Abstract
Abstract. Many scholars have used microtremor applications to evaluate the vulnerability index. In order to reach fast and reliable results, microtremor measurement is preferred as it is a cost-effective method. In this paper, the vulnerability index will be reviewed by utilization of microtremor measurement results in Nicosia city. 100 measurement stations have been used to collect microtremor data and the data were analysed by using Nakamura’s method. The value of vulnerability index (Kg) has been evaluated by using the fundamental frequency and amplification factor. The results obtained by the artificial neural network (ANN) will be compared with microtremor measurements. Vulnerability Index Assessment using Neural Networks (VIANN) is a backpropagation neural network, which uses the original input microtremor Horizontal Vertical Spectrum Ratio (HVSR) spectrum set. A 3-layer back propagation neural network which contains 4096 input, 28 hidden and 3 output neurons are used in this suggested system. The output neurons are classified according to acceleration sensitivity zone, velocity zones, or displacement zones. The sites are classified by their vulnerability index values using binary coding: [1 0 0] for the acceleration sensitive zone, [0 1 0] for the velocity sensitive zone, and [0 0 1] for the displacement sensitive zone.
Highlights
Earthquake is a common natural disaster, which can directly affect human lives
The parameters, such as fundamental frequency (F0), predominant period (T0), amplitude or the amplification value (HVSR) of the site are given in the data catalogue that is selected for artificial neural network
The ground vibrations as we called microtremors have amplitude values vary between 0.1 and 1 micron and velocity amplitude changing between 0.001 and 0.01 cm s-1.Microtremors can be classified in two major groups according to their frequency values: frequency values greater than 1 Hz known as “short period” which refer to shallow soil layers
Summary
Earthquake is a common natural disaster, which can directly affect human lives. Earthquakes exemplify a massive natural hazard, resulting in casualties and major damage to buildings and businesses. The output results were compared with available nonlinear regression analysis in their study (Kert and Chu, 2002) According to their opinion the present neural networks model has better results than the others. The parameters, such as fundamental frequency (F0), predominant period (T0), amplitude or the amplification value (HVSR) of the site are given in the data catalogue that is selected for artificial neural network. The authors claimed that to determine the intrinsic structure of the data and to test the efficiency of our parametrization strategy, it has been analysed the pre-processed data using an unsupervised neural method The authors applied this method to the entire dataset composed of landslide, microtremor, and explosion-quake signals. PGA values of FFBP were modified by the regression analysis in order to improve prediction performance
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