Evaluation of Shear Stress in Soils Stabilized with Biofuel Co-Products via Regression Analysis Methods

Creative Commons License

Uzer A. U.

BUILDINGS, vol.13, no.11, 2023 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 13 Issue: 11
  • Publication Date: 2023
  • Doi Number: 10.3390/buildings13112844
  • Journal Name: BUILDINGS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Agricultural & Environmental Science Database, Applied Science & Technology Source, Communication Abstracts, INSPEC, Metadex, Directory of Open Access Journals, Civil Engineering Abstracts
  • Keywords: ANN, artificial intelligence, direct shear test, Regression Learner
  • Kayseri University Affiliated: Yes


In recent years, the employment of artificial neural networks (ANNs) has risen in various engineering fields. ANNs have been applied to a range of geotechnical engineering problems and have shown promising outcomes. The aim of this article is to enhance the effectiveness of estimating unfamiliar intermediate values from existing shear stress data by employing ANNs. Artificial neural network modelling was undertaken through the Regression Learner program that is integrated with the Matlab 2023a software package. This program offers a user-friendly graphical interface for developing AI models absent of the need for any coding. The validation and training of the ANNs were executed by relying on shear box test data which had been conducted at the Geotechnical Laboratory situated at Iowa State University. The objective of these experiments was to explore the potential of biofuel co-products (BCPs) in soil stabilization. The data should be structured with input and output parameters in columns and samples in rows. The dataset comprises a 216 x 6 matrix. The data columns provide information on soil type (pure soil-unadulterated; and 12% BCP-adulterated soil), time (1, 7, and 28 days), normal stress (0.069-DS10, 0.138-DS20, and 0.207-DS30 MPa), moisture content (OMC-4%, OMC%, and OMC+4%), and corresponding shear stress (sigma, MPa) values. The AI predictions for the test data output provide an outstanding R2 score of 0.94. This indicates that employing ANN to teach shear test data facilitates gaining a large quantity of data more efficiently, with fewer experiments and in less time. Such an approach seems encouraging for geotechnical engineering.