Formula-Guided Machine Learning for Ground Vibration Propagation and Attenuation Modeling
Pei-Yao Chen, Chen Wang, Fang Yan, Chao-Yang Zhang, Xiang-Yu Tan, Guo-Ping Lin, Jian-Sheng Fan

TL;DR
This paper introduces a hybrid machine learning approach that automatically derives accurate and interpretable ground vibration attenuation formulas, improving upon traditional methods for environmental impact assessments at scientific facilities.
Contribution
The study presents a novel hybrid iterative fitting method combining machine learning with the Bornitz formula for automatic, high-precision formula derivation from experimental data.
Findings
Derived a ground vibration attenuation formula validated by field tests
Demonstrated the method's superior interpretability and accuracy over black-box models
Verified physical validity through finite element simulations
Abstract
Understanding the propagation and attenuation patterns of ground vibrations is critical for evaluating the impact of environmental disturbances on large-scale scientific facilities. However, complex site conditions often result in intricate vibration behaviors, limiting the accuracy of traditional predictive methods. This study proposes a hybrid iterative fitting method that integrates machine learning with the Bornitz formula through an intelligent formula generation model. The method enables the automatic derivation of high-precision, interpretable ground vibration attenuation formulas from experimental data. A case study was conducted at the High Energy Photon Source in Beijing, where field tests were performed to collect vibration data. Using the proposed approach, an attenuation formula describing ground vibration propagation was derived. The physical validity of the model was…
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Taxonomy
TopicsRailway Engineering and Dynamics · Structural Health Monitoring Techniques · Acoustic Wave Phenomena Research
