# Prediction of Unconfined Compressive Strength in Cement-Treated Soils: A Machine Learning Approach
## 1 Introduction
Alternative ML techniques have also shown promising results. [PERSON] et al. [38] applied Support Vector Regression (SVR) to model UCS in geopolymer-stabilized clays, showing that SVR can effectively capture non-linear parameter interactions and outperform traditional empirical models. [PERSON] et al. [39] evaluated multiple ensemble ML algorithms, including Gradient Boosting, k-nearest neighbors, SVM, and Random Forest, finding that Gradient Boosting and k-nearest neighbors achieved the highest accuracy for UCS prediction in cement- and lime-stabilized soils. [PERSON] and [PERSON] [40] proposed advanced ensemble and hybrid ML models, demonstrating that Extreme Gradient Boosting(XGBoost) can achieve R\({}^{2}\) values up to 0.99 for UCS prediction in soft soils, underscoring the importance of selecting relevant input features. Recent developments have pushed the boundaries further with deep learning and hybrid approaches. [PERSON] et al. [41] explored deep learning methods--including convolutional neural networks (CNNs), long short-term memory networks (LSTM), and backpropagation neural networks (BPNNs)--trained on both real and synthetic data, showing that generative adversarial networks (WGAN) can significantly enhance model generalization for predicting UCS in geopolymer-stabilized soils. [PERSON] et al. [42] extended previous work by integrating SVR with metaheuristic algorithms, demonstrating that hybrid models (SVR-HGS, SVR-PSO) further improve predictive performance depending on input variables. Additionally, [PERSON] et al. [12] pioneered the use of explainable AI (XAI) to rank the importance of strength-controlling factors in cement-treated soils and generate design charts for mix optimization, bridging the gap between black-box predictions and engineering understanding. The superiority of ML approaches for this application stems from their ability to capture non-linear interactions between multiple variables simultaneously, learn from data patterns without predetermined functional forms, and adapt to local soil conditions through training on site-specific datasets. Studies by [PERSON] et al. [43] and [PERSON] et al. [44] have shown that factors such as cement content, fine particle fraction, and dry density are critical for ANN-based UCS predictions in sandy and unsaturated cemented soils, respectively, and that evolutionary hybrid ML models efficiently capture complex non-linearities. These collective findings highlight a growing consensus that ML techniques not only improve the accuracy of UCS prediction for cement-treated soils but also provide valuable insights into the controlling mechanisms of soil strength development across diverse geotechnical applications. Based on recent advances and ongoing challenges in the literature, this study addresses the effects of compaction energy, cement dosage, and their impact on strength development in stabilized soils. The influence of compaction velocity on UCS development is examined systematically, as this parameter is often overlooked in research, despite being important in practice. The experimental program varies compaction velocities (0.75-1.25 mm/min), cement contents (2.5-10%), and curing times (1-28 days). This approach covers a wide range of scenarios, from lightly stabilized subgrades to highly cemented deep mixing. In addition, we combine laboratory testing with modern machine learning techniques. The result is a predictive model, made publicly available, that allows rapid estimation of UCS without extensive laboratory work. Unconfined compressive strength was chosen as the principal performance indicator for its practical advantages: ease of testing, reproducibility, cost-effectiveness, and its proven relevance in both geotechnical and concrete engineering. The UCS test is uniquely suited for monitoring the strength evolution of cemented soils, especially at higher cement dosages and extended curing periods, where conventional soil tests may become unreliable or insufficient [23; 39; 45; 46].