Predictive Analysis of Structural Damage in Submerged Structures: A Case Study Approach Using Machine Learning

Predictive Analysis of Structural Damage in Submerged Structures: A Case Study Approach Using Machine Learning

This study focuses on the development of a machine learning (ML) model to elaborate on predictions of structural damage in submerged structures due to ocean states and subsequently compares it to a real-life case of a 6-month experiment with a benthic lander bearing a multitude of sensors. The ML mo...

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Bibliographic Details
Main Authors: Alexandre Brás dos Santos, Hugo Mesquita Vasconcelos, Tiago M. R. M. Domingues, Pedro J. S. C. P. Sousa, Susana Dias, Rogério F. F. Lopes, Marco L. P. Parente, Mário Tomé, Adélio M. S. Cavadas, Pedro M. G. P. Moreira
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Fluids
Subjects:
fluid–structure interaction
benthic lander
structural health monitoring
machine learning models
Online Access:https://www.mdpi.com/2311-5521/10/1/10
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