Machine Learning for CPTu Interpretations and Site Characterization 

Dr. Iman Entezari, Senior Research Engineer at ConeTec, presents his talk "Machine Learning for CPTu Interpretations and Site Characterization".
Abstract: Machine Learning (ML) has emerged as a powerful tool across various industries, offering valuable data-driven insights for informed decision-making. The application of ML methods in geotechnical engineering and soil mechanics dates back to the early 1990’s. However, with increasing computational power, ML has gained substantial interest within the geotechnical engineering community and has gradually become an alternative solution for geotechnical problems. The availability of large in-situ testing databases, specifically CPTu databases, coupled with ML techniques provide an opportunity to develop site-specific or regional models for better site characterization. ML is also a powerful tool for investigating refinements to existing empirical CPTu relationships. With more accurate in-situ predictions of soil properties, geotechnical analysis using the CPTu can be improved. This talk explores ML applications in CPTu interpretation including estimation of mine tailings solids and fines, estimation of soil unit weight, and estimation of shear wave velocity from CPTu data.
Speaker Bio: Dr. Iman Entezari is a Senior Research Engineer at ConeTec, leading the integration of hyperspectral sensing technology into soil and tailings characterization methodologies. With a keen interest in Machine Learning, he harnesses hyperspectral, vision, and CPT data to develop innovative solutions for optimizing the accuracy and efficiency of soil and tailings characterization and geotechnical site investigations. Iman received his Ph.D. from the University of Alberta in 2016 and was a post-doctoral fellow at UofA Geotechnical Centre from 2016 to 2018.