User-Friendly Tool for Expedited Ground Vibration Assessment Induced by Impact Pile Driving

Driven piles are a common geotechnical solution for foundations in weak soil profiles. However, hammer impacts during the driving process can generate excessive levels of ground vibration, which, in extreme cases, can affect nearby structures and people. Due to the complexity of wave propagation in soils, the accurate prediction of these vibrations typically requires advanced numerical modeling approaches. To address this challenge, a surrogate modeling framework was developed by integrating Artificial Neural Networks (ANNs) and Extreme Gradient Boosting (XGBoost), trained on a synthetic dataset generated from an experimentally validated numerical model. The proposed surrogate model enables the rapid prediction of ground vibration characteristics, including peak particle velocity (PPV) and frequency content, across a broad range of soil, pile, and hammer conditions. In addition to its predictive capabilities, the tool allows users to design a specific mitigation measure (open trench) and compare the vibration levels with international standards. Experimental validation confirmed the model’s ability to replicate field measurements with acceptable accuracy. The expedited prediction tool is available as supplemental data and can be used by other researchers and technicians for quick and accurate ground vibration predictions.