Title:

Machine learning and linear regression approach to model unconfined compressive strength of ceramic waste modified soil as subgrade pavement material

Alternative title:

Podejście uczenia maszynowego i regresji liniowej do modelowania nieograniczonej wytrzymałości na ściskanie ceramicznej modyfikowanej gleby odpadowej jako materiału nawierzchni podłoża

Creator:

Alkahtani, Meshel Qablan

Subject:

ceramic waste ; artificial neural network ; unconfined compression strength ; California bearing ratio

Description:

An effective application of artificial intelligence involves artificial neural networks. Artificial neural networks and linear regression models were developed to simulate the effects of using discarded ceramic waste as a subgrade for pavement. The ceramic waste was used at 2.5%, 5%, 7.5%, 10%, 12.5%, and 15%. A sample with 0% ceramic waste was tested to serve as a reference sample. The dataset was produced from laboratory experimentation findings used to train, test, and evaluate the model. A training set, a target set, and a prediction set were created from the dataset. The artificial neural network MSE was 0.42-1.40, while the linear regression model range was 1.74 to 3.63 for ceramic modified samples. The R2 range for the ANN model was 0.85-0.92, and the linear regression model exhibited a range of 0.71-0.78. The ANN model was more accurate than the linear regression model. Future studies are required to compare different machine-learning approaches for predicting soil mechanical properties.

Place of publishing:

Koszalin

Publisher:

Politechnika Koszalińska

Date:

2024

Type:

artykuł w czasopiśmie

Language:

eng

Is part of:

Rocznik Ochrona Środowiska. Vol. 26, s. 424-431

Rights:

Biblioteka Politechniki Koszalińskiej

Access rights:

internet

License:

Creative Commons BY-SA 4.0