# Predicting Geotechnical Parameters from Seismic Wave Velocity Using Artificial Neural Networks
## 1 Introduction
Pressure Meter Test (PMT), SPT, Arterberg limit tests, and dry unit weight data were also collected from the Salt River Project (SRP) [23; 24]. [PERSON] and [PERSON] developed correlations between S-wave velocity, cone penetration parameters, undrained shear strength, and 1-D compression parameters for Norwegian clay [25]. Data used for this research was collected from 29 sites; in south-eastern and mid-Norway. Regression analyses were performed to establish the correlation between in situ S-wave velocity (\(\mathrm{V_{s}}\)) and cone net resistance (\(\mathrm{q_{net}}\)), collected from the cone penetration test. The coefficient of determination R\({}^{2}\) was 0.73. The undrained shear strength values obtained from direct shear tests were correlated with \(\mathrm{V_{s}}\). with a regression coefficient (R\({}^{2}\)) of 0.91. Their analysis also showed a good correlation between pre-consolidation stress (\(\mathrm{P_{c}}\)) and \(\mathrm{V_{s}}\) with an R\({}^{2}\) value of 0.81. [PERSON] developed ANN models to predict geotechnical parameters from S-wave and P-wave velocity separately using laboratory data for compacted clay and sandy clay soil [26]. The results indicated that P-wave velocity and S-wave velocity were more sensitive to dry density and void ratio than to saturation and water content. The performance of the ANN models to predict geotechnical parameters from soil mix proportion and either P-wave or S-wave velocity was better when multiple geotechnical parameters were predicted at a time. Empirical correlations were developed by [PERSON] et al. between index properties and seismic velocities [27]. [PERSON] and [PERSON] used velocity data published by [PERSON] and showed the dependency of porosity with S-wave and P-wave velocity [28; 29]. [PERSON] and [PERSON] showed a direct positive linear correlation exists between seismic wave velocity and cohesion and shear strength for compacted sand-kaolin mixtures. They also attempted to establish correlation between seismic wave velocity and friction angle but found that it is insignificant [30]. [PERSON] et al. developed correlations between shear wave velocity with vertical effective stress, unit weight, preconsolidation stress and undrained shear strength for clay type soil [31]. ANN is gaining popularity as a problem-solving tool in the field of civil engineering. Researchers are using ANN to predict concrete compressive strength [32; 33; 34], ultrasonic pulse velocity [35], slump of concrete [36; 37; 38]. [PERSON] showed the application of ANN to assess the nonlinear behavior of steel structures [39]. ANN was used to forecast flexure and initial stiffness of beam column joints [40]. In geotechnical engineering ANN was used to study slope stability [41], pile analysis [42; 43], developing correlation between ER and geotechnical parameters [44], analysis of liquefaction potential [45; 46]. In transportation engineering researchers applied ANN to develop transportation systems [47]. ANN can solve complex problems, but the performance depends on the size and accuracy of the data set. Using a big data set can help to train the network efficiently. In many fields big data sets are not always easily available. Researchers are working on developing ANN models using small data sets. [PERSON] described a particular neural network tool which is capable of handling small data sets and its application to a specific case study [48]. [PERSON] et al. used deep neural network to predict material defects using small data set [49]. Literature contains a good number of correlations between seismic wave velocity and blow counts. There are fewer correlations with other important geotechnical parameters, such as water content, dry density, cohesion, angle of friction, saturation, void ratio, etc. More study is necessary to establish the correlation between seismic wave velocity and different types of geotechnical parameters.