Browsing by Author "Askan, Aysegul"

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Comparison of Field Survey-Based Macroseismic Intensity Map and Numerical Macroseismic Intensity Maps Using Mars and Multiple Regression Methods for 6th of February 2023 Kahramanmaraş Earthquakes
(Springer Science and Business Media Deutschland GmbH, 2024) Albayrak, Kubilay; Askan, Aysegul; Yerlikaya-Ozkurt, Fatma
Earthquakes are natural events that cause damage to built environments by the instant seismic energy release. This energy is measured by instrumental devices to obtain the peak ground motion parameters such as Peak Ground Acceleration (PGA) and Peak Ground Velocity (PGV). Additional measurements based on the questionnaires after the earthquakes are required to identify the felt or macro seismic intensity levels. These measurements are crucial to identify the total effects of earthquakes over not only an area but also for the spatial distribution of ground motion parameters. For this purpose, it is important to study the multi-variable criteria correlations between ground motion parameters and Modified Mercalli Intensity (MMI) levels based on linear relationships of predictor variable couples. In this regard, the Multivariate Adaptive Regression Splines (MARS) Method and the Multiple Linear Regression Method are used. The entire dataset is composed of 69 earthquakes between 2005 and 2022 with 2171 ground motion parameters coupled with MMI levels. For MMI-based correlations, the MARS method is used to identify the non-linearities between predictor variables by piecewise linear functions, but for the Multiple Linear Regression Method, the least correlated variables of PGA-Epicentral Distance and PGV-Epicentral Distance are used to obtain the relationship between MMI and PGM parameters. The resulting equations obtained for the entire Turkiye database are performed to identify MMI maps of the 6th of February 2023, Kahramanmaras Earthquakes, and these maps are used to check the accuracy of the results by the comparison of field survey-based MMI maps. Finally, the numerical MMI maps are found to be consistent with the field survey-based MMI maps.
Citation - WoS: 1
Citation - Scopus: 1
Modeling of Kappa Factor Using Multivariate Adaptive Regression Splines: Application To the Western Türkiye Ground Motion Dataset
(Springer, 2024) Kurtulmus, Tevfik Ozgur; Yerlikaya-Ozkurt, Fatma; Askan, Aysegul
The recent seismic activity on Turkiye's west coast, especially in the Aegean Sea region, shows that this region requires further attention. The region has significant seismic hazards because of its location in an active tectonic regime of North-South extension with multiple basin structures on soft soil deposits. Recently, despite being 70 km from the earthquake source, the Samos event (with a moment magnitude of 7.0 on October 30, 2020) caused significant localized damage and collapse in the Izmir city center due to a combination of basin effects and structural susceptibility. Despite this activity, research on site characterization and site response modeling, such as local velocity models and kappa estimates, remains sparse in this region. Kappa values display regional characteristics, necessitating the use of local kappa estimations from previous earthquake data in region-specific applications. Kappa estimates are multivariate and incorporate several characteristics such as magnitude and distance. In this study, we assess and predict the trend in mean kappa values using three-component strong-ground motion data from accelerometer sites with known VS30 values throughout western Turkiye. Multiple linear regression (MLR) and multivariate adaptive regression splines (MARS) were used to build the prediction models. The effects of epicentral distance Repi, magnitude Mw, and site class (VS30) were investigated, and the contributions of each parameter were examined using a large dataset containing recent seismic activity. The models were evaluated using well-known statistical accuracy criteria for kappa assessment. In all performance measures, the MARS model outperforms the MLR model across the selected sites.
Citation - WoS: 9
Citation - Scopus: 10
Prediction of Potential Seismic Damage Using Classification and Regression Trees: a Case Study on Earthquake Damage Databases From Turkey
(Springer, 2020) Yerlikaya-Ozkurt, Fatma; Askan, Aysegul
Seismic damage estimation is an important key ingredient of seismic loss modeling, risk mitigation and disaster management. It is a problem involving inherent uncertainties and complexities. Thus, it is important to employ robust approaches which will handle the problem accurately. In this study, classification and regression tree approach is applied on damage data sets collected from reinforced concrete frame buildings after major previous earthquakes in Turkey. Four damage states ranging from None to Severe are used, while five structural parameters are employed as damage identifiers. For validation, results of classification analyses are compared against observed damage states. Results in terms of well-known classification performance measures indicate that when the size of the database is larger, the correct classification rates are higher. Performance measures computed for Test data set indicate similar success to that of Train data set. The approach is found to be effective in classifying randomly selected damage data.