Abstract: Inspired by big data, fully utilizing geotechnical data for precise characterization and modeling of geotechnical parameters is critical for the digitalization of geotechnical engineering. This study collected geotechnical investigation reports from 75 engineering projects in Shenzhen, established a database containing 8 geotechnical parameters of clay and weathered residual soil (SZ-SOIL/8/11369), and thoroughly analyzed the distribution characteristics of geotechnical parameter data in Shenzhen. Subsequently, a model for predicting geotechnical parameters was developed using this database and a generative adversarial network (GAN). The proposed method was applied to a project in Shenzhen, successfully predicting mechanical parameters from known physical parameters and accurately forecasting the geotechnical parameter distribution of the project site using small samples. The results indicate that the proposed method can make reasonable predictions for samples with missing parameters, achieving the goal of reducing the uncertainty in geotechnical parameters at local engineering sites through extensive regional survey data. This provides parameter assurance for the resilience design and risk assessment of geotechnical and underground engineering structures in Shenzhen.

Key words: distribution of geotechnical parameters, database, prediction, generative adversarial network