Hosted by Prof Majid Nazem of RMIT University, Melbourne, Australia.

Machine Learning in Geotech needs data. You can easily generate training sets using OPTUM G2.

The Machine Learning approach is an area of AI and is based upon the principle that machines can receive data, learn, and predict behaviour based on past observations from the received data. This webinar will address the applicability of different Machine Learning techniques in several geomechanics problems such as slope stability, load bearing capacity of piles, dynamic penetration, and predicting the inherent properties of soils.

Statistical techniques and Artificial Intelligence (AI) have been used to predict the soil behaviour in many geotechnical applications. Nonetheless, with the development of big data and the enhancement of computational intelligence, AI models have been more successful in predictions compared to statistical models. Due to their efficiency and robustness, AI techniques have attracted some attention for solving complex problems where there are highly nonlinear relations amongst influential parameters.