Integrating machine learning techniques for predicting ground vibration in pile driving activities

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

Autores

Abouelmaty, AM (Autor) Outra; Aires Colaço (Autor) FEUP; Fares, AA (Autor) Outra; Ramos, A (Autor) Outra; Costa, PA (Autor) Outra

Revista

Título: Computers and Geotechnics

Vol. 176

ISSN: 0266-352X

Editora: Elsevier

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-017-481

DOI: 10.1016/j.compgeo.2024.106784

Abstract (EN): This study focuses on the assessment of ground vibrations due to pile driving activities. Given the likelihood of excessive vibration due to the driving process, it is imperative to predict vibration levels during the design phase. The primary goal of this work is to integrate machine learning techniques, specifically Extreme Gradient Boosting (XGBoost) and Artificial Neural Networks (ANNs) for real-time vibration prediction. The training dataset was generated using a validated numerical model and the trained models were validated based on experimental results. This validation process highlights the efficiency and accuracy of Extreme Gradient Boosting in predicting the-free-field response of the ground.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 10