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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.

Site characterization is an important task in Geotechnical Engineering. In situ tests based on standard penetration test (SPT), cone penetration test (CPT) and shear wave velocity survey are popular among geotechnical engineers. Site characterization using any of these properties based on finite number of in-situ test data is an imperative task in probabilistic site characterization. These methods have been used to design future soil sampling programs for the site and to specify the soil stratification. It is never possible to know the geotechnical properties at every location beneath an actual site because, in order to do so, one would need to sample and/or test the entire subsurface profile. Therefore, the main objective of site characterization models is to predict the subsurface soil properties with minimum in-situ test data. The prediction of soil property is a difficult task due to the uncertainities. Spatial variability, measurement ‘noise’, measurement and model bias, and statistical error due to limited measurements are the sources of uncertainities. Liquefaction in soil is one of the other major problems in geotechnical earthquake engineering. It is defined as the transformation of a granular material from a solid to a liquefied state as a consequence of increased pore-water pressure and reduced effective stress. The generation of excess pore pressure under undrained loading conditions is a hallmark of all liquefaction phenomena. This phenomena was brought to the attention of engineers more so after Niigata(1964) and Alaska(1964) earthquakes. Liquefaction will cause building settlement or tipping, sand boils, ground cracks, landslides, dam instability, highway embankment failures, or other hazards. Such damages are generally of great concern to public safety and are of economic significance. Site-spefific evaluation of liquefaction susceptibility of sandy and silty soils is a first step in liquefaction hazard assessment. Many methods (intelligent models and simple methods as suggested by Seed and Idriss, 1971) have been suggested to evaluate liquefaction susceptibility based on the large data from the sites where soil has been liquefied / not liquefied. The rapid advance in information processing systems in recent decades directed engineering research towards the development of intelligent models that can model natural phenomena automatically. In intelligent model, a process of training is used to build up a model of the particular system, from which it is hoped to deduce responses of the system for situations that have yet to be observed. Intelligent models learn the input output relationship from the data itself. The quantity and quality of the data govern the performance of intelligent model. The objective of this study is to develop intelligent models [geostatistic, artificial neural network(ANN) and support vector machine(SVM)] to estimate corrected standard penetration test (SPT) value, Nc, in the three dimensional (3D) subsurface of Bangalore. The database consists of 766 boreholes spread over a 220 sq km area, with several SPT N values (uncorrected blow counts) in each of them. There are total 3015 N values in the 3D subsurface of Bangalore. To get the corrected blow counts, Nc, various corrections such as for overburden stress, size of borehole, type of sampler, hammer energy and length of connecting rod have been applied on the raw N values. Using a large database of Nc values in the 3D subsurface of Bangalore, three geostatistical models (simple kriging, ordinary kriging and disjunctive kriging) have been developed. Simple and ordinary kriging produces linear estimator whereas, disjunctive kriging produces nonlinear estimator. The knowledge of the semivariogram of the Nc data is used in the kriging theory to estimate the values at points in the subsurface of Bangalore where field measurements are not available. The capability of disjunctive kriging to be a nonlinear estimator and an estimator of the conditional probability is explored. A cross validation (Q1 and Q2) analysis is also done for the developed simple, ordinary and disjunctive kriging model. The result indicates that the performance of the disjunctive kriging model is better than simple as well as ordinary kriging model. This study also describes two ANN modelling techniques applied to predict Nc data at any point in the 3D subsurface of Bangalore. The first technique uses four layered feed-forward backpropagation (BP) model to approximate the function, Nc=f(x, y, z) where x, y, z are the coordinates of the 3D subsurface of Bangalore. The second technique uses generalized regression neural network (GRNN) that is trained with suitable spread(s) to approximate the function, Nc=f(x, y, z). In this BP model, the transfer function used in first and second hidden layer is tansig and logsig respectively. The logsig transfer function is used in the output layer. The maximum epoch has been set to 30000. A Levenberg-Marquardt algorithm has been used for BP model. The performance of the models obtained using both techniques is assessed in terms of prediction accuracy. BP ANN model outperforms GRNN model and all kriging models. SVM model, which is firmly based on the theory of statistical learning theory, uses regression technique by introducing -insensitive loss function has been also adopted to predict Nc data at any point in 3D subsurface of Bangalore. The SVM implements the structural risk minimization principle (SRMP), which has been shown to be superior to the more traditional empirical risk minimization principle (ERMP) employed by many of the other modelling techniques. The present study also highlights the capability of SVM over the developed geostatistic models (simple kriging, ordinary kriging and disjunctive kriging) and ANN models. Further in this thesis, Liquefaction susceptibility is evaluated from SPT, CPT and Vs data using BP-ANN and SVM. Intelligent models (based on ANN and SVM) are developed for prediction of liquefaction susceptibility using SPT data from the 1999 Chi-Chi earthquake, Taiwan. Two models (MODEL I and MODEL II) are developed. The SPT data from the work of Hwang and Yang (2001) has been used for this purpose. In MODEL I, cyclic stress ratio (CSR) and corrected SPT values (N1)60 have been used for prediction of liquefaction susceptibility. In MODEL II, only peak ground acceleration (PGA) and (N1)60 have been used for prediction of liquefaction susceptibility. Further, the generalization capability of the MODEL II has been examined using different case histories available globally (global SPT data) from the work of Goh (1994). This study also examines the capabilities of ANN and SVM to predict the liquefaction susceptibility of soils from CPT data obtained from the 1999 Chi-Chi earthquake, Taiwan. For determination of liquefaction susceptibility, both ANN and SVM use the classification technique. The CPT data has been taken from the work of Ku et al.(2004). In MODEL I, cone tip resistance (qc) and CSR values have been used for prediction of liquefaction susceptibility (using both ANN and SVM). In MODEL II, only PGA and qc have been used for prediction of liquefaction susceptibility. Further, developed MODEL II has been also applied to different case histories available globally (global CPT data) from the work of Goh (1996). Intelligent models (ANN and SVM) have been also adopted for liquefaction susceptibility prediction based on shear wave velocity (Vs). The Vs data has been collected from the work of Andrus and Stokoe (1997). The same procedures (as in SPT and CPT) have been applied for Vs also. SVM outperforms ANN model for all three models based on SPT, CPT and Vs data. CPT method gives better result than SPT and Vs for both ANN and SVM models. For CPT and SPT, two input parameters {PGA and qc or (N1)60} are sufficient input parameters to determine the liquefaction susceptibility using SVM model. In this study, an attempt has also been made to evaluate geotechnical site characterization by carrying out in situ tests using different in situ techniques such as CPT, SPT and multi channel analysis of surface wave (MASW) techniques. For this purpose a typical site was selected wherein a man made homogeneous embankment and as well natural ground has been met. For this typical site, in situ tests (SPT, CPT and MASW) have been carried out in different ground conditions and the obtained test results are compared. Three CPT continuous test profiles, fifty-four SPT tests and nine MASW test profiles with depth have been carried out for the selected site covering both homogeneous embankment and natural ground. Relationships have been developed between Vs, (N1)60 and qc values for this specific site. From the limited test results, it was found that there is a good correlation between qc and Vs. Liquefaction susceptibility is evaluated using the in situ test data from (N1)60, qc and Vs using ANN and SVM models. It has been shown to compare well with “Idriss and Boulanger, 2004” approach based on SPT test data. SVM model has been also adopted to determine over consolidation ratio (OCR) based on piezocone data. Sensitivity analysis has been performed to investigate the relative importance of each of the input parameters. SVM model outperforms all the available methods for OCR prediction.