This study presents the application of an artificial neural network (ANN) and geographic information system (GIS) for estimating the social vulnerability to earthquakes in the Tabriz city, Iran. Thereby, seven indicators were identified and used for earthquake vulnerability mapping, including population density, household density, employed density, unemployed density, and literate people. To obtain more accuracy in our analysis, all of the indicators were entered into a geographic information system (GIS). After the standardization of the data, an artificial neural network (ANN) model was applied for deriving a social vulnerability map (SVM) of different hazard classes for Tabriz city. The results showed that 0.77% of the total area was found to be very highly vulnerable. Very low vulnerability was recorded for 76.31% of the total study area. The comparison of data provided by (SVM) and the residential building vulnerability (RBV) of Tabriz city indicated the validity of the results obtained by ANN processes. Scatter plots are used to plot the data. These scatter plots indicate the existence of a strong positive relationship between the most vulnerable zones (1, 4, and 5) and the least (3, 7, and 9) of the SVM and RBV. The results highlight the importance of using social vulnerability study for defining seismic-risk mitigation policies, emergency management, and territorial planning in order to reduce the impacts of disasters.
Earthquakes are among the most catastrophic natural geo-hazards worldwide and endanger numerous lives annually. Therefore, it is vital to evaluate seismic vulnerability beforehand to decrease future fatalities. The aim of this research is to assess the seismic vulnerability of residential houses in an urban region on the basis of the Multi-Criteria Decision Making (MCDM) model, including the analytic hierarchy process (AHP) and geographical information system (GIS). Tabriz city located adjacent to the North Tabriz Fault (NTF) in North-West Iran was selected as a case study. The NTF is one of the major seismogenic faults in the north-western part of Iran. First, several parameters such as distance to fault, percent of slope, and geology layers were used to develop a geotechnical map. In addition, the structural construction materials, building materials, size of building blocks, quality of buildings and buildings-floors were used as key factors impacting on the building’s structural vulnerability in residential areas. Subsequently, the AHP technique was adopted to measure the priority ranking, criteria weight (layers), and alternatives (classes) of every criterion through pair-wise comparison at all levels. Lastly, the layers of geotechnical and spatial structures were superimposed to design the seismic vulnerability map of buildings in the residential area of Tabriz city. The results showed that South and Southeast areas of Tabriz city exhibit low to moderate vulnerability, while some regions of the north-eastern area are under severe vulnerability conditions. In conclusion, the suggested approach offers a practical and effective evaluation of Seismic Vulnerability Assessment (SVA) and provides valuable information that could assist urban planners during mitigation and preparatory phases of less examined areas in many other regions around the world.
High operational and maintenance costs represent as major economic constraints in the wind turbine (WT) industry. These concerns have made investigation into fault diagnosis of WT systems an extremely important and active area of research. In this paper, an immune system (IS) inspired methodology for performing fault detection and isolation (FDI) of a WT system is proposed and developed. The proposed scheme is based on a self nonself discrimination paradigm of a biological IS. Specifically, the negative selection mechanism [negative selection algorithm (NSA)] of the human body is utilized. In this paper, a hierarchical bank of NSAs are designed to detect and isolate both individual as well as simultaneously occurring faults common to the WTs. A smoothing moving window filter is then utilized to further improve the reliability and performance of the FDI scheme. Moreover, the performance of our proposed scheme is compared with another state-of-the-art data-driven technique, namely the support vector machines (SVMs) to demonstrate and illustrate the superiority and advantages of our proposed NSA-based FDI scheme. Finally, a nonparametric statistical comparison test is implemented to evaluate our proposed methodology with that of the SVM under various fault severities.
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