Yes, statistical models can be used to predict the failure probability of bridge structures under varying load conditions. Approaches such as probabilistic reliability analysis, Bayesian models, and survival analysis are commonly applied in structural engineering. These methods allow engineers to account for uncertainties in material properties, load variations (traffic, wind, seismic), and deterioration over time. However, the accuracy strongly depends on the quality of input data (e.g., inspection records, material tests, monitoring sensors) and the appropriateness of the chosen model. In practice, combining statistical models with finite element analysis and structural health monitoring data often provides more reliable predictions.

Yes, statistical models can effectively predict the failure probability of bridge structures under varying load conditions by analyzing historical data, material properties, and load distributions. Techniques like reliability analysis, regression models, and probabilistic methods (e.g., Monte Carlo simulation) help estimate risk and structural safety under uncertainty.