Journal of Earthquake and Tsunami

In this paper, a comprehensive range of uncertainties is considered to assess the seismic abilities of a moment-resisting system. To incorporate the parameter of construction quality, which has a descriptive nature, a suitable fuzzy logic engine has been developed. This engine, for the first time, addresses the quantitative assessment of construction quality parameters based on linguistic variables, including map accuracy, worker skills, material quality, and site supervision conditions. Instead of using random selection, a self-organizing map (SOM) algorithm is employed to carefully select strong ground motion records, reducing time costs. By applying incremental dynamic analysis (IDA) results, analytical equations are derived for the response surface method. These equations determine the collapse fragility’s mean and standard deviation. The material quality is modeled using the fuzzy inference engine, with the coefficient of logarithm response surface. Collapse fragility curves are created by taking into account many of their material quality values and utilizing the fuzzy model to estimate the modeling parameter based on the logarithm regression coefficients. These curves take into consideration various sources of uncertainty. In countries with inadequate material quality control, it is important to take cognitive uncertainty into account when developing fragility curves. This will help improve the overall risk management strategy.

An earthquake of Mw = 7.0 occurred on October 30, 2020, in the Aegean Sea near Samos Island, which caused severe structural damage in Bayraklı, Izmir (Türkiye), located around 70 km from the epicenter. To investigate the source, path, and site effects, ground motions recorded in Western Anatolia are simulated using the stochastic finite-fault method based on a dynamic corner frequency approach. The input model parameters are calibrated using the recorded motions at selected 10 stations within an epicentral distance of less than 100 km. The soil amplifications are modeled using horizontal-to-vertical spectral ratios and generic amplification factors. At most stations, including a few within Izmir Bay, amplitudes and frequency contents are modeled closely. Minor discrepancies within particular frequency bands can be attributed to insufficient representation of the local site effects. Finally, distributions of observed and simulated felt intensities are found to be consistent.

An earthquake early-warning system (EEWS) is essential for preserving human life. In terms of disaster management and EQ risk reduction, quickly determining the earthquake’s (EQ’s) magnitude and position is important. To prevent an EQ catastrophe, these parameters can be transmitted over an IoT network. The Evolutionary Gravitational Neocognitron Neural Network (EGNNN), a novel technique for real-time earthquake parameter observation, is introduced in this paper to enhance earthquake early warning (EEW) systems. The proposed framework uses various sophisticated data processing methods to analyze publically accessible earthquake data from sources like the Japan Meteorological Agency. The Morphological Filtering Extended Empirical Wavelet Transformation (MFEEWT) method is first used to pre-process seismic waveform input, significantly lowering noise levels in the data. After that, from the pre-processed waveforms the relevant features are extracted via the Fast Discrete Curvelet Transform with Wrapping (FDCT-WRP) and a Stacked Autoencoder (SAE). The EGNNN, an innovative neural network model created for earthquake parameter prediction, specifically location and magnitude, uses these extracted features as input. The Osprey optimization algorithm (OOA) is employed to enhance the EGNNN model. A crucial component of EEW systems, dependable and quick alert delivery is ensured by the proposed system’s integration with an Internet of Things (IoT) framework. The EGNNN swiftly evaluates earthquake parameters and sends this vital data to a centralized IoT system, enabling pertinent entities to act promptly. Performance evaluation is conducted on the Python platform, comparing the proposed technique with existing methods. The proposed method EGNNN-EPO-IoT achieves a higher warning time of 23.34%, 3.45%, and 7.86%; 12.23%, 32.21%, and 22.09% higher accuracy compared to the other existing techniques such as 3S-AE-CNN-EPO-IoT (3 sec-Autoencoder-Convolutional neural network-Earthquake parameter observation-IoT), XGB-EPO-IoT (Extreme Gradient Boosting-EPO-IoT), and SVM-EPO-IoT (Support Vector Machine-EPO-IoT).

“Strong column–weak beam, strong joint–weak member” is a core concept for the seismic design of buildings. For existing reinforced concrete (RC) frame buildings that do not satisfy this concept, installing RC wing walls beside existing columns is a simple and effective method for strengthening both columns and joints to promote a beam-hinging mechanism. In this study, a numerical simulation method is used to analyze the seismic performance of weak-joint RC frames strengthened by additional wing walls considering the column axial force ratio and to determine whether to consider the secondary load as a variable. The results show that when the secondary load is not considered, i.e. existing columns do not bear a load during the strengthening process, the strength, stiffness and energy dissipation of the structure increase with increasing axial force ratio, but the ductility of the structure decreases. When the secondary load is considered, the strength and initial stiffness of the frame are the greatest when the column axial force ratio is 0.4. When the axial force ratio is less than 0.4, the secondary load has less influence on the strength, stiffness and energy consumption. When the axial force ratio is greater than 0.4, the secondary load has a significant impact on the seismic performance of the strengthened structure, and the strength, stiffness, energy consumption and ductility are significantly greater than those in the case in which the secondary load is not considered. Additionally, the larger the axial force ratio is, the more significant the difference is. It is revealed that in engineering seismic strengthening using the wing wall installation method, the influence of secondary load on seismic performance should not be ignored, especially when the axial force ratio is large.

Proper consideration of variations in soil properties and their effects is necessary to enhance the seismic safety of structures. In this study, the effect of spatial variations in the cyclic resistance ratio on seismic ground behavior was investigated. Initially, dynamic centrifuge model tests were conducted on sandy ground featuring a 20% mixture of weak zones with low relative density and on homogeneous sandy ground with no mixture of weak zones. Subsequently, an effective stress analysis was performed by modeling the distribution of weak zones in the centrifuge model tests. Finally, after confirming the validity of the parameter settings, several analytical models with different weak-zone distributions were generated and numerically analyzed using random field theory. The results indicate that a local mixing of approximately 20% weak zones has only a limited effect on overall ground behavior. However, differences were observed in the rate of increase and dissipation of the excess pore water pressure ratio and in the residual horizontal displacement.

The soil–structure interaction plays a crucial role in determining the displacement and internal forces of multi-story buildings subjected to strong ground motion. One of the critical dynamic characteristics influencing soil–structure interaction is the fundamental site period and the average shear wave velocity associated with it. This study introduces an original equation to determine these parameters. In addition, for the first time in the literature, the version of the Rayleigh method used for finding the fundamental periods of buildings is used to find the fundamental site period. The soil is modeled as an equivalent shear beam to obtain the proposed equation. The peak displacement is obtained by acting the soil mass as an external load on the equivalent shear beam. For single-layer soil, the fundamental site period is proportional to the square root of the peak displacement of the equivalent shear beam. The least squares method generalizes the proposed relation for single-layer soils to multi-layer soil profiles. Modified Finite element Transfer matrix method is used for calibration in the least squares method. The equations used in the literature and earthquake codes for determining the fundamental site period and average shear velocity are tested on various examples, and it is shown that the method proposed in this study, along with the Rayleigh method, gives better results than these equations. The performances of these two methods and the five commonly used equations are tested and compared on different soil profiles. Transfer functions, Finite Element Method (SAP200) and Modified Finite Element Transfer Matrix Method are used for verification. For all soil profiles, the results obtained from the transfer function, Finite Element Method (SAP200) and Modified Finite Element Transfer Matrix Method are found to be in agreement. The true percent relative error found in the results obtained with the proposed method is 4.47%.