Seismic damage assessment based on regional synthetic ground motion dataset: a case study for Erzincan,Turkey     

Shaghayegh Karimzadeh  Aysegul Askan  Murat Altug Erberik  Ahmet Yakut 《Natural Hazards》2018,92(3):1371-1397

Estimation of seismic losses is a fundamental step in risk mitigation in urban regions. Structural damage patterns depend on the regional seismic properties and the local building vulnerability. In this study, a framework for seismic damage estimation is proposed where the local building fragilities are modeled based on a set of simulated ground motions in the region of interest. For this purpose, first, ground motion records are simulated for a set of scenario events using stochastic finite-fault methodology. Then, existing building stock is classified into specific building types represented with equivalent single-degree-of-freedom models. The response statistics of these models are evaluated through nonlinear time history analysis with the simulated ground motions. Fragility curves for the classified structural types are derived and discussed. The study area is Erzincan (Turkey), which is located on a pull-apart basin underlain by soft sediments in the conjunction of three active faults as right-lateral North Anatolian Fault, left-lateral North East Anatolian Fault, and left-lateral Ovacik Fault. Erzincan city center experienced devastating earthquakes in the past including the December 27, 1939 (Ms = 8.0) and the March 13, 1992 (Mw?=?6.6) events. The application of the proposed method is performed to estimate the spatial distribution of the damage after the 1992 event. The estimated results are compared against the corresponding observed damage levels yielding a reasonable match in between. After the validation exercise, a potential scenario event of Mw?=?7.0 is simulated in the study region. The corresponding damage distribution indicates a significant risk within the urban area.  相似文献   

Stochastic Modeling of Groundwater Fluoride Contamination: Introducing Lazy Learners     

Khabat Khosravi  Rahim Barzegar  Shaghayegh Miraki  Jan Adamowski  Prasad Daggupati  Mohammad Reza Alizadeh  Binh Thai Pham  Mohammad Taghi Alami 《Ground water》2020,58(5):723-734

While it remains the primary source of safe drinking and irrigation water in northwest Iran's Maku Plain, the region's groundwater is prone to fluoride contamination. Accordingly, modeling techniques to accurately predict groundwater fluoride concentration are required. The current paper advances several novel data mining algorithms including Lazy learners [instance-based K-nearest neighbors (IBK); locally weighted learning (LWL); and KStar], a tree-based algorithm (M5P), and a meta classifier algorithm [regression by discretization (RBD)] to predict groundwater fluoride concentration. Drawing on several groundwater quality variables (e.g., concentrations), measured in each of 143 samples collected between 2004 and 2008, several models predicting groundwater fluoride concentrations were developed. The full dataset was divided into two subsets: 70% for model training (calibration) and 30% for model evaluation (validation). Models were validated using several statistical evaluation criteria and three visual evaluation approaches (i.e., scatter plots, Taylor and Violin diagrams). Although Na⁺ and Ca²⁺ showed the greatest positive and negative correlations with fluoride (r = 0.59 and −0.39, respectively), they were insufficient to reliably predict fluoride levels; therefore, other water quality variables, including those weakly correlated with fluoride, should be considered as inputs for fluoride prediction. The IBK model outperformed other models in fluoride contamination prediction, followed by KStar, RBD, M5P, and LWL. The RBD and M5P models were the least accurate in terms of predicting peaks in fluoride concentration values. Results of the current study can be used to support practical and sustainable management of water and groundwater resources.  相似文献