Laboratory dataset and ANN prediction model for strength and mechanical properties of soft soils

Two ANN algorithms were developed to predict the strength and stiffness properties of soft soils treated with construction and demolition wastes. The laboratory experimental data were collected for over 296 soil specimens (peat and clay) to assess their Unconfined Compressive Strength (UCS), small-strain Young’s modulus (E0) and shear modulus (G0). These tests included varying curing times (28, 60, 90, and 120 days), different cement and recycled material content, and water-to-cement ratios. The developed algorithms can accurately predict the strength and mechanical properties of soft soils, offering a viable alternative to traditional UCS and FFR tests.