Geotechnical database building and 3D modeling of the soil in Medina, Saudi Arabia

Abstract

Sustainable innovative uses of underground space dictate the use of efficient and cost-effective techniques for geo-investigation and planning. This is now affordable with detailed three-dimensional (3D) geotechnical models of the soil properties that provide key source data to tackle the inherent complex nature of the subsurface beneath the densely urbanized cities. These models are vital for the sustainable cities safe urban expansion, tunneling, and optimal design of settlements. In the present research, 3D models were built using 189 samples collected from 92 boreholes distributed in Medina, Saudi Arabia. Models built included the database generated for soil varieties, classes of cohessionless, and cohesive soils based on their standard penetration test (SPT)-N value, along with rock quality designation (RQD) of the sound bedrocks, soil classes according to AASHTO, grain size analysis, Atterberg limits (liquid limits and plasticity index), the shear stress parameters (friction angle, Ø, and cohesion, c), the unconfined compression strength, and the soil water chemistry (pH, SO32−, and Cl−). Five soil varieties that were recognized ranged in size from clays to cobbles overlain by fills and underlain by basalt, rhyolite/granite, or andesite. AASHTO main soil types are A-1-b, A-2–4, and A-1-a. Out of the 189 samples, 174 were non-plastic while 15 samples showed an average liquid limit of 42.57% and plasticity index average of 9.92. Friction angle averaged 30.47° with c values average of 0.11. Unconfined compressive stress averaged 609 kg/cm2. Soil water chemistry clarified alkaline water (8–8.6 pH) with means of 0.1 for Cl− and SO32− contents. Geotechnical properties are spatially rendered in 3D and interpreted to better enable city planners predict and locate risk zones in the urban underground space.

Application of Machine Learning and 3D Modelling to Improve Geotechnical Site Characterization

“…The approach developed in this study can be used with existing geotechnical investigation databases to improve information characterization of complex subsurface conditions that are not well covered by borehole observations alone. This approach has been tried in the literature albeit without the rigour of accuracy and uncertainty assessment and machine learning (Masoud et al, 2022). Accuracy and uncertainty assessments can help to target specific locations that need further borehole observations.…”

“…3D modelling gives more effective visualization and comprehension of the relationship between vertical and horizontal continuum of subsurface conditions than in 1D or 2D. Since geological and geotechnical sub-surfaces are inherently 3D, they are best portrayed with 3D representation (Masoud et al, 2022). Presently, there is scanty literature information on open-source software for 3D representation of geotechnical investigations.…”