A load uncertainty quantification-based method for loading reliability assessment of full-scale structural test

In full-scale structural testing, applied loads are subject to the influence of multiple interacting factors, resulting in complex nonlinear relationships and inherent uncertainty. To evaluate the impact of such uncertainty on loading reliability, a reliability assessment framework based on Load Uncertainty Quantification (LUQ) is proposed. A piecewise machine learning modeling strategy is first utilized to construct a predictive model for load uncertainty parameters derived from historical load data. Quantification of load uncertainty is subsequently performed by integrating specific test input conditions. In order to account for both permissible load deviation thresholds and the statistical dependencies among uncertainties at various loading points, a Copula-based method is adopted for evaluating loading reliability. A case study focused on a Full-Scale Structural Test (FSST) is used to verify the proposed methodology, demonstrating its accuracy, effectiveness, and applicability. The findings offer meaningful insights for reliability assessment in other complex structural testing contexts.