An improved model that uses a combination of genetic algorithms and neural networks can also be found to be useful for solving the problem of checking the seismic design values. For more specific areas in Taiwan, the seismic key element, that is, PGA, can be estimated using neural network models trained on a series of historical seismic recorded data. Therefore, recently developed techniques in the field of computational intelligence, including neural networks and genetic algorithms, may be a better alternative for solving earthquake-related problems around the world because of their simplicity and effectiveness. However, the first method often involves very tedious calculations, and the second method must assume a function in advance. The conventional methods of using seismic parameters to evaluate the potential damage of earthquakes are primarily based on vibration analysis and regression analysis. The present study focuses on the topic of using seismic recorded parameters and site soil conditions to evaluate the potential damage resulting from ground strong motions. There exist various types of earthquake problems a typical case study for estimating peak ground acceleration (PGA) and a detailed review of recent efforts in predictions can be seen in the previous literatures. These design values can be used to calculate earthquake force and should be examined as often as possible to determine their fit with actual conditions, either from a practical viewpoint or academic viewpoint. After a few times of revisions and adjustments, the current building code in Taiwan classifies the entire island into two zones: the earthquake area coefficient of horizontal acceleration is 0.33 g for Zone A and 0.23 g for Zone B. Because earthquakes occur frequently in this area, this factor must be taken into account in structural analysis and design. Taiwan is an island located in the circum-Pacific seismic zone, sometimes called the Ring of Fire. Seismic design values play an important role in constructing buildings to comply with regional safety standards that consider the effects of strong ground motions. ![]() The results of this study provide an insight into this type of nonlinear problem, and the proposed method may be applicable to other areas of interest around the world. This equation represents seismic characteristics in Taiwan region more reliably and reasonably. Finally, this study develops a new equation for the relationship of horizontal peak ground acceleration and focal distance by the curve fitting method. Four locations identified to have higher estimated peak ground accelerations than that of the seismic design value in the 24 subdivision zones are investigated in Taiwan. This study further develops a new weight-based neural network model for estimating peak ground acceleration at unchecked sites. Initial comparison results show that a neural network model with three neurons in the hidden layer can achieve relatively better performance based on the evaluation index of correlation coefficient or mean square error. This study proposes an improved computational neural network model that uses three seismic parameters (i.e., local magnitude, epicentral distance, and epicenter depth) and two geological conditions (i.e., shear wave velocity and standard penetration test value) as the inputs for predicting peak ground acceleration-the key element for evaluating earthquake response.
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