Journal of Seismology and Earthquake Engineering
Volume:23 Issue: 3, Summer 2021

The decay parameter κ (Kappa) which was, first, presented by Anderson and Hough (1984) is commonly used to represent the observed decay of acceleration spectrum at high frequencies. It is considered that κ has a direct linear relationship with distance. The intercept of this line is called κ0, which is a site-dependent component, and it is supposed to be due to attenuation of seismic waves in near-surface layers. Nowadays, these decay parameters have various applications such as estimation of attenuation function in the square-root-impedance method or implementation of host to target method in ground motion prediction equations. However, the characteristics of both κ and κ0 have not been studied, as it deserves. In the present paper, the decay parameter κ is obtained by the classical approach using 1157 records from all over Iran. The linearity of κ-distance relationship is questioned and investigated for different distances. It is found that the slope of κ-distance relationship reduces significantly at greater distances, and therefore, a linear correlation equation could not predict well enough specially for great distances. Furthermore, the log-likelihood (LLH) criterion is applied to select the best model that correlates κ0 with VS30 for different seismotectonic provinces of Iran. This criterion which is based on information theory yields that a linear equation can be a better correlation than a rational one. No asymptote value is observed for κ0 at high values of VS30.

The event of Sarpol-e Zahab with magnitude of 7.3 severely struck the border area of Iran an Iraq in Kermanshah province, leading to catastrophic damages to a wide region, specially Sarpol-e Zahab city. Douts arous whether the damage distribution allover the city stemed from seismic ground response or superficial loose fill material. The issue was explord in this study by especial attention to an area with high damages in Sarpol-e Zahab, called Fooladi. Since the seismic bedrock motion was not available, it was first deconvoluted from the recorded acceleration on the ground surface. Then ground response analysis was conducted at three different locations in Fooladi area, by applying deconvolved motion. It was determined that the local site condotion was accountable for damage severity specially in Fooladi area.

Post-earthquake assessment of buildings is one of the fundamental questions that needs to be answered immediately after a strong seismic event. Taking a building out of service after an earthquake can have a financial impact even greater than the earthquake itself. Up to now, damaged buildings are categorized in three groups, with red, yellow, or green labels, by engineering judgment based on visual screening. To have a more accurate method, the response of the buildings in aftershocks can be focused on new vibration-based system identification methods. But determining the system parameters is still a challenging subject; involving parameters with low identifiability, or correlated parameters can potentially influence the results in model updating problems. In this paper, a sensitivity matrix-based method is introduced to prioritize parameter estimability. The matrix-based process is capable of quickly determining the correlation between different parameters. Moreover, this method provides an explicit criterion for determining the optimal number of identifiable parameters. To indicate the efficiency of the method, a nonlinear Single-Degree of Freedom (SDOF) system has been simulated. Multiple model updating procedures have been carried out on the selected system, using the Unscented Kalman Filter (UKF). The result shows that system identification based on the sensitivity analysis outcome improves the quality of the identification precision. Additionally, this method decreases identification time by 35 percent that this amount can be crucial for updating large-scaled models.

One of the fields in data-based structural health monitoring (SHM) that has not been widely considered is the data classification step. Applications of the semi-supervised methods in data classification is getting more attention nowadays. In this study, an efficient semi-supervised support vector machine (S3VM) algorithm is used to for classifying between healthy and unhealthy stages. For this reason, a combined model-based and data-based approach is taken to determine the damage sensitive features. A hybrid approach has been utilized to generate the feature vectors. Using the vibrational data of the structure, the dynamic properties is obtained by system identification methods. Modal strain energy used as damage sensitive features (DSF). Different states of healthy and unhealthy conditions of the structure is used to evaluate the effectiveness of the proposed algorithm. Also, the Support Vector Machines (SVM) algorithm is utilized to compare the results. Since the semi-supervised support vector machines algorithm is based on support vector machines formulation, it is a suitable algorithm to compare the result with. It can be seen that the use of unlabeled data will enhance the effectiveness of the classification methods especially in the lack labeled data. When the labeled dataset is large enough, the result for both supervised and semi-supervised support vector machines is almost the same.

The aim of this study was to evaluate the optimization of three-dimensional steel moment frames structures with torsional irregularity, via particle swam algorithm under earthquake load. In this way, to produce torsional irregularity, the center of stiffness has been kept constant with symmetrical grouping of the members, and the center of mass was placed with specific distances from the center of stiffness. Besides that, two types of three-dimensional steel moment frames structures have been modeled. The first structure has box-shaped columns which is assessed with 0 Percent, 20%, 40% and 60% torsional irregularity. The second structure includes cross-shaped columns which is evaluated with the same condition as first structure. Particle Swarm Optimization (PSO) algorithm has been used to achieve the best structure in terms of: weight, maximum drift, dimensional fit of joints and strong column to weak beam. In addition, in the optimizing process of elements, the required strength of the sections according to AISC 360-16 is satisfied under the LRFD method. The AISC360-16 database is also used for sections of structures (I-shaped beams, box-shaped and cross-shaped columns). The optimization results demonstrated that the weight of the first structure (box-shaped column) with, 20%, 40% and 60% torsional irregularity was higher in comparison with the regular optimal state with 43.242 ton, to 5.5, 9.9 and 11.3%, respectively. Moreover, the weight of the second structure (cross-shaped column) in same noted condition was 0.31, 9.4 and 11.9% more than the weight of the structure in regular optimal state with 54.915 ton, respectively.

This paper assessed the sensitivity of seismic losses to the geographic resolution of building exposure model. One of the key steps of seismic risk assessment is providing an accurate and reliable building inventory. Generally, building exposure model is derived from various sources of information with different degrees of quality and accuracy. Therefore, compilation of exposure model is a complex process that is associated with uncertainties. In this regard, selecting the most appropriate geographic resolution of building exposure model is a challenge. There is a trade-off between the accuracy of ground motion values in the centroid of grid cells and computation efficiency. On the one hand, selecting a higher resolution will result in less efficient computing. Increased grid cell size, on the other hand, will impose uncertainty on the results due to inaccuracy in estimating ground motion values in the proper location of buildings. The purpose of this study is to address this question “what is the impact of geographic resolution of exposure model on the seismic risk assessment?”. To do so, a sensitivity analysis with three distinct levels of resolution was performed in Tehran, Iran, as a case study, to evaluate the impact of exposure model resolution on estimated losses. The results showed that total damage over the region is almost insensitive to the resolution of exposure models; while, a more accurate damage map with lower standard deviation is achieved by refining resolutions. This is an important outcome that will assist researchers performing seismic risk assessment in large geographic areas, like countries or provinces, to be aware of the effects of geographic resolution of exposure model on results.