pisa Tower

Mario Cimino, Federico Galatolo, Marco Parola, Nicola Perilli, Nunziante Squeglia

2022

Abstract

Structural health monitoring of buildings via agnostic approaches is a research challenge. However, due to the recent advent of pervasive multi-sensor systems, historical data samples are still limited. Consequently, data-driven methods are often unfeasible for long-term assessment. Nevertheless, some famous historical buildings have been subject to monitoring for decades, before the development of smart sensors and Deep Learning (DL). This paper presents a DL approach for the agnostic assessment of structural changes. The proposed approach has been experimented to the stabilizing intervention carried out in 2000-2002 on the leaning tower of Pisa (Italy). The data set is made by operational and environmental measures collected from 1993 to 2006. Both conventional and recent approaches are compared: Multiple Linear regression, LSTM and Tansformer. Experimental results are promising, and clearly shows a better change sensitivity of the LSTM, as well as a better modeling accuracy of the Transformer.

Paper Citation

in Harvard Style

Cimino M., Galatolo F., Parola M., Perilli N. and Squeglia N. (2022). Deep Learning of Structural Changes in Historical Buildings: The Case Study of the Pisa Tower. In Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - Volume 1: NCTA; ISBN 978-989-758-611-8, SciTePress, pages 396-403. DOI: 10.5220/0011551800003332

in Bibtex Style

@conference{ncta22,
author={Mario Cimino and Federico Galatolo and Marco Parola and Nicola Perilli and Nunziante Squeglia},
title={Deep Learning of Structural Changes in Historical Buildings: The Case Study of the Pisa Tower},
booktitle={Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - Volume 1: NCTA},
year={2022},
pages={396-403},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011551800003332},
isbn={978-989-758-611-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - Volume 1: NCTA
TI - Deep Learning of Structural Changes in Historical Buildings: The Case Study of the Pisa Tower
SN - 978-989-758-611-8
AU - Cimino M.
AU - Galatolo F.
AU - Parola M.
AU - Perilli N.
AU - Squeglia N.
PY - 2022
SP - 396
EP - 403
DO - 10.5220/0011551800003332
PB - SciTePress