Prediction of train-induced ground-borne vibration transmission considering parametric uncertainties

Train-induced vibrations are of increasing concern as railway tracks and buildings are located closer to each other. In this study, a field measurement campaign was carried out where train-induced accelerations were monitored at the ground surface. Despite the uniformity of the train and track, field measurements unveil discrepancies in train-induced ground-borne vibrations at the given observation point. It underscores the need for a probabilistic evaluation of train-induced environmental vibrations. This study quantifies the uncertainties in train-induced ground-borne vibration transmissions and achieves a probabilistic assessment of the amplification phenomenon during ground-borne vibration transmissions. A transfer function-based model accounting for the train-induced ground-borne vibration transmission is developed and applied to the case study to justify its validity. Based on the validated prediction model, Monte Carlo method is subsequently adopted for quantifying uncertainties in train-induced ground-borne vibration transmissions caused by the spatially varied soil properties and by the variation in train speed, separately. Variations in soil properties are simulated using log-normally distributed random fields, while the train speed is simulated using a uniformly distributed random variable. The amplitude ratio between a pair of observation points is used to characterize ground-borne vibration transmissions. Burr distribution fitting is applied to the calculated samples of amplitude ratios, and during this process, the null hypothesis test is not rejected at the 5% significance level. The proposed methodology enables the determination of the amplification probability during train-induced ground-borne vibration transmissions. In addition, it aids reliability analysis and site screening in assessing building serviceability.