Probabilistic Evaluation of Flexural Demand in RC Beams Through Monte Carlo Simulation

This study presents a stochastic approach to assess bending moment demand in reinforced concrete beams subjected to vertical loads, incorporating uncertainties in material properties, geometry, and loading conditions. A Monte Carlo simulation framework was developed in Python version 3.9.3 using the OpenSeesPy library to analyze the variability of internal forces based on probabilistic input parameters. The analysis focuses on a four-span continuous beam representative of typical structural configurations in buildings. Probability distributions were assigned to key structural design parameters such as the unit weight of concrete ((Formula presented.)), beam dimensions (b, h), column dimension (a), and applied loads, based on standard statistical assumptions and design guidelines. A total of 10,000 simulations were performed to obtain statistical descriptors of bending moment demand across the different spans. The results reveal significant variability in moment magnitudes, underscoring the importance of accounting for uncertainty in structural design. The proposed methodology enables the estimation of demand distributions and the identification of critical spans with higher sensitivity to parameter variations. Although the study does not evaluate structural capacity or failure probability, it contributes to the integration of stochastic techniques in the preliminary stages of design. Future work may include the incorporation of reliability 16 indices and comparisons with design code values.