Composite materials, known for their enhanced mechanical strength through fiber reinforcement, are increasingly used in industrial applications. However, like metals, they suffer strength degradation from cyclic loading, making fatigue failure a critical concern. The fatigue behavior of polymer composites is strongly influenced by factors such as stress amplitude, stress ratio, and loading frequency. Conventional stress-life approaches often treat these factors independently, which limits their predictive accuracy. This study introduces a novel frequency–stress–ratio fatigue index, integrated into a residual-strength degradation approach, to predict fatigue life under constant amplitude, tension-tension loading conditions. To the best of the authors’ knowledge, the majority of models available in the literature require substantial experimental data to calibrate the parameters essential for their application in real-world scenarios. The proposed model requires only two material parameters and assumes failure occurs when the residual strength degrades to the level of the applied maximum stress. The results demonstrate that the proposed formulation provides a unified representation of fatigue behavior influenced by both cycle-dominated and time-dependent mechanisms, offering robust predictive capabilities. This approach not only addresses a critical need for reliable and practical fatigue prediction methods in composite materials but also contributes significantly to the optimization of engineering design processes.

La mampostería en México se ha constituido en un sistema de construcción tradicional, la combinación de este sistema constructivo en algunos edificios de concreto armado puede contribuir al incremento de resistencia y rigidez. Sin embargo, cuando las distribuciones de los muros de mampostería se colocadas de forma irregular, pueden significar un riesgo para el sistema estructural, la ausencia de muros en planta baja provoca una irregularidad denominada piso suave. Este trabajo presenta el análisis de una familia de edificios de cinco niveles bajo la influencia de sismos de alto riesgo, generados en la Costa del Pacifico mexicano. Para tal efecto, se realizó un diseño sísmico convencional de la estructura, a base de marcos ortogonales de concreto armado, sin considerar la presencia de muros de mampostería. El modelo resultante fue sometido al análisis probabilístico para obtener el escenario correspondiente a la variación estadística relativo a valor de cargas, características geométricas y propiedades mecánicas de los elementos estructurales. Después se consideran modelos con y sin muros, analizándolos por medio de métodos no lineales para obtener su capacidad inicial y demanda sísmica, donde las distorsiones máximas de azotea y de entrepiso fueron parámetros a considerar. La configuración de los edificios con mampostería consideró a los muros de relleno ligados a los marcos que se ubican por encima de la planta baja. Lo anterior permitió obtener información cuantitativa que hizo posible concluir que la presencia de muros de mampostería cambia el comportamiento global del sistema y repercute en el índice de daño.

SIB (Simulation of Buildings) is an open-source, modular Fortran 90/95 code that generates stochastic realizations of reinforced concrete (RC) buildings as three-dimensional frame models. The code simulates three classes of uncertainty simultaneously: (i) spatial sustained live loads, modeled through three alternative probabilistic models; (ii) geometric variability in cross-section dimensions and concrete cover; and (iii) variability in reinforcement, concrete unit weight, and material-strength parameters. Each simulated building is written to a generic text format and, optionally, to OpenSees input file for downstream nonlinear structural analysis. The modular architecture separates storage from procedure modules and is designed for extension to additional probabilistic models, member types, and analysis platforms. A three-story, single-bay RC building is used as an illustrative case, and the simulated populations are verified against the probabilistic models from which they were drawn.