Evaluation of the Changes in the Strength of Clay Reinforced with Basalt Fiber Using Artificial Neural Network Model

Abstract

In this research, the impact of basalt fiber reinforcement on the unconfined compressive strength of clay soils was experimentally analyzed, and the collected data were utilized in an artificial neural network (ANN) to predict the unconfined compressive strength based on the basalt fiber reinforcement ratio and length. For this purpose, two different lengths of basalt fiber (6 mm and 12 mm) were added to unreinforced bentonite clay at ratios of 0%, 1%, 2%, 3%, 4%, and 5%, and unconfined compressive tests were performed on the prepared reinforced clay samples to determine the unconfined compressive strength (qu) values. The evaluation of the obtained experimental results was carried out by creating ANN models. To validate the prediction capabilities of the ANN, a comparative analysis was performed using linear regression, support vector machines, and Gaussian process regression models. Ultimately, a five-fold cross-validation technique was employed to objectively evaluate the overall performance of the model. The evaluations revealed that the ANN model predictions using data obtained from experimental studies showed the highest accuracy and were in close agreement with the experimental results.