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Ground motion predictive modelling based on genetic algorithms

机译：基于遗传算法的地震动预测建模

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This study aims to utilise genetic algorithms for the estimation of peak ground accelerations (PGA). A case study is carried out for the earthquake data from south-west Turkey. The input parameters used for the development of attenuation relationship are magnitude, depth of earthquake, epicentral distance, average shear wave velocity and slope height of the site. Earthquake database compiled by the Earthquake Research Institute of Turkey was used for model development. An important contribution to this study is the slope/hill data included into the dataset. Developed empirical model has a good correlation (R = 0.78 and 0.75 for the training and overall datasets) between measured and estimated PGA values. The proposed model is also compared with local empirical predictive models and its results are found to be reasonable. The slope-hill effect found to be an important parameter for the estimation of PGA.
机译：这项研究旨在利用遗传算法估算地面加速度峰值（PGA）。对来自土耳其西南部的地震数据进行了案例研究。用于建立衰减关系的输入参数是震级，地震深度，震中距离，平均剪切波速度和场地的坡度。由土耳其地震研究所汇编的地震数据库用于模型开发。这项研究的重要贡献是数据集中包含的坡度/坡度数据。已开发的经验模型在测得的PGA值和估计的PGA值之间具有良好的相关性（对于训练和整体数据集，R = 0.78和0.75）。将该模型与当地经验预测模型进行比较，发现其结果是合理的。发现坡度-坡度效应是估算PGA的重要参数。

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