Combined analysis of the geomorphic evolution of Greymouth with Multi-channel Analysis of Surface Waves (MASW) provides new insight into the geotechnical implications of reclamation work. The MASW method utilises the frequency dependent velocity (dispersion) of planar Rayleigh waves created by a seismic source as a way of assessing the stiffness of the subsurface material. The surface wave is inverted to calculate a shear wave velocity (Park et al., 1999). Once corrected, these shear-wave (Vs) velocities can be used to obtain a factor of safety for liquefaction susceptibility based on a design earthquake. The primary study site was the township of Greymouth, on the West Coast of New Zealand’s South Island. Greymouth is built on geologically young (Holocene-age) deposits of beach and river sands and gravels, and estuarine and lagoonal silts (Dowrick et al., 2004). Greymouth is also in a tectonically active region, with the high seismic hazard imposed by the Alpine Fault and other nearby faults, along with the age and type of sediment, mean the probability of liquefaction occurring is high particularly for the low-lying areas around the estuary and coastline. Repeated mapping over 150 years shows that the geomorphology of the Greymouth Township has been heavily modified during that timeframe, with both anthropogenic and natural processes developing the land into its current form. Identification of changes in the landscape was based on historical maps for the area and interpreting them to be either anthropogenic or natural changes, such as reclamation work or removal of material through natural events. This study focuses on the effect that anthropogenic and natural geomorphic processes have on the stiffness of subsurface material and its liquefaction susceptibility for three different design earthquake events. Areas of natural ground and areas of reclaimed land, with differing ages, were investigated through the use of the MASW method, allowing an initial estimation of the relationship between landscape modification and liquefaction susceptibility. The susceptibility to liquefaction of these different materials is important to critical infrastructure, such as the St. John Ambulance Building and Greymouth Aerodrome, which must remain functional following an earthquake. Areas of early reclamation at the Greymouth Aerodrome site have factors of safety less than 1 and will liquefy in most plausible earthquake scenarios, although the majority of the runway has a high factor of safety and should resist liquefaction. The land west of the St. John’s building has slightly to moderately positive factors of safety. Other areas have factors of safety that reflect the different geology and reclamation history.

Recent ground failure case histories after 1994 Northridge, 1999 Kocaeli and 1999 Chi-Chi earthquakes revealed that low-plasticity silt-clay mixtures generate significant cyclic pore pressures and can exhibit a strain-softening response, which may cause significant damage to overlying structural systems. These observations accelerated research studies on liquefaction susceptibility of fine-grained soils. Alternative approaches to Chinese Criteria were proposed by several researchers (Seed et al. 2003, Bray and Sancio 2006, Boulanger and Idriss 2006) most of which assess liquefaction triggering potential based on cyclic test results compared on the basis of index properties of soils (such as LL, PI, LI, wc/LL). Although these new methodologies are judged to be major improvements over Chinese Criteria, still there exist unclear issues regarding if and how reliably these methods can be used for the assessment of liquefaction triggering potential of fine grained soils. In this study, results of cyclic tests performed on undisturbed specimens (ML, CL, MH and CH) were used to study cyclic shear strain and excess pore water pressure generation response of fine-grained soils. Based on comparisons with the cyclic response of saturated clean sands, a shift in pore pressure ratio (ru) vs. shear strain response is observed, which is identified to be a function of PI, LL and (wc/LL). Within the confines of this study, i) probabilistically based boundary curves identifying liquefaction triggering potential in the ru vs. shear strain domain were proposed as a function of PI, LL and (wc/LL), ii) these boundaries were then mapped on to the normalized net tip resistance (qt,1,net) vs. friction ratio (FR) domain, consistent with the work of Cetin and Ozan (2009). The proposed framework enabled both Atterberg limits and CPT based assessment of liquefaction triggering potential of fine grained low plasticity soils, differentiating clearly both cyclic mobility and liquefaction responses.

Although silt and clay mixtures were mostly considered to be resistant to cyclic loading due to cohesional components of their shear strength, ground failure case histories compiled from fine grained soil profiles after recent earthquakes (e.g. 1994 Northridge, 1999 Adapazari, 1999 Chi-Chi) revealed that the responses of low plasticity silt and clay mixtures are also critical under cyclic loading. Consequently, understanding the cyclic response of these soils has become a recent challenge in geotechnical earthquake engineering practice. While most of the current attention focuses on the assessment of liquefaction susceptibility of fine-grained soils, it is believed that cyclic strain and strength assessments of silt and clay mixtures need to be also studied as part of complementary critical research components. Inspired by these gaps, a comprehensive laboratory testing program was designed. As part of the laboratory testing program 64 stress-controlled cyclic triaxial tests, 59 static strain-controlled consolidated undrained triaxial tests, 17 oedometer, 196 soil classification tests including sieve analyses, hydrometer, and consistency tests were performed. Additionally 116 cyclic triaxial test results were compiled from available literature. Based on this data probability-based semi-empirical models were developed to assess liquefaction susceptibility and cyclic-induced shear strength loss, cyclically-induced maximum shear, post-cyclic volumetric and residual shear strains of silt and clay mixtures. Performance comparisons of the proposed model alternatives were studied, and it is shown that the proposed models follow an unbiased trend and produce superior predictions of the observed laboratory test response. Superiority of the proposed alternative models was proven by relatively smaller model errors (residuals).

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.