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This paper examines an analysis model for predicting the tip capacity of drilled shaft foundations under gravelly soils. Forty one static compression load test data are utilized for this purpose. Comparison of predicted and measured results demonstrates that the prediction model greatly overestimates the tip capacity of drilled shafts. Further assessment on the model reveals a greater variation in three coefficients; the effective overburden pressure ( ), the overburden bearing capacity factor (Nq); and the bearing capacity modifier for soil rigidity (ζqr). These factors are modified from the back-analysis of the drilled shaft load test results. Varying effective shaft depths are considered for the back-calculation to explore their effects on capacity behavior. Based on the analyses, the recommended effective shaft depth for the evaluation of effective overburden pressure is limited to 15B (B=shaft diameter). The Nq and ζqr are enhanced while maintaining their basic relationship with the soil effective friction angle, in which the Nq increases and ζqr decreases as increases. Specific design recommendations for the tip bearing capacity analysis of drilled shafts in gravelly soils are given for engineering practice.

This study explores the characteristics of ground vibration induced by Taiwan high-speed trains on embankments. A series of field measurement data is used for evaluating near-field vibration, far-field vibration, and vibration influence distance. Various influence factors, including train speed, ground shear wave velocity, frequency dependence, and volume of the structure, are applied for evaluation. Based on the analyses, the near-field ground vibration mainly depends on the train speed, ground shear wave velocity, and frequency dependence. The far-field vibration propagation is affected by ground shear wave velocity and frequency dependence. In general, the high frequency range has the highest attenuation coefficient and the low frequency range has the lowest. The influence distance in hard ground is the farthest, whereas the soft ground is the shortest. Finally, a specific ground vibration assessment is established using these characteristics.

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This paper presents an automatic prediction model for ground vibration induced by Taiwan high-speed trains on embankment structures. The prediction model is developed using different field-measured ground vibration data. The main characteristics that affect the overall vibration level are established based on the database of measurement results. The influence factors include train speed, ground condition, measurement distance, and supported structure. Support vector machine (SVM) algorithm, a widely used prediction model, is adopted to predict the vibration level induced by high-speed trains on embankments. The measured and predicted vibration levels are compared to verify the reliability of the prediction model. Analysis results show that the developed SVM model can reasonably predict vibration level with an accuracy rate of 72% to 84% for four types of vibration level, including overall, low, middle, and high frequency ranges. The methodology in developing the automatic prediction system for ground vibration level is also presented in this paper.