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<p>Ground vibrations caused by dynamic loading, such as blasting and pile driving, can pose serious risks to nearby structures and infrastructure. This study introduces a hybrid methodology that integrates numerical modeling with a genetic algorithm (GA) to develop a shape function for modeling ground vibrations by capturing the fundamental relationships among key parameters. Comprehensive numerical simulations were performed with parametric variations to examine the effects of load magnitude, distance from the source, and ground elastic modulus on peak particle velocity (PPV) in a free-field scenario under dynamic loading. Then, a GA was utilized to derive an optimal nonlinear regression model, establishing a robust relationship between the input parameters and PPV. To enhance the model’s adaptability to real-world conditions, a system complexity factor (SCF) was introduced as a correction factor, enabling the model to account for site-specific geological and loading complexities. Validation against field measurements demonstrated strong predictive accuracy and a good fit, highlighting the model’s reliability. This methodology provides a versatile and generalizable tool for estimating PPV across a wide range of dynamic loading scenarios.</p>

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Prediction of Ground Vibrations Under Dynamic Loading Using a Hybrid Numerical and Genetic Algorithm Approach

  • 期刊论文

  • 发表日期：

    2026年6月16日

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  • Amin Manouchehrian,
  • Rini Asnida Abdullah,
  • Muhammad Irfan Shahrin

摘要

Ground vibrations caused by dynamic loading, such as blasting and pile driving, can pose serious risks to nearby structures and infrastructure. This study introduces a hybrid methodology that integrates numerical modeling with a genetic algorithm (GA) to develop a shape function for modeling ground vibrations by capturing the fundamental relationships among key parameters. Comprehensive numerical simulations were performed with parametric variations to examine the effects of load magnitude, distance from the source, and ground elastic modulus on peak particle velocity (PPV) in a free-field scenario under dynamic loading. Then, a GA was utilized to derive an optimal nonlinear regression model, establishing a robust relationship between the input parameters and PPV. To enhance the model’s adaptability to real-world conditions, a system complexity factor (SCF) was introduced as a correction factor, enabling the model to account for site-specific geological and loading complexities. Validation against field measurements demonstrated strong predictive accuracy and a good fit, highlighting the model’s reliability. This methodology provides a versatile and generalizable tool for estimating PPV across a wide range of dynamic loading scenarios.