An artificial intelligence strategy to detect damage from response measurements: application on an ancient tower

Marrongelli, Gabriele; Finotti, Rafaelle Piazzaroli; Gentile, Carmelo; Barbosa, Fernando Policarpo

doi:10.1051/matecconf/201821121002

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…Title: An artificial intelligence strategy to detect damage from response measurements: application to an ancient tower. Authors: Marrongelli et al [18]…”

Section: Year Of Publication: 2018mentioning

confidence: 99%

A proposal of classification for machine-learning vibration-based damage identification methods

Marafini¹

2023

Theoretical and Applied Mechanics - AIMETA 2022

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Abstract. Recent advances in computing power and sensing technology led to a significant evolution of Structural Health Monitoring (SHM) techniques, transforming SHM into a “Big Data” problem. The use of data-driven approaches for damage identification purposes, specifically Machine Learning (ML) methods, has gained popularity. ML can help at various levels of the SHM process: to pre- and post-process input data, extract damage sensitive features, and operate pattern recognition in measured data and output valuable information for damage identification. In this paper, the role of ML in SHM applications is discussed together with a new scheme for classifying ML applications in SHM, especially focusing on vibration-based monitoring, given its consolidated theoretical base. Finally, the implications of the application of these methods to historic structures are discussed, with a brief account of existing case studies. The proposed classification is exemplified using the most recent studies available in the literature on cultural heritage structures.

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“…Title: An artificial intelligence strategy to detect damage from response measurements: application to an ancient tower. Authors: Marrongelli et al [18]…”

Section: Year Of Publication: 2018mentioning

confidence: 99%

A proposal of classification for machine-learning vibration-based damage identification methods

Marafini¹

2023

Theoretical and Applied Mechanics - AIMETA 2022

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“…Then, four algorithms—auto-associative neural network, factor analysis, Mahalanobis distance, and singular value decomposition—were applied and their accuracy was compared. Other studies include research using 1D Convolutional Neural Networks [ 16 ], support vector machines [ 17 , 18 , 19 , 20 ], Gaussian process regression [ 21 ], and Genetic algorithms [ 18 , 22 ]. Although these studies propose machine learning methods based on interesting experimental data, they basically remain at the level of damage detection, because it is very difficult to prepare test specimens with various damage geometries in the order of 10 2 or more in experiments.…”

Section: Introductionmentioning

confidence: 99%

Development of a Machine Learning-Based Damage Identification Method Using Multi-Point Simultaneous Acceleration Measurement Results

Chun

Yamane

Izumi

et al. 2020

Sensors

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It is necessary to assess damage properly for the safe use of a structure and for the development of an appropriate maintenance strategy. Although many efforts have been made to measure the vibration of a structure to determine the degree of damage, the accuracy of evaluation is not high enough, so it is difficult to say that a damage evaluation based on vibrations in a structure has not been put to practical use. In this study, we propose a method to evaluate damage by measuring the acceleration of a structure at multiple points and interpreting the results with a Random Forest, which is a kind of supervised machine learning. The proposed method uses the maximum response acceleration, standard deviation, logarithmic decay rate, and natural frequency to improve the accuracy of damage assessment. We propose a three-step Random Forest method to evaluate various damage types based on the results of these many measurements. Then, the accuracy of the proposed method is verified based on the results of a cross-validation and a vibration test of an actual damaged specimen.

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Vibration-Based Novelty Detection of Masonry Towers Using Pattern Recognition

Marrongelli

Gentile

Saisi

2021

Lecture Notes in Civil Engineering

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An artificial intelligence strategy to detect damage from response measurements: application on an ancient tower

Cited by 4 publications

References 12 publications

A proposal of classification for machine-learning vibration-based damage identification methods

A proposal of classification for machine-learning vibration-based damage identification methods

Development of a Machine Learning-Based Damage Identification Method Using Multi-Point Simultaneous Acceleration Measurement Results

Vibration-Based Novelty Detection of Masonry Towers Using Pattern Recognition

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