One of the most common irregularities in structures is the irregularity in height and lateral stiffness. Due to the commonness of the use of irregular structures and also the different seismic responses of this type of structures, in comparison with regular structures, investigating the seismic response of irregular structures has always been the subject of several research studies. The structures designed for the reduced base shear, under the design earthquake, have inelastic response. To calculate the real (inelastic) displacements of structures under the design earthquake, the displacements obtained from the reduced base shear, are amplified by the deflection amplification factor (Cd). Seismic codes have dedicated a Cd for each structural system. But different studies have shown that the dedicated Cd by the codes cannot accurately estimate the real displacements. The main purpose of this research is to propose the Cd values for more accurately estimating the maximum inter-story drift ratio (MIDR) and maximum Roof drift ratio (MRDR) in steel special moment resisting frames (SMRFs) with the soft story. The number of stories and the location of the soft story are the variables considered in this research. The results show that the use of Cd = 5.5, recommended by the 2800 standard and ASCE 7-16 for steel SMRFs, underestimates the real MIDR and also MRDR, under the design earthquake. It is shown that by increasing the number of stories, the mean Cd obtained from the analyses increases. The reason for this issue is the P-Δ effects that increase by increasing the number of stories. In addition, it is shown that a specified trend cannot be found between the location of the soft story and the mean Cd values in the stories of the structures. Thus, for more accurately estimating MIDR in the considered structures, under the design earthquake, Cd = 8.5 is proposed. Furthermore, for more accurately estimating MRDR, Cd Roof = 8.0 is proposed.

The incident angle of ground motion is one of the sources of uncertainty in the seismic response of buildings. Moreover, understanding the structural response to the impose ground motion may cause significant changes in the maximum response of buildings. In order to investigate the influence of the spatial distribution of orthogonal components of earthquake strong motion on the structural responses, three 15-story buildings were analyzed in this study using the time-history method. A significant live load (750 kg/m2) is imposed at different vertical levels of the structures. The imposed load was combined with ground motion excitations in the range of 0 to 90 degrees. The response of structure was investigated using Roof drift index and inter-story drift ratio. Results demonstrate the orientation of seismic excitation and considering the maximum values of Roof drift index, which correspond to the critical direction increase Roof drift index between 8 to 12 percent. Furthermore, the inter-story drift ratio increased between 30 to 33 percent due to the orientation of excitation and considering the maximum values of the inter-story drift ratio, which correspond to the critical direction.

First Roof weighting interval in mechanized longwall mining is related directly to the applied loads on support system and thus, has important role on stability, safety, and continuity of operation. This paper presents an innovative approach based on cavability of immediate Roof to estimate first Roof weighting interval. Nine inherent parameters of Roof strata and its surrounding environment which affect caving process were taken into account to develop a classification system, incorporating fuzzy hybrid multi criteria decision-making technique. Roof Strata Cavability index (RSCi) was defined as summation of ratings for all parameters. Subsequently, the relationship between RSCi and extracted volume until the first caving moment (i. e. the extraction height× panel width × first Roof weighting interval) was determined using linear and non-linear regression models. Models were proposed and validated using the actual field data collected from different longwall panels around the world. Results indicated that the quadratic polynomial model gave a better performance in the estimation of first Roof weighting interval, compared to the other models. It was concluded that the proposed approach is an accurate and flexible tool to estimate first Roof weighting interval in longwall mining.

In Iran, some 22 percent of the building energy dissipates through the Roof. In the hot arid parts of the country, most of this energy is used for cooling rather than heating. There are several methods for reducingthis energy loss. Insulation is one popular method throughout the world. A more effective method is to reduce the energy penetrating the building by means of cooling the Roof. There are three main techniques for obtaining a “cool Roof”. In the first method, heat loads are reduced by lightening the color of the Roof, hence increasing its albedo. In the second method, the “green Roof”, the Roof is suffused with vegetation, which is more expensive and technically complicated. In the third method, the Roof is sprinkled with water. In this method, a significant amount of water is wasted, however. Because of its inexpensiveness and simplicity, the first method was chosen for an experiment, in which part of a dark green Roof was repainted with brilliant light green. The daily summer temperatures were then measured on an hourly basis and compared with the shaded areas of the Roof as well as areas covered by plants. The results indicated that the repainted areas were about 17 percent cooler.

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.

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.

In this paper, most in uential parameters on nonlinear response of structure-soil-structure system, including time period of main (T1) and adjacent (T2) structure, distance between structures (d) and soil type, have been thoroughly investigated. The purpose of this paper is to understand the e ect of changing each parameter on the nonlinear de ection of structures, investigating the requirements of code and predicting structural behaviour without modelling the adjacent structure. For this aim, six structures of 2 to 15 stories based on two clay samples is modelled with zero, 10 and 25 meters distance and is analyzed using nonlinear time history analysis. Direct method is used for modelling the systems. According to the results, soft base is caused sharp increase in the ratio of the  rst story drift to the  xbase with a minimum ratio of 1. 1 and a maximum of 3. 61. The increase of T1 and T2 is led to increase structural deformation so that T1 e ects are at least twice T2. For main structures with T1 in the range of 0. 7 to 1. 5 times the soil period, Structural drift is intensi- ed; but for main structural time period T1 less than 0. 7 times the soil period, the e ects of structure-soilstructure interaction are negligible. Base on this study, Contrary to the expectation that increasing the distance between structures leads to a reduction in the e ects of structural-soil-structure interaction, only for structures with time period more than 3 seconds, increase the distance between structures, is decreased adjacent structures e ects. By comparing drift ratio of structure-soilstructure and soil-structure systems, in one third of the upper elevation of the structure, this ratio has a sudden drop that is compensated in the Roof, which with decreasing T2 and increasing d, the accumulation of the drift ratio from the Roof to the  rst oor is transferred. Ultimately, what codes are considered for soft support enforcement is unreliable. Finally, by identifying the key parameters of structural-soil-structure interaction, relationships are developed to improve the requirements of the regulations for soft support.