The Realization and Application of AI Reconstruction Method of Stratum Information Based on FCN

It is quite important for the refined design and construction of geotechnical engineering to obtain a certain amount of borehole data and then determine the stratum profile information through geotechnical investigation. However, due to the particularity of the area where some engineering sites are located (existing old urban areas, cultural Relic Protection Building, etc.), there is often a problem that the borehole data is difficult to obtain, which makes the corresponding stratum information determination challenging. To this end, an artificial intelligence (AI) method for stratum information reconstruction is developed based on the fully convolutional network (FCN). The core idea of this method is to use the existing borehole data in the region as a learning sample, analyze and extract the multi-dimensional information features of the sample (vertical stratification, horizontal extension), and then use this information feature as a template to perform probability-based stratum profile information interpolation reconstruction for engineering sites with only a small amount of borehole data. Through the study and reconstruction of the geological survey data of a tunnel project and foundation pit project in the ancient city of Suzhou, it is found that the accuracy of stratum prediction gradually tends to be stable after the number of simulations increases to more than 30 times, and can reach about 90%. This verifies the applicability of the developed AI reconstruction method of stratum information, which will provide an effective choice for the prediction of complex stratum information in related projects.