عنوان مقاله [English]
This paper proposes attenuation relationships for peak ground acceleration (PGA) for horizontal and vertical components of earthquakes in the Iranian plateau. So far, many attenuation relationships have been presented based on several parameters. Attenuation relationships were, initially, simple functions of a few variables, but gradually became more and more sophisticated over time as the number and accuracy of records and the use of computer calculations increased. Ground motion during an earthquake depends mainly on source mechanics, magnitude, local geology, surface topography, source to site distance and the dynamic properties of the material propagation. However, in this research, because of lack of information in Iran, only the magnitude of the earthquake, the distance between the earthquake source and the location and the ground type are used as important factors. Surface wave magnitude (Ms) is used in this research, since the occurred and reported earthquakes in Iran are shallow. Furthermore, hypocentral distance is considered as the distance between the earthquake source and the location. The Iranian plateau is divided into two seismic zones: Alborz-central Iran and Zagros. From a ground-type point of view, the records of each region can be subdivided into two parts. Therefore, all records are grouped into four categories: 1) Alborz and central Iran-rock ground type, 2) Alborz and central Iran-soil ground type, 3) Zagros-rock ground type, and 4) Zagros-soil ground type. In this regard, a majority of available catalogs relating to 490 seismic events in Iran have been gathered, out of which, 954 records are used. These include 493 records related to Alborz and central Iran and the rest associated with the Zagros region. These records comprise earthquakes with a surface wave magnitude greater, or equal to, 4, with a hypocentral distance greater than 5 km (and often less than 200 km). To obtain the attenuation relationships for peak ground acceleration, a Gene Expression Programming (GEP) algorithm is used instead of the conventional constant regression model. The model is extracted smartly as a continuous period function. The results show a consistency with a high proportionality coefficient among the observed and anticipated results.