A fusion approach for evaluating ground conditions for seismic microzoning at the Egyptian Solar Park in Benban, Aswan

Abstract

Seismic microzoning is the process of mapping out and comprehending the differences in ground motion due to earthquakes in a certain location. Accurate seismic microzoning is vital for the development and safety of buildings and infrastructure in earthquake-prone locations. In this work, we present the application of microtremors, multichannel analysis of surface and machine learning approaches for seismic microzoning at Benban Solar Park in Aswan, Egypt. The findings of the investigation indicated that the ground at Benban Solar Park was generally stiff, with certain regions having stronger stiffness and damping qualities than others. The data also indicated variances in the ground conditions at various sites inside the solar park, with certain regions having a greater risk of ground motion due to earthquakes. Overall, the combination of microtremors, multichannel analysis, and machine learning has shown to be an excellent strategy for correctly and effectively mapping out the ground conditions at Benban Solar Park and assuring the safety and structural integrity of the solar power plants at the park. Moreover, the results of the research could be used to guide the design and construction of the future solar power plants at the park and to examine the safety and structural integrity of the solar park. Furthermore, the application of these techniques not only ensures the safety and structural integrity of the solar power plants at Benban Solar Park, but also promotes sustainable development by providing valuable information for the design and construction of future solar power plants at the park, in line with the principles of environmentally-conscious and responsible development.