February 7, 2025: NHERI Computational Symposium

MSc Student Aman Karki presented his research on quantifying the influence of modeling uncertainty on assessing the collapse capacity of civil structures under dynamic earthquake loading.

Influence of Damage Modeling Uncertainty on Assessment of Structural Collapse under Dynamic Earthquake Loading

Prediction of extreme limit states in civil structures subjected to earthquake loading is critical for performance-based seismic design (PBSD) but may be associated with substantial uncertainties due to the limitations of the engineering modeling tools. In addition to the inherent randomness in structural and material properties (aleatory uncertainty) considered in PBSD, epistemic uncertainty due the challenges of simulating the strength and stiffness deterioration in structures can lead to biased estimates of damage states and collapse capacities, influencing decisions on structural safety and post-earthquake functionality. This study evaluates the influence of the modeling uncertainty on the collapse assessment of reinforced concrete structural components under dynamic earthquake loading using models of different fidelities, including (1) a conventional lumped-plasticity frame model which uses nonlinear springs to represent several damage mechanisms, (2) a new regularized distributed-plasticity frame model created by the second author, which utilizes a nonlocal damage technique to address strain singularities in representing the deterioration of concrete and steel. We create a framework that integrates our numerical models with the NHERI SimCenter tool quoFEM (Quantified Uncertainty with Optimization for the Finite Element Method) to study effect of the modeling uncertainty in structural collapse assessment due to the constitutive parameters controlling the post-peak response of the structural components. Analysis of hundreds of nonlinear dynamic simulations reveals significant bias in the estimated structural collapse capacity due to the choice of the numerical modeling approach. We highlight the strengths and deficiencies of the different modeling strategies and identify promising approaches to reduce modeling uncertainty.