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Considering the uncertainties associated with earthquake, seismic response of structures, damage caused by the response and the repair costs, determining Earthquake Insurance Premium (EIP) is a challenging task that requires a framework for modeling all inherent risks. Most of the models proposed for EIP determination have two basic components. The first component indicates the probability of seismic hazard (generally obtained from the probabilistic seismic hazard analysis), and the second component indicates the probability of occurrence of a given damage to the structure with respect to different seismic hazard levels (generally obtained from the fragility curves). These two components are interconnected through an intermediate parameter, known as the Intensity Measure (IM), which is of great importance in calculations and has hardly been considered so far. In this study, the dependency of EIP on the IM used in the probabilistic seismic hazard analysis and the fragility curves is analyzed. To this end, the EIP was determined for five types of buildings at low, medium and high seismic hazard levels using two IMs (peak ground acceleration and first mode spectral acceleration). The results of this study warn that the EIP can be highly dependent on the IM, so that for all studied structures, the EIP determined based on peak ground acceleration at a high seismic hazard level is nearly three times as many as the one determined by the first mode spectral acceleration. A significant finding is that when the first mode spectral acceleration is used as the IM, the ratio of EIP at different seismic hazard levels closely matches the ratio of IM at the same levels. This result can be valuable in developing insurance codes and regulations. However, this is not the case for the currently used IM, i.e., peak ground acceleration.

Every year, the phenomenon of earthquake causes a lot of human, financial and environmental losses. Transmission pipelines are one of the vital arteries that are very important, however, in the event of an earthquake can cause devastating damages. Safeguarding urban and interurban facilities, including electricity, water supply, oil and gas transmission lines, against these loads requires careful studies and engineering designs. Given that traditional methods for seismic design of pipelines such as FEM modeling and experimental methods are so expensive, a new combined method for predicting the strain of pipes based on the Artificial Neural Network (ANN) is proposed. For this purpose, the parameters of the pipeline including pipe and soil type, length, discharge, path slope, depth, etc. and earthquake-induced characteristics including earthquake acceleration, earthquake occurrence time, Peak ground acceleration (PGA), etc. were included in the model. Earthquake input parameters were considered as input parameters and pipe strains and stresses were considered as output parameter. ANSYS finite element software has also been used to simulate the pipeline and produce training data. The results of finite element software were used as input and output parameters for training and validating artificial neural network. 753 models created using ANSYS and its input/output data divided into three parts to creat ANN model. 70% of the total data were used for training, 15% for validating and 15% for testing the ANN model. Results show that the proposed Method provides a very good agreement with the computational results of the ANSYS with accuracy of 96 percent.

Careful estimation of global ductility will certainly lead to greater accuracy in the design of structural members. In this paper, a new and optimal intelligent model is proposed to predict the roof ductility (μR) of EBF steel frames exposed to the near-fault pulse-like earthquakes, using the Adaptive Neuro-Fuzzy Inference System (ANFIS). To achieve this goal, a databank consisting of 12960 data is created. To establish different geometrical properties of models, 3-,6-, 9-, 12-, 15, 20-stories, steel EBF frames are considered with 3 different types of link beam, column stiffness, and brace slenderness. All models are analysed to reach 4 different performance levels using nonlinear time history under 20 near-fault earthquakes. About 6769 data are applied as ANFIS training data. Subtractive clustering and Fuzzy C-Mean clustering (FCM) methods are applied to generate the purposed model. The results show that FCM provides more accurate outcomes. Moreover, to validate the model, 2257 data are applied (as test data) in order to calculate the correlation coefficient (R) and mean squared error (MSE) between the predicted values of (μR) and the real values. The results of correlation analysis show the high accuracy of the proposed intelligent model.

The most important feature of the behavior factor is that it allows the structural designer to be able to evaluate the structural seismic demand, using an elastic analysis, based on force-based principles quickly. In most seismic codes, this coefficient is merely dependent on the type of lateral resistance system and is introduced with a fixed number. However, there is a relationship between the behavior factor, ductility (performance level), structural geometric properties, and type of earthquake (near and far). In this paper, a new and accurate correlation is attempted to predict the behavior factor (q) of EBF steel frames, under near-fault earthquakes, using the genetic algorithm (GA). For this purpose, a databank consisting of 12960 data is created. To establish different geometrical properties of models, 3−, 6−, 9−, 12−, 15, and 20− story steel EBF frames were considered with 3 different types of link beam, 3 different types of column stiffness, and 3 different types of brace slenderness. Using nonlinear time history under 20 near-fault earthquake, all models were analyzed to reach 4 different performance levels. 6769 data were used as GA training data. Moreover, to validate the correlation, 2257 data were used as test data for calculating mean squared error (MSE) and correlation coefficient (R) between the predicted values of (q) and the real values. In addition, the MSE and R were calculated for correlation in the train and test data. Also, the comparison of the response of maximum inelastic displacement of 5 stories EBF from the proposed correlation and the mean inelastic time-history analysis confirms the accuracy of the estimate relationship.