Innovative cone resistance and sleeve friction prediction from geophysics based on a coupled geo-statistical and machine learning process

Geotechnical parameters derived from an intrusive cone penetration test (CPT) are used to asses mechanical properties to inform the design phase of infrastructure projects. However, local, in situ 1D measurements can fail to capture 3D subsurface variations, which could mean less than optimal design decisions for foundation engineering. By coupling the localised measurements from CPTs with more global 3D measurements derived from geophysical methods, a higher fidelity 3D overview of the subsurface can be obtained. Machine Learning (ML) may offer an effective means to capture all types of geophysical information associated with CPT data at a site scale to build a 2D or 3D ground model. In this paper, we present an ML approach to build a 3D ground model of cone resistance and sleeve friction by combining several CPT measurements with Multichannel Analysis of Surface Waves (MASW) and Electrical Resistivity Tomography (ERT) data on a land site characterisation project in the United Arab Emirates (UAE). To avoid a potential overfitting problem inherent to the use of machine learning and a lack of data at certain locations, we explore the possibility of using a prior Geo-Statistical (GS) approach that attempts to constrain the overfitting process by “artificially” increasing the amount of input data. A sensitivity study is also performed on input features used to train the ML algorithm to better define the optimal combination of input features for the prediction. Our results showed that ERT data were not useful in capturing 3D variations of geotechnical properties compared to V s due to the geographical location of the site (200 m east from the Oman Gulf) and the possible effect of saline water intrusion. Additionally, we demonstrate that the use of a prior GS phase could be a promising and interesting means to make the prediction of ground properties more robust, especially for this specific case study described in this paper. Looking ahead, better representation of the subsurface can lead to a number of benefits for stakeholders involved in developing assets. Better ground/geotechnical models mean better site calibration of design methods and fewer design assumptions for reliability-based design, creating an opportunity for value engineering in the form of lighter construction without compromising safety, shorter construction timelines, and reduced resource requirements. • In situ 1D CPT measurements can failed to capture 3D subsurface variations. • 3D overview of the subsurface can be derived by coupling various geosciences data. • Machine Learning process may offer an effective means to build a ground model. • Geostatistical approach can be a way to improve the training phase. • Infrastructure project risk could benefit from artificial intelligence process.