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

• Type: Article in International Scientific Journal
• Date: 2024

Authors

Abouelmaty, AM (Author) Other; Aires Colaço (Author) FEUP; Fares, AA (Author) Other; Ramos, A (Author) Other; Costa, PA (Author) Other

Journal

Title: Computers and Geotechnics

Vol. 176

ISSN: 0266-352X

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10