Machine learning and cointegration for structural health monitoring of a model under environmental effects

• Tipo: Artigo em Revista Científica Internacional
• Data: 2024

Autores

Rodrigues, M (Autor) Outra; Vera L. Miguéis (Autor) FEUP; Felix, C (Autor) Outra; Rodrigues, C (Autor) Outra

Revista

Título: Expert Systems with Applications

Vol. 238

ISSN: 0957-4174

Editora: Elsevier

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00Z-8CG

DOI: 10.1016/j.eswa.2023.121739

Abstract (EN): Data-driven models have been recognized as powerful tools to support Structural Health Monitoring (SHM). This paper contributes to the literature by exploring two data-driven approaches to detect damage through changes in a set of variables that assess the condition of the structure, and accommodates the challenge that may arise due to the influence of environmental and operational variabilities. This influence is reflected in the response of the structure and can reduce the probability of detecting damage in a structure or increase the probability of signaling false positives. This paper conducts a comparative study between a machine learning detection approach (supported by linear regression, random forest, support vector machine, and neural networks) and a cointegration approach, with the aim of detecting damage as early as possible. This study also contributes to the literature by evaluating the merits of the damage detection methods using real data collected from a small-scale structure. The structure is analyzed in a reference state and a perturbed state in which damage is emulated. The results show that both approaches are able to detect damage within the first 24 h, without ever signaling false positives. The cointegration based approach can notably detect damage after 10 h and 15 minutes, while the machine learning approach takes 20 h 30 m to detect damage.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 14