Geospatial technology is increasing in demand for many applications in geosciences. Spatial variability of the bed/hard rock
is vital for many applications in geotechnical and earthquake engineering problems such as design of deep foundations, site
amplification, ground response studies, liquefaction, microzonation etc. In this paper, reduced level of rock at Bangalore,
India is arrived from the 652 boreholes data in the area covering 220 km2. In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability
of the rock depth, Geostatistical model based on Ordinary Kriging technique, Artificial Neural Network (ANN) and Support Vector
Machine (SVM) models have been developed. In Ordinary Kriging, the knowledge of the semi-variogram of the reduced level of
rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of the Bangalore,
where field measurements are not available. A new type of cross-validation analysis developed proves the robustness of the
Ordinary Kriging model. ANN model based on multi layer perceptrons (MLPs) that are trained with Levenberg–Marquardt backpropagation
algorithm has been adopted to train the model with 90% of the data available. The SVM is a novel type of learning machine
based on statistical learning theory, uses regression technique by introducing loss function has been used to predict the
reduced level of rock from a large set of data. In this study, a comparative study of three numerical models to predict reduced
level of rock has been presented and discussed. 相似文献
This study pertains to prediction of liquefaction susceptibility of unconsolidated sediments using artificial neural network
(ANN) as a prediction model. The backpropagation neural network was trained, tested, and validated with 23 datasets comprising
parameters such as cyclic resistance ratio (CRR), cyclic stress ratio (CSR), liquefaction severity index (LSI), and liquefaction
sensitivity index (LSeI). The network was also trained to predict the CRR values from LSI, LSeI, and CSR values. The predicted
results were comparable with the field data on CRR and liquefaction severity. Thus, this study indicates the potentiality
of the ANN technique in mapping the liquefaction susceptibility of the area. 相似文献
The paper deals with an application of neural networks for detection of natural periods of vibrations of prefabricated, medium height buildings. The neural network technique is also used to simulate the dynamic response at selected floor of one of the analysed buildings subject to seismic loading induced by explosives in a nearby quarry. Both the training and testing patterns were formulated on the basis of measurements performed on actual structures. The results of neural network identification of natural periods of the considered buildings obtained with different soil, geometrical and stiffness parameters are compared with the results of experiments. The application of back-propagation neural networks enables us to identify the natural periods of the buildings with accuracy quite satisfactory for engineering practice. The experimental and generated data of vibration displacements are compared and much clearer comparison is given on the phase plane: displacements versus velocities. It was stated that a good generalization takes place both with respect to displacements and velocities. 相似文献