A machine learning and optimization algorithm-based framework containing three components is developed to quantitatively derive the optimal woodframe building retrofit policy. First, machine-learning-based surrogate models are developed as compact statistical links between building structural characteristics (e.g. number of stories, story strengths, building configuration) and nonlinear response history analysis (including collapse simulation) and performance assessment (e.g. collapse, demolition, repair costs) outcomes. Second, objective functions are defined at the regional or portfolio scale (e.g. maximizing the total increase in collapse margin ratio for all buildings, minimizing total losses for a scenario earthquake) with appropriate constraints (e.g. minimum increase in collapse margin ratio for any single building, maximum cost of retrofit). Lastly, stochastic optimization algorithm is implemented to determine the retrofit enhancements (e.g. increase in strength and ductility) that would achieve the most desirable combined outcome for all buildings in the portfolio. 

Ground-Motion time-series is essential to analyze the damage state of a facility after an earthquake. The number of ground motion recording stations is sparse, and there are many crucial locations, which are uninstrumented. A method is sought to estimate the ground motion time series that a facility experienced during an earthquake by interpolating the observed surround motions.