## 1 Introduction  
Accurate prediction of the uniaxial compressive strength (UCS) of rock masses is essential for the safe and cost-effective design of a wide range of geotechnical and civil infrastructure, including foundations, tunnels, and slope stability assessments. Traditional empirical and regression-based models--linking UCS to parameters such as porosity, density, or ultrasonic pulse velocity--provide valid first-order estimates but frequently underperform when applied to heterogeneous, partially saturated, or karst-affected carbonate formations. This performance gap stems from the rigid functional forms and limited textural variability assumed in these models, which fail to capture complex pore structure-mechanical strength relationships observed in field settings [1, 2, 3]. In the Seybaplaya bank rocks of Campeche, Mexico--a formation characterized by mixed biocalsic limestones, coquinas, and breccias with wide-ranging water content, interconnected porosity, and skeletal density--such limitations become particularly acute, leading to potential under- or overestimation of rock strength and consequent engineering risk.  
Recent advances in machine learning offer promising alternatives: artificial neural networks (ANNs) can automatically learn complex, nonlinear interactions among multiple geomechanical inputs without requiring explicit correlation assumptions. Multiple studies have demonstrated that ANNs, when trained with features such as water content, porosity, and density, achieve markedly improved predictive accuracy for UCS across diverse lithologies and saturation states [4; 5]. Further, hybrid frameworks--ranging from adaptive neuro-fuzzy inference systems (ANFISs) to particle swarm optimization (PSO)-tuned networks--underscore the flexibility and robustness of data-driven approaches in rock mechanics contexts.  
Research Gap. Despite these successes, there remains a notable lack of systematic, side-by-side evaluations comparing the effects of different ANN training algorithms and regularization schemes on predictive performance for karst-influenced carbonate rocks. Specifically, the impact of convergence speed, overfitting control, and noise resilience across algorithmic variants--radial basis function (RBF), Bayesian regularization (BR), scaled conjugate gradient (SCG), and Levenberg-Marquardt (LM)--has not been fully elucidated for formations similar to Seybaplaya. Without such comparative insight, practitioners lack clear guidance on selecting the most appropriate network architecture for reliable UCS estimation under field-analog conditions.  
Contributions of This Study. To address this gap, the present work delivers the following key contributions:  
1. Direct Algorithmic Comparison. We implement and rigorously compare four feed-forward ANN architectures (RBF, BR, SCG, LM) on a uniform dataset of 50 Seybaplaya carbonate core specimens, leveraging identical preprocessing pipelines, data split protocols, and performance metrics.
2. Statistical Validation. Through 30 independent training-testing runs per model and hypothesis testing via the Friedman test with Benjamini-Hochberg correction, we objectively determine the statistical significance of inter-algorithm performance differences.
3. Feature Importance Analysis. Employing a partial derivatives sensitivity analysis, we quantify the relative influence of water content, porosity, and density on UCS predictions, providing actionable insight into variable prioritization for field measurements.
4. Practical Recommendations. We synthesize our findings into clear recommendations for selecting ANN training strategies that balance predictive accuracy, convergence efficiency, and generalization robustness in karst-affected carbonate engineering applications.  
This work advances the state of geomechanical strength prediction for heterogeneous carbonate formations by delivering the following key contributions: