Framework for Identification and Prediction of Corrosion Degradation in a Steel Column through Machine Learning and Bayesian Updating

Abstract

In recent years, structural health monitoring, starting from accelerometric data, is a method which has become widely adopted. Among the available techniques, machine learning is one of the most innovative and promising, supported by the continuously increasing computational capacity of current computers. The present work investigates the potential benefits of a framework based on supervised learning suitable for quantifying the corroded thickness of a structural system, herein uniformly applied to a reference steel column. The envisaged framework follows a hybrid approach where the training data are generated from a parametric and stochastic finite element model. The learning activity is performed by a support vector machine with Bayesian optimization of the hyperparameters, in which a penalty matrix is introduced to minimize the probability of missed alarms. Then, the estimated structural health conditions are used to update an exponential degradation model with random coefficients suitable for providing a prediction of the remaining useful life of the simulated corroded column. The results obtained show the potentiality of the proposed framework and its possible future extension for different types of damage and structural types.

Machine Learning Big Data Analysis of the Impact of Air Pollutants on Rhinitis-Related Hospital Visits

“…In recent years, the advent and ascension of machine learning techniques have catalyzed a revolution in the analysis of biomedical big data [ 19 , 20 , 21 , 22 ]. The ability to process and derive meaningful insights from large-scale, complex data has paved the way for a more nuanced understanding of disease patterns, genetic underpinnings, and the impacts of environmental factors on health [ 23 , 24 , 25 , 26 ]. In the context of our study, machine learning offers a novel approach to understanding the intricate dynamics between air pollution and rhinitis, thus aiding in the extraction of valuable insights from the vast amount of data we have amassed.…”