In statistics it is often assumed that sample observations are independent. But sometimes in practice, observations are somehow dependent on each other. Spatiotemporal data are dependent data which their correlation is due to their spatiotemporal locations. Spatiotemporal models arise whenever data are collected across both time and space. Therefore such models have to be analyzed in terms of their spatial and temporal structure. Usually a spatiotemporal random field {Z(s, t) : (s, t) Î D x T} is used for modeling the spatiotemporal data, where D Ì Rd, d ³ 1 is a space region and T Í R is a time region. One of the fundamental subjects in analyzing such data is prediction. In spatial statistics, assuming that the spatiotemporal random field Z(s,.t) is stationary with finite variance at all coordinates (s, t) Î D x T, and spatiotemporal covariance function C(h, u) = cov (Z(s, t)j Z(s + h, t + u)) exists, the unknown value of the random field at a given location (s0, t0) is usually predicted with kriging as the best linear unbiased predictor. In practice, the spatiotemporal covariance function is unknown and a positive definite function should be fitted to the estimates of the covariance function. To ensure that a valid spatiotemporal covariance model is fitted to the data, one usually considers a parametric family whose members are known to be separable positive definite functions. A separable spatiotemporal covariance function might decompose into sum or product of a purely spatial and a purely temporal covariance function. In this paper the product-sum model introduced by De Iaco et al. (2001) is used to determine the spatiotemporal correlation of the data.In some applied problems, in addition to the values of an attribute of interest Z(0, 0), some additional information is available in each sample location, so the precision of prediction would be improved by their implementation. In this paper, to exploit this additional information in kriging, two techniques for spatiotemporal kriging of temperature are compared. The first technique, spatiotemporal ordinary kriging, is the simplest of the two, and uses only information about temperature. The second technique, spatiotemporal kriging with external drift, uses also the relationship between temperature and height to aid the interpolation. It is shown that the behavior of the temperature predictions is physically more realistic when using spatiotemporal kriging with external drift. The implementation of spatiotemporal kriging with external drift, then, is illustrated in a real problem, consisting of maximum and minimum temperature of 6 provinces in Iran.

The effect of aftershocks on structures are not usually considered in seismic design codes. In addition to mainshock events, aftershocks can cause major damage to structures, especially to mainshock-damaged structures. Analysis of the characteristics of the mainshock, foreshocks, and aftershocks reveal differences in the ground motion parameters. Structures may undergo a variety of seismic waves with different characteristics that can increase the chance of seismic amplification. The present study examined the effects of aftershock as well as foreshocks events on the response of single degree-of-freedom (SDOF) systems with nonlinear behavior. This allowed inclusion of possible differences during calculation of the response spectrum for cases having foreshock and aftershock effects and those excluding these effects. To this end, 38 mainshocks from different seismic regions with moment magnitudes (Mw) greater than 3.5 were used. More than 168 time acceleration histories from mainshock, aftershock, and foreshock events were applied to evaluate the effects of aftershocks and foreshocks on the response spectrum. The parameters of post-yield stiffness ratio (hardening and softening), ductility factor, period, and site classification were taken into account during 121,000 nonlinear analyses on 60 SDOF models. The results show that the aftershocks as well as foreshocks have a significant effect on the response spectrum, increasing the structural response. Consequently, the effect of aftershocks must be considered in the development of design spectra in seismic codes and guidelines.

Statistical Shape Models are used to interpret shapes. They include mean and variance of corresponding points of training shapes. One of the most important challenges in building statistical shape models is to establish correct correspondences among landmarks in a training set. In this paper, the non-rigid CPD (Coherent Point drift) method is used to find correct correspondences among points. This method uses both Deterministic Annealing and a non-rigid scheme to register two shapes simultaneously. Then, the statistical shape model is built using a rigid transformation. The proposed method is evaluated using Compactness, Generalization ability and Specificity measures. The built model is compared to models created using the ICP (Iterative Closest Point), TPS-RPM (Thin Plate Spline – Robust Point Matching) and MDL (Minimum Descreption Length) methods by these metrics. The results show that the proposed method performs like the MDL regarding Specificity measure (0.21±0.06). The Compactness and Generalization ability measures of the proposed method are very similar to those for the MDL method. The run-time of our proposed method is about 68 seconds which is faster than non-rigid TPS-RPM and MDL approaches (390 and 3600 seconds respectively). Our results are superior to the ICP and TPS-RPM algorithms.

Over the past few years, unstiffened steel plate shear (SPSW) has been used as a lateral load resisting system, due to its proper behavior against earthquake. The designing procedure in seismic codes for these systems is still based on the elastic force-based method. Nowadays, the Inelastic behavior in these methods is gradually being replaced with performance-based seismic design (PBPD) methods. One of these methods is performance based plastic design (PBPD). The design concept in this methodology is based on performing a desired yield mechanism in the structure and an Inelastic target displacement. In this procedure, determination of the Inelastic design base shear structure requires basic assumptions and an iterative process to achieve the intended performance (target displacement). One important basic design parameter is to determine the initial structure yield drift which has been associated with some inadequacy in most studies.In this paper, the design methodology for an unstiffened steel plate shear wall system has been developed, and a simple method to determine the amount of structure yield drift has also been proposed.This design methodology had already been checked for 6 structures with five and ten stories and 3 different target ductility values (2, 3 and 4) under a design level earthquake, based on the Iranian earthquake code (Standard 2800). Inelastic static analysis (Pushover) of designed structures based on this method reveals that the actual ductility of structures is close to the assumed target ductility and the actual yield mechanism is the same as the assumed yield mechanism. Based on the test results, the yield drift value is different in each structure, and, to achieve the correct design, iteration methods are essential to yield drift convergence in the design process. In addition, results showed that the proposed method for determination of structure yield drift is very quick and reliable for use in the design procedure.

In this paper, a new method is proposed for identification of Inelastic shear frame structures with hesteresis, using data collected on their dynamic response. It uses the Prandtl-Ishlinskii rate independent model for hysteresis, which was originally used in the field of plasticity and ferromagnetism. The proposed identification method is capable of identifying the mass, damping and restoring force of a frame structure, which can be used in forming the equations of motion of the frame. By solving the equations of motion, the dynamic response is predicted. The method is based on the combined use of Quadratic Programming (QP) and Genetic Algorithms (GA). First, assuming a set of Prandtl-Ishlinskii constants, the QP is used to find the best frame parameters that can be used in its equations of motion to predict its dynamic response with the minimum of error compared to the real data collected on its dynamic response, while the GA is used to find the best Prandtl-Ishlinskii constants for more reduction in error. The method has been applied to different frames with bilinear nonlinearity where the results show the high capability of the method. Two examples, a Single and a Multi Degree Of Freedom (SDOF and MDOF) frame, are included in the paper.