Quantum mechanics

Soil horizon

Engineering

Statistics

Geotechnical engineering

Predictability

Geology

Soil water

Physics

Mathematics

Electronic engineering

Environmental science

Soil science

Sensitivity (control systems)

Landslide

CATENA

In the current paper, a hybrid model was developed to generate 3D delineated soil horizons using clay sensitivity (St) with 1 m depth intervals in a landslide prone area in the southwest of Sweden. A hybridizing process was carried out using generalized feed forward neural network (GFFN) incorporated with genetic algorithm (GA). The model was conducted by means of seven variables consisting of the geographical coordinates and piezocone penetration test data (CPTu). The output of model (St) as a description of the effect of soil disturbance on shear strength plays a significant role in landslides in Sweden and thus can be applied for site-specific evaluation. Therefore, the use of St-based models to delineate soil layers can be a cost-effective solution to improve geo-engineering design practices and assist in the reduction of related environmental risks, such as catastrophic landslide events or excavation failures. Evaluated model performance based on different applied soil classifications showed 4.38% improvement in the predictability level of GFFN-GA compared to optimum GFFN. Accordingly, delineated soil layers were evaluated using different criteria including previous landslides as well as supplementary geophysical and geotechnical investigations. The results show that the adopted hybrid GFFN-GA is an efficient tool that can potentially be applied to delineate soil horizons for the prediction of future events.

A visualized hybrid intelligent model to delineate Swedish fine-grained soil layers using clay sensitivity