LAUSR

Abstract Frequent casualties and massive infrastructure damages are strong indicators of the need for dynamic site characterization and systematic evaluation of a site's sustainability against hazards. Microzonation is one of the most popular techniques in assessing a site's hazard potential. Improving conventional macrozonation maps and generating detailed microzonation is a crucial step towards preparedness for hazardous events and their mitigation. In most geoscience studies, the direct measurement of parameters imposes a huge cost on projects. On one hand, field tests are expensive, time-consuming, and require specific high-level expertise. Laboratory methods, on the other hand, are faced with difficulties in perfect sampling. These limitations foster the need for the development of new numerical techniques that correlate simple-accessible data with parameters that can be used as inputs for site characterization. In this paper, a microzonation algorithm that combines neural networks ( NNs ) and geographic information system ( GIS ) is developed. In the field, standard penetration and downhole tests are conducted. Atterberg limit test and sieve analysis are performed on soil specimens retrieved during field-testing. The field and laboratory data are used as inputs, in the integrated NNs-GIS algorithm, for developing the microzonation of shear wave velocity and soil type of a selected site. The algorithm is equipped with the ability to automatically update the microzonation maps upon addition of new data.