## 1 Introduction
To achieve this objective, various intelligent methods are employed for evaluating compressive strength performance, including artificial neural networks (ANNs), fuzzy logic, genetic algorithms, and particle swarm optimization [19; 20; 21; 22; 23]. In their study, [PERSON] et al. [21] aimed to determine the mechanical properties of basalt-fiber-reinforced silty soils and to identify the optimal ratio and size of basalt fibers on soil properties. To obtain more reliable results, they characterized soil behavior using machine learning models. The results showed that fiber length and confining pressure had a significant effect on the prediction of maximum deviator stress. In the study by [PERSON] et al. [23], mixtures were prepared with 6, 12, and 24 mm long basalt fiber reinforcements in proportions of 1%, 2%, and 3% with five different water contents for clay soils. Additionally, the dataset, consisting of input variables such as water content, fiber length, and fiber ratio, and output variables such as stress and deformation, was modeled using different machine learning algorithms. The results from the machine learning algorithms revealed that the best fit was obtained with Support Vector and Decision Tree Regression. In the study by [PERSON] et al. [19], the aim was to develop a functional relationship between fiber content, soil density, and soil moisture and the mechanical factor of fiber-reinforced soil for four different fibers (coconut, jute, water hyacinth, and polypropylene). For this purpose, an integrated methodology involving laboratory tests and the Extreme Learning Machine (ELM) technique was used to develop models. Models were developed using ELM to predict the compressive strength of soils reinforced with different types of fibers. The predictive accuracy of the ELM models appeared to be reasonably sufficient for estimating the compressive strength of fiber-reinforced soils. In the study by [PERSON] and [PERSON] [20], the effects of pond ash and polypropylene fiber on soil strength were investigated. Experimental data were processed using an ANN to develop prediction models for mechanical and durability parameters. The results of the ANN modeling excellently predicted the mechanical properties, with the correlation coefficient reaching up to 0.96. In the study conducted by [PERSON] et al. [22], the shear strength of clay soil reinforced with glass fiber was predicted using ANFIS (Adaptive Neuro-Fuzzy Inference System). For this purpose, experimental samples with different water contents (13%, 15%, and 17%) and different glass fiber ratios (0%, 1%, 1.5%, and 2%) were prepared. The examination of statistical parameters showed that the ANFIS model provided the best results in predicting shear strength when the data were split into 80% for training and 20% for testing. [PERSON] et al. [24] focused on using ANNs to model soil characteristics, enhancing the prediction accuracy of engineering properties such as soil moisture content and compressive strength, thus reducing the need for extensive experimental testing. A different study explored the use of machine learning models such as ANN for predicting the mechanical properties of concrete [25]. It highlighted the benefits of using ANN in comparison to traditional methods for faster and more accurate predictions of compressive strength. In this study, the effects of basalt fiber reinforcement on the \(\mathrm{q_{u}}\) of clay soils were experimentally investigated, and the obtained data were used in an ANN to estimate the \(\mathrm{q_{u}}\) value according to the basalt fiber reinforcement ratio and length. Following a comprehensive experimental investigation, \(\mathrm{q_{u}}\) results were derived from the data collected during the experiments. The primary objective of this research is to enable the estimation of \(\mathrm{q_{u}}\) for mixtures reinforced with basalt fibers of varying lengths and proportions without the need for additional experimental work, and to determine the optimal basalt fiber reinforcement ratio and length.