| 1. |
- Ghaderi, Abdolvahed, et al.
(author)
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A visualized hybrid intelligent model to delineate Swedish fine-grained soil layers using clay sensitivity
- 2022
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In: Catena (Cremlingen. Print). - : Elsevier BV. - 0341-8162 .- 1872-6887. ; 214, s. 106289-
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Journal article (peer-reviewed)abstract
- 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 geoengineering 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.
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| 2. |
- Ghaderi, Abdolvahed, et al.
(author)
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An artificial neural network based model to predict spatial soil type distribution using piezocone penetration test data (CPTu)
- 2019
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In: Bulletin of Engineering Geology and the Environment. - : SPRINGER HEIDELBERG. - 1435-9529 .- 1435-9537. ; 78:6, s. 4579-4588
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Journal article (peer-reviewed)abstract
- Soil types mapping and the spatial variation of soil classes are essential concerns in both geotechnical and geoenvironmental engineering. Because conventional soil mapping systems are time-consuming and costly, alternative quick and cheap but accurate methods need to be developed. In this paper, a new optimized multi-output generalized feed forward neural network (GFNN) structure using 58 piezocone penetration test points (CPTu) for producing a digital soil types map in the southwest of Sweden is developed. The introduced GFNN architecture is supported by a generalized shunting neuron (GSN) model computing unit to increase the capability of nonlinear boundaries of classified patterns. The comparison conducted between known soil type classification charts, CPTu interpreting procedures, and the outcomes of the GFNN model indicates acceptable accuracy in estimating complex soil types. The results show that the predictability of the GFNN system offers a valuable tool for the purpose of soil type pattern classifications and providing soil profiles.
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