Soil classification analysis based on piezocone penetration test data — A case study from a quick-clay landslide site in southwestern Sweden

Shahri, Abbas Abbaszadeh; Malehmir, Alireza; Juhlin, Christopher

doi:10.1016/j.enggeo.2015.01.022

Cited by 35 publications

(1 citation statement)

References 31 publications

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“…An example of the division of the loess subsoil based on the results of static soundings is shown in Figure 11. SBT charts (e.g., Robertson's chart) classify soils in terms of their behavior and are widely used to interpret the soil profile [20,38,[45][46][47]. For aeolian loess soils, R f values most often range from 1.7 to 3.2%, which, with relatively high cone resistances q c , locates them in the area on the border between silty and sandy loess, and very often they are classified as sandy.…”

Section: Qcmean/σ (Mpa)mentioning

confidence: 99%

CPT Parameters of Loess Subsoil in Lublin Area

Nepelski

Lal

2021

Applied Sciences

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Loess soils were created by the wind transporting particles with later or in parallel occurred protogenetic, syngenetic and epigenetic processes. As a result, various genetic processes affected loesses strength and deformability characteristics. The aim of the study is to estimate the main CPT parameters of loess subsoil in Lublin area according to divided facies. The subsoil in the area of the Nałęczowski Plateau, where Lublin is located, consists mainly of loess from aeolian and aeolian–diluvial facies, and in the deeper parts—from aeolian–alluvial facies. Most of the results obtained for the aeolian facies at the level of qc in the range from 4.5 to 8.0 MPa indicate that these soils are a good load-bearing substrate for building structures. Cone resistances mostly at the level of 1.5–4.0 MPa for the diluvial and alluvial facies confirm that these facies constitute less favorable foundation conditions. The reduced resistance results mainly from the increased water content in ground pores. It is especially the soils of the diluvial facies that provide unfavorable foundation conditions, as they occur near the surface. Genetic processes are a very important element that should be taken into account in engineering research.

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Section: Qcmean/σ (Mpa)mentioning

confidence: 99%

CPT Parameters of Loess Subsoil in Lublin Area

Nepelski

Lal

2021

Applied Sciences

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An artificial neural network based model to predict spatial soil type distribution using piezocone penetration test data (CPTu)

Ghaderi

Shahri

Larsson

2018

Bull Eng Geol Environ

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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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Landslide susceptibility mapping using hybridized block modular intelligence model

Shahri

Moud²

2020

Bull Eng Geol Environ

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Landslide susceptibility map (LSM) provide useful tool for decision makers in hazard mitigation concerns. In the present paper, a novel hybrid block-based neural network model (HBNN) for the purpose of producing high-resolution LSM was developed. This hybrid approach was found through the mixture of expert modular structures and divide-and-conquer strategy incorporated with genetic algorithm (GA). The introduced HBNN then was applied on southern part of Guilan province (north of Iran) using 14 causative factors covering topographic and geomorphologic features, and geological and tectonical factors as well as hydrology, land data, and climate conditions. The landslide inventory map was provided using a synergy work from monitored events, interpretation of aerial photographs, and carried out geotechnical investigations in the area as well as field surveys. To insight, the predictability of proposed HBNN was compared with two developed models using multilayer perceptrons (MLPs) and generalized feed forward neural network (GFFN). The generated LSM was validated using receiver operating characteristic (ROC) curves, statistical error indices, and sensitivity and weight analyses as well as monitored landslides. Based on the compared metrics, HBNN with 86.52% and 90.15% in prediction and success rate as well as 89.36% for precision-recall curve demonstrated more consistent tool for future landslide susceptibility zonation. This implies on capability of developed HBNN in producing higher resolution and more reliable LSM for urban and land-use planners.

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Soil classification analysis based on piezocone penetration test data — A case study from a quick-clay landslide site in southwestern Sweden

Cited by 35 publications

References 31 publications

CPT Parameters of Loess Subsoil in Lublin Area

CPT Parameters of Loess Subsoil in Lublin Area

An artificial neural network based model to predict spatial soil type distribution using piezocone penetration test data (CPTu)

Landslide susceptibility mapping using hybridized block modular intelligence model

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