Modelling Soil Water Retention Curve for Cohesive Soil Using Artificial Neural Network

Sharanya, A. G.; Heeralal, M.; Thyagaraj, T.

doi:10.1007/978-981-19-6513-5_31

Cited by 1 publication

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Artificial Neural Networks is used to estimate the soil-water characteristic curves. This method is considered an aid to determining the suction value [20][21][22][23][24][25]. This system is built similarly to the human brain, with a neural network to connect the input data to the output data.…”

Section: Figure 1 Typical Features Of the Soil-water Characteristic C...mentioning

confidence: 99%

Prediction of Soil-Water Characteristic Curves of Four Subgrade Materials using a Modified Perera Model

Gueye,

Ba,

Mbaye

et al. 2023

Civ Eng J

View full text Add to dashboard Cite

One of the main hydraulic properties of unsaturated soils is the Soil-Water Characteristic Curve (SWCC). It is essential to understand, predict soil water storage and determine the hydraulic and mechanical behaviour of soils. These curves can be obtained by direct and indirect measurements. The measurements to obtain these curves are expensive, delicate to perform and can be really slow for fine soils, so predictive models become necessary. In order to make a numerical model, a couple of identification tests were carried out to obtain the physical properties of each sample among the four subgrade materials collected in the regions of Dakar and Thies (Senegal). The measurement tests of the matric suction were then conducted depending on the nature of the material (fine-grained soil or coarse-grained soil) and allowed to draw the SWCC of each soil. Among numerous predictive models developed for SWCC in the last decades; this study used the Perera model to fit the SWCC of four (04) subgrade materials, which did not give a satisfactory coefficient of correlation (R2 = 58% and a relatively low sum of the squared residuals (SSR)). This leads to modifying the Perera model to better fit the SWCC on the basis of an understanding of the effect of each parameter on the shape of the SWCC. The proposed modified model was validated by checking the adjusted R2, minimizing the SSR in order to approach at most the experimental air entry value. The modified model works pretty well on coarse-grained and fine-grained soils. This modified model of Perera provided a very good correlation R2equal to 99.98, 98.74, 99.64, and 99.73 for the sandy soils (Sebikotane and Keur Mory) and the Marley and Clayey soils of Diamniadio, with a minimal SSR obtained compared to Perera’s and Hernandez model. Doi: 10.28991/CEJ-2023-09-06-03 Full Text: PDF

show abstract

Section: Figure 1 Typical Features Of the Soil-water Characteristic C...mentioning

confidence: 99%

Prediction of Soil-Water Characteristic Curves of Four Subgrade Materials using a Modified Perera Model

Gueye,

Ba,

Mbaye

et al. 2023

Civ Eng J

View full text Add to dashboard Cite

show abstract

Modelling Soil Water Retention Curve for Cohesive Soil Using Artificial Neural Network

Cited by 1 publication

References 9 publications

Prediction of Soil-Water Characteristic Curves of Four Subgrade Materials using a Modified Perera Model

Prediction of Soil-Water Characteristic Curves of Four Subgrade Materials using a Modified Perera Model

Contact Info

Product

Resources

About