Modeling of electrical resistivity of soil based on geotechnical properties

Alsharari, Bandar; Olenko, Andriy; Abuel-Naga, Hossam

doi:10.1016/j.eswa.2019.112966

Cited by 24 publications

(11 citation statements)

References 29 publications

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“…Several related studies such as in determining foundation behavior like prediction of shallow foundation reliability [11], pile raft foundation [12], axial capacity of pile foundation [13], shaft resistance [14], elastic settlement [15], settlement shallow foundation [16] and loading-unloading pile static load [17]. Other related research such as predicting soil physical and mechanical properties like prediction of CBR value [18], uniaxial compressive strength [19], undrained shear strength [20]- [21], bearing capacity [22]- [23], unit weight [24], compression index & compression ratio [25], classification [26], compression coefficient [27], liquefaction [28], and electrical resistivity of soil [29]. ANN is also used in prediction of dynamic compaction [30] and slope stability [31].…”

Section: Table 1 Summarize Of Literature Reviewmentioning

confidence: 99%

Prediction of standard penetration test value on cohesive soil using artificial neural networks

Nugroho¹,

Fernando²,

Suryanita³

2021

Jurnal Informatika

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Soil investigation is the main key in starting a construction. Standard Penetration Test (SPT) and Cone Penetration Test (CPT) are field tests that are often used in estimating soil parameters for foundation design purposes. The SPT value shows a correlation with the CPT value and other soil parameters. At present, there have been many conventional correlations examining these correlations, but the nonlinear nature of the soil due to very complex soil formations means that this correlation cannot be used in all situations. Artificial neural networks (ANN) are often used to estimate a complex and nonlinear value. In this study, that will predict the value of SPT on cohesive soil based on CPT test data and soil physical properties using artificial neural network capabilities using the Backpropagation algorithm and the activation function is bipolar sigmoid. This study uses 284 data from several places in Sumatra Island, Indonesia with data input are tip resistance (qc), shaft resistance (fs), effective overburden pressure (σ'0), percentage of liquid limit, plastic limit, sand, silt and clay. This study shows that the artificial neural network is able and effective in predicting the N-SPT value with a small error value and a strong regression equation. In this study, RMSE 3,441, MAE 2,318 and R 2 0,9451 for training data and RMSE 2,785, MAE 2,085, R 2 0,9792 for test data. This model is hereinafter referred to as NN_Nspt(C).

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Section: Table 1 Summarize Of Literature Reviewmentioning

confidence: 99%

Prediction of standard penetration test value on cohesive soil using artificial neural networks

Nugroho¹,

Fernando²,

Suryanita³

2021

Jurnal Informatika

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“…Since investigating the electrical response of soils is considered as a non destructive approach allowing precious information of soil [13][14][15][16], large uses have been employed for electrical measurements in many areas of scientific and engineering investigations, such as environmental, geotechnical, groundwater and underground fields. Specifically, electrical resistivity has been developed in geotechnical engineering, agriculture and geology since it is considered as a quick and non invasive method [17][18][19][20]. Soil resistivity values less than 100 Ωm reveals that soil nutrients are abundant in this area; however, sites with soil resistivity greater than 600 Ωm are reported as poor in soil nutrients [21].…”

Section: Introductionmentioning

confidence: 99%

Assessment and Correlation of Saline Soil Characteristics using Electrical Resistivity

Maliki¹,

Hadjadj²,

Laredj³

et al. 2023

J. Electrochem. Sci. Technol

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Soil salinity is becoming one of the most devastating environmental hazards over the years. Soil investigation involves fast, low cost and non disturbing methods to measure soil characteristics for both construction projects as well as for agricultural use. The electrical resistivity of saline soils is greatly governed by salt concentration and the presence of moisture in soil matrix. Experimental results of this investigation highlight that there is a significant relationship between the electrical resistivity of soil samples mixed with chloride solutions (NaCl, KCl, and MgCl 2 ) at various concentrations, and soil physical properties. Correlations represented by quadratic functions were obtained between electrical resistivity and soil characteristics, namely, water content, degree of saturation and salt concentration. This research reveals that the obtained correlations between electrical resistivity, salt concentration, water content and degree of saturation are effective for predicting the characteristics of salt affected soils in practice, which constitute a governing element in the assessment of saline lands sustaining infrastructure.

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“…In the geotechnical field, there have been many studies using the capabilities of this ANN. Related researches such as soil composition [2], soil classification [3], soil compaction [4], bearing capacity [5], unit weight [6], shallow foundation bearing capacity [7]- [9], estimated settlement in shallow foundations [10]- [12], preconsolidation stress [13], electrical resistivity of soil [14], deformation of geogrid-reinforced soil structures [15], tunnel boring machine performance [16], estimating cohesion of limestone samples [17] and many other related studies.…”

Section: Introductionmentioning

confidence: 99%

Estimation of standard penetration test value on cohesive soil using artificial neural network without data normalization

Nugroho

Fernando

Suryanita

2022

IJ-AI

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Artificial neural networks (ANNs) are often used recently by researchers to solve complex and nonlinear problems. Standard penetration test (SPT) and cone penetration test (CPT) are field tests that are often used to obtain soil parameters. There have been many previous studies that examined the value obtained through the SPT test with the CPT test, but the research carried out still uses equations that are linear. This research will conduct an estimated value of SPT on cohesive soil using CPT data in the form of end resistance and blanket resistance, and laboratory test data such as effective overburden pressure, liquid limit, plastic limit and percentage of sand, silt and clay. This study used 242 data with testing areas in several cities on the island of Sumatra, Indonesia. The developed artificial neural network will be created without data normalization. The final results of this study are in the form of root mean square error (RMSE) values 3.441, mean absolute error (MAE) 2.318 and R2 0.9451 for training data and RMSE 2.785, MAE 2.085, R2 0.9792 for test data. The RMSE, MAE and R2 values in this study indicate that the ANN that has been developed is considered quite good and efficient in estimating the SPT value.

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Modeling of electrical resistivity of soil based on geotechnical properties

Cited by 24 publications

References 29 publications

Prediction of standard penetration test value on cohesive soil using artificial neural networks

Prediction of standard penetration test value on cohesive soil using artificial neural networks

Assessment and Correlation of Saline Soil Characteristics using Electrical Resistivity

Estimation of standard penetration test value on cohesive soil using artificial neural network without data normalization

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