Treffer: Enhancing Structural Reliability Analysis with Machine Learning-Based Surrogate Models: Theoretical and Experimental Insights

This paper presents a comprehensive framework for structural dynamics and reliability analysis by integrating theoretical modeling of continuous systems and experimental investigations of multi-storey steel frames. First, a beamlike structure with infinite degrees of freedom is analyzed using the Rayleigh-Ritz method, highlighting the importance of partial di!erential equations in capturing distributed mass, sti!ness, and damping. Tuned Mass Dampers (TMDs) are incorporated to mitigate resonant vibrations, and advanced sampling techniques (Latin Hypercube Sampling, Monte Carlo simulation) are employed to quantify structural reliability under uncertain loading conditions. Machine learning models, including Random Forest and Neural Networks, are then developed as surrogate models to predict failure probabilities, revealing critical nonlinear relationships between system parameters. To validate and extend these insights, the framework is applied to a twostorey steel frame tested under controlled laboratory conditions. A mechanical shaker supplies dynamic excitations with varying statistical characteristics-kurtosis, root mean square (RMS), skewness, and crest factor-while force and acceleration measurements capture the structure's real-time responses. By training machine learning algorithms (Random Forest, Gradient Boosting, XGBoost, and Neural Networks) on time-domain features, the study demonstrates the capability of data-driven methods to capture complex vibratory behaviors beyond what standard mechanical models predict.