Prediction of Unconfined Compressive Strength of Lime Treated Soils Using an Artificial Neural Network

Asma Muhmed; Mohamad Gabar; Zeyad Katab; Ali Musa

doi:10.64516/fpazvt12

Article Details

Prediction of Unconfined Compressive Strength of Lime Treated Soils Using an Artificial Neural Network

Authors:

Asma Muhmed | Mohamad Gabar | Zeyad Katab | Ali Musa

Publisher:

Tobruk University, Libya

Journal:

Tobruk University Journal of Engineering Sciences Vol.3 Issue 1, pp.29-45

Document Type:

Article

Publication Date:

7/2022

Document Language:

English

Document ID (Org No.):

2001397674

pISSN:

3105-3939

DOI:

10.64516/fpazvt12

Keywords:

Abstract:

 Robust model based on Artificial-Neural-Network was proposed to predict the Unconfined Compressive Strength (UCS) of lime treated soils. In total, an experimental database using 1120 test specimens was created. Critical examination of the collected experimental data suggested that there are eight key parameters that govern the attained strength gain. These input parameters are; liquid limit, plastic limit, dry unit weight, water content, fine content, lime content, curing temperature and curing time whereas the only output dependent parameter is the UCS. The parameters of the proposed model including weights, biases and transfer functions were successfully converted to an explicit mathematical model relating the UCS with the key input parameters. Based on the results of the statistical evaluation, it was shown that a three-layered Artificial-Neural-Network model with 19 hidden neurons was capable to predict the UCS of lime treated soils with a high degree of accuracy. A coupling effect of the input parameters and weights analysis were conducted for the developed Artificial-Neural-Network-model to assess the importance of the key parameters.

Article Details

Prediction of Unconfined Compressive Strength of Lime Treated Soils Using an Artificial Neural Network

Authors:

Asma Muhmed | Mohamad Gabar | Zeyad Katab | Ali Musa

Publisher:

Tobruk University, Libya

Journal:

Tobruk University Journal of Engineering Sciences Vol.3 Issue 1, pp.29-45

Document Type:

Article

Publication Date:

7/2022

Document Language:

English

Document ID (Org No.):

2001397674

pISSN:

3105-3939

DOI:

10.64516/fpazvt12

unconfined compressive strength

lime stabilisation

artificial neural network

Issues

Article Details

Prediction of Unconfined Compressive Strength of Lime Treated Soils Using an Artificial Neural Network

Authors: Asma Muhmed | Mohamad Gabar | Zeyad Katab | Ali Musa

Publisher: Tobruk University, Libya

Journal: Tobruk University Journal of Engineering Sciences Vol.3 Issue 1, pp.29-45

Document Type: Article

Publication Date: 7/2022

Document Language: English

Document ID (Org No.): 2001397674

pISSN: 3105-3939

DOI: 10.64516/fpazvt12

unconfined compressive strength

lime stabilisation

artificial neural network

Issues

Authors:

Asma Muhmed | Mohamad Gabar | Zeyad Katab | Ali Musa

Publisher:

Tobruk University, Libya

Journal:

Tobruk University Journal of Engineering Sciences Vol.3 Issue 1, pp.29-45

Document Type:

Article

Publication Date:

7/2022

Document Language:

English

Document ID (Org No.):

2001397674

pISSN:

3105-3939

DOI:

10.64516/fpazvt12