ANN prediction of some geotechnical properties of soil from their index parameters

Tizpa, Parichehr; Chenari, Reza Jamshidi; Karimpour-Fard, Mehran; Machado, Sandro Lemos

doi:10.1007/s12517-014-1304-3

Cited by 55 publications

(18 citation statements)

References 16 publications

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“…En [21] se utilizó Perceptrón Multicapa para predecir la relación de las características de compactación, permeabilidad y resistencia al corte del suelo con las propiedades físicas del suelo. En este estudio, la base de datos de 580 ejemplos fue construida a partir de otras investigaciones disponibles, con resultados de ensayos como la granulometría, límites de Atterberg, compactación, permeabilidad medida a diferentes niveles de grado de compactación (90-100%) y pruebas de compresión triaxial con drenaje consolidado.…”

Section: Parámetros De Compactación Y Cbr Del Suelounclassified

Importance of artificial neural networks in civil engineering: a systematic review of the literature

Purizaca

Barturen

Pérez

et al. 2020

ITECKNE

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Artificial neural networks (ANN) have a relevant role nowadays; several areas apply this technique due to the advantages they have to solve complex problems with many constraints compared to traditional methods, which are becoming outdated. Very little is known about this technique and its application in different branches of Civil Engineering. For this reason, the present research aims to conduct a systematic review of the literature to identify the use of this technique and to determine the results of the application of ANN models in civil engineering. A total of 41 scientific articles were included, distributed as follows: 6 in Scopus, 1 in ScienceDirect, 23 in ProQuest, 7 in Google Scholar, 2 in DialNet, 2 in SciELO. It was found that ANNs are used to predict or forecast variables associated with the fields of study in civil engineering; 8 applications of ANN were found for concrete properties, 11 for soil properties, 5 for seismic analysis, 9 for hydraulics, 7 for real estate valuation and 1 for bridge design. Likewise, it was found that the multilayer Perceptron is the most used ANN model, achieving an average R2 of 0.99, which shows advantages to solve problems with precision, in shorter times, with missing data in the data sets, as well as the reduction of the error factor.

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Section: Parámetros De Compactación Y Cbr Del Suelounclassified

Importance of artificial neural networks in civil engineering: a systematic review of the literature

Purizaca

Barturen

Pérez

et al. 2020

ITECKNE

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“…Pichler et al, 2003). With specific reference to the compaction of granular soils, predictions with ANN's have been implemented by Sinha and Wang (2008) and by Tizpa et al (2015).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Predictive correlations for the compaction of clean sands

Salvatore

Modoni

Chiaro

et al. 2015

Transportation Geotechnics

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“…An ANN is an approach to create a mathematical model that works analogously to the human brain (Sarmadian, Mehrjardi and Akbarzadeh, 2009). PSD, as a physical property, has a great impact on key soil parameters like permeability and various ANNs have been developed to understand its dynamics (Khanlari et al, 2012;Tizpa et al, 2015)Linear and non-linear empirical equations are famous, but, are limited because of the inability to produce correct results when effective size is greater than 3 mm or clayey soil (Carrier, 2003). These limitations provide the necessary motivation/push for testing and validating ANNs; as an effective alternative for those empirical equations, for modeling and predicting key parameters of soil.…”

Section: Introductionmentioning

confidence: 99%

Network Function Virtualization for Underwater Acoustic Wireless Communication Using Stochastic Network Calculus

Adil¹

2024

JMCMS

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The permeability of the soil is one of the most important properties of an unlined earthen canal or river bed. Using fine plastic particles has experimentally proven to reduce soil permeability, but the experimental study of the effect of a variety of types of plastic fines and their percentages in riverbed soil is tedious work to do. Estimation of permeability of riverbed soil by altering it with plastic fines using Artificial Neural Networks (ANNs) may reduce this effort. Particle size distributions (PSDs) have a significant influence on the permeability of bed soils. Being able to predict the permeability of bed soil by knowing the PSDs may provide an easy approach to know the loss of water by percolation. This study has investigated the quantitative relationships between permeability and PSD indices using ANNs. The aim was to build a mathematical model capable of predicting the permeability of bed soil by PSD indices of choice. A model was built using ANNs including PSD indices as input and permeability as output. The model stated that the coefficients of curvature and uniformity (Cc) and (Cu) and effective particle size (D50) may be used to predict the bed permeability. The computational model was able to predict the effect of variation of PSD indices on bed permeability, thus allowing increasing the efficiency of the river bed, to ensure maximum downstream water supply, lesser seepage and percolation and better productivity. The test result has confirmed the efficiency of the developed ANN tool in predicting the bed permeability for different PSD combinations.

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ANN prediction of some geotechnical properties of soil from their index parameters

Cited by 55 publications

References 16 publications

Importance of artificial neural networks in civil engineering: a systematic review of the literature

Importance of artificial neural networks in civil engineering: a systematic review of the literature

Predictive correlations for the compaction of clean sands

Network Function Virtualization for Underwater Acoustic Wireless Communication Using Stochastic Network Calculus

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