A new pinched nonlinear hysteretic structural model for automated creation of digital clones in structural health monitoring

Abstract

Optimizing risk treatment of structures in post-event decision-making is extremely difficult due to the lack of information on building damage/status after an event, particularly for nonlinear structures. This work develops an automated, no human intervention, modeling approach using structural health monitoring results to create accurate digital building clones of nonlinear structures for collapse prediction assessment and optimized decision-making. Model-free hysteresis loop analysis structural health monitoring method provides accurate structural health monitoring results from which model parameters of a nonlinear computational foundation model are identified. A new identifiable nonlinear smooth hysteretic model capturing essential structural dynamics and deterioration is developed to ensure robust parameter identification using support vector machines. Method performance is validated against both numerical and experimental data of a scaled 12-story reinforced concrete nonlinear structure. Results of numerical validation show an average error of 1.5% across 18 structural parameters from hysteresis loop analysis and an average error of 2.0% over 30 identified model parameters from support vector machines in the presence of 10% added root-mean-square noise. Validation using experimental data of the scale test reinforced concrete structure also shows a good match of identified hysteresis loop analysis and predicted nonlinear stiffness changes using the digital clones created with an average difference of 1.4%. More importantly, the predicted response using the digital clones for the highly nonlinear pinched hysteretic behavior matches the measured response well, with the average correlation coefficient Rcoeff= 0.92 and average root-mean-square error of 4.6% across all cases. The overall approach takes structural health monitoring from a tool providing retrospective damage data into automated prospective prediction analysis by “cloning” the structure using computational modeling, which in turn allows optimized decision-making using existing risk analyses and tools.