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

Creator:

Alkahtani, Meshel Qablan

Date:

2024

Type:

artykuł w czasopiśmie

Alternative title:

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

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

Identifier:

oai:dlibra.tu.koszalin.pl:2104

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

Exlibris

Subject and keywords:

ceramic waste artificial neural network unconfined compression strength California bearing ratio

Object collections:

Last modified:

Dec 23, 2025

In our library since:

Dec 23, 2025

Number of object content hits:

3

All available object's versions:

https://dlibra.tu.koszalin.pl/publication/2114

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2024, Alkahtani, Meshel Qablan, Machine learning and linear regression approach to model unconfined compressive strength of ceramic waste modified soil as subgrade pavement material Dec 23, 2025

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