A method for numerical simulation of train induced track vibration and wave propagation in subgrade has been proposed. The method uses a mass to simulate the bogie of a train and considers the effect of rail roughness. For this method, rail roughness is considered as a randomly generated signal and a filter is used to block the undesired components. The method predicts the particle velocity around the track and can be applied to many kinds of railroad trackbeds including traditional ballast trackbed and modern Hot mix asphalt (HMA) trackbed. Results from ballast and HMA trackbeds are compared and effects of HMA layer on damping track vibration and dissipating wave propagation are presented. To verify the credibility of the method, in-track measurements were also conducted. Site measurements included performing geophysical tests such as spectral analysis of surface wave test and seismic refraction test to determine the subsurface conditions at the test site. Ballast and HMA samples were tested in the laboratory by resonant column test to obtain the material properties. Particle velocities were measured and analyzed in the frequency domain. Results from in-track tests confirm the applicability of the numerical method. The findings and conclusions are summarized and future research topics are suggested.

Two surface wave testing methods, namely, (i) the spectral analysis of surface waves (SASW), and (ii) the multi-channel analysis of surface waves (MASW), form non-destructive and non-intrusive techniques for predicting the shear wave velocity profile of different layers of ground and pavement. These field testing tools are based on the dispersive characteristics of Rayleigh waves, that is, different frequency components of the surface wave travel at different velocities in layered media. The SASW and MASW testing procedure basically comprises of three different components: (i) field measurements by employing geophones/accelerometers, (ii) generating dispersion plots, and (iii) predicting the shear wave velocity profile based on an inversion analysis. For generating the field dispersion plot, the complexities involved while doing the phase unwrapping calculations for the SASW technique, while performing the spectral calculations on the basis of two receivers’ data, makes it difficult to automate since it requires frequent manual judgment. In the present thesis, a new method, based on the sliding Fourier transform, has been introduced. The proposed method has been noted to be quite accurate, computationally economical and it generally overcomes the difficulties associated with the unwrapping of the phase difference between the two sensors’ data. In this approach, the unwrapping of the phase can be carried out without any manual intervention. As a result, an automation of the entire computational process to generate the dispersion plot becomes feasible. The method has been thoroughly validated by including a number of examples on the basis of surface wave field tests as well as synthetic test data. While obtaining the dispersion image by using the MASW method, three different transformation techniques, namely, (i) the Park’s wavefield transform, (ii) the frequency (f) -wavenumber ( ) transform and (iii) the time intercept ( -phase slowness (p) transform have been utilized for generating the multimodal dispersion plots. The performance of these three different methods has been assessed by using synthetic as well as field data records obtained from a ground site by means of 48 geophones. Two-dimensional as well as three-dimensional dispersion plots were generated. The Park’s wavefield transformation method has been found to be especially advantageous since it neither requires a very high sampling rate nor an inclusion of the zero padding of the data in a wavenumber (distance) domain. In the case of an irregular dispersive media, a proper analysis of the higher modes existing in the dispersion plots becomes essential for predicting the shear wave velocity profile of ground on the basis of surface wave tests. In such cases, the establishment of the predominant mode becomes quite significant. In the current investigation for Rayleigh wave propagation, the predominant mode has been computed by maximizing the normalized vertical displacements along the free surface. Eigenvectors computed from the thin layer approach (TLM) approach are analyzed to predict the corresponding predominant mode. It is noted that the establishment of the predominant mode becomes quite important where only two to six sensors are employed and the governing (predominant) modal dispersion curve is usually observed rather than several multiple modes which can otherwise be identified by using around 24 to 48 multiple sensors. By using the TLM, it is, however, not possible to account for the exact contribution of the elastic half space in the dynamic stiffness matrix (DSM) approach. A method is suggested to incorporate the exact contribution of the elastic half space in the TLM. The numerical formulation is finally framed as a quadratic eigenvalue problem which can be easily solved by using the subroutine polyeig in MATLAB. The dispersion plots were generated for several chosen different ground profiles. The numerical results were found to match quite well with the data available from literature. In order to address all the three different aspects of SASW and MASW techniques, a series of field tests were performed on five different ground sites. The ground vibrations were induced by means of (i) a 65 kg mass dropped freely from a height of 5 m, and (ii) by using a 20 pound sledge hammer. It was found that by using a 65 kg mass dropped from a height of 5 m, for stiffer sites, ground exploration becomes feasible even up to a depth of 50-80 m whereas for the softer sites the exploration depth is reduced to about 30 m. By using a 20 lb sledge hammer, the exploration depth is restricted to only 8-10 m due to its low impact energy. Overall, it is expected that the work reported in the thesis will furnish useful guidelines for (i) performing the SASW and MASW field tests, (ii) generating dispersion plots/images, and (iii) predicting the shear wave velocity profile of the site based on an inversion analysis.