No abstract

In this article, seismic zoning maps of Golestan based on modified methodology of probabilistic seismic hazard analysis have been prepared. For this purpose, first, major seismotectonic provinces of Iran surrounding the study region are delineated and seismicity parameters are evaluated for each province. By determining the main active faults of the region and preparing the earthquake catalog containing 19 historical events and 270 instrumental events, 25 potential seismic sources are modelled as area sources in the region. In order to properly reflect the inhomogeneity of seismicity in time and space, and to avoid underestimation of potential hazard of large magnitude earthquakes, the annual mean occurrence rate of earthquakes in each seismotectonic province should be allocated to each magnitude interval in the corresponding potential seismic sources, using the spatial distribution function. In this research, different kinds of seismological, tectonic and geophysical data are used to indicate the possible future earthquake activities in the interest region, providing basis for evaluation of spatial distribution function. Seismic hazard assessment is carried out by considering a universal attenuation relationship and using the SEISRISKIII computer program for the area in a radius of 200 km from centre of Gorgan (the centre of Golestan province). The study region is divided into a series of grid points and seismic hazard analysis for every grid point is carried out using characteristics of seismic activity in seismotectonic provinces and potential seismic sources. Horizontal Peak Ground Acceleration (PGA) for different seismic hazard levels is evaluated by modified probabilistic estimation. Finally, seismic hazard zoning maps of Golestan for 10% Probability of exceedance (PE) and 2% PE in 50 years and for three types of the site soil are prepared and compared with the seismic hazard macrozonation of Iran based on standard 2800.

This study presents a methodology that utilizes a new combination of two compressed damage indices as input data of an artificial neural network (ANN) ensemble to detect multi-damages in the braces of cold formed steel shear walls. To identify an efficient input data for ANN, first, three main groups of damage indices are considered: modal parameter-based damage indices; frequency response functions (FRFs)-based damage indices and time series-based damage indices. Furthermore, principal component analysis (PCA) technique is applied to reduce the dimensions of FRFs and time series-based input pattern. By a sensitivity study, two suitable damage indices of PCA-compressed time series data and PCA-compressed FRFs are identified and then combined to produce a new efficient input data for a hierarchy of ANN ensembles. The numerical results show that the ANN ensemble-based damage detection approach with the proposed collection of two damage indices is effective and reliable.