Aerostatic bearings are critical components in ultra-precision systems, valued for their near-frictionless motion, high positional accuracy, and clean operation. However, their reliability is compromised by crash events, which occur when the loss of air film separation leads to direct surface contact during relative motion. This thesis presents a parametric finite element model to assess crash resistance by quantifying the impact of surface damage on load-carrying capacity and static stiffness. The model incorporates experimentally observed wear geometries into a coupled Reynolds equation and restriction model, enabling stochastic evaluation through uncertainty quantification and sensitivity analysis. A key contribution is the identification of negative bearing stiffness as a previously unreported failure mechanism, in which a decrease in air film height leads to a drop in load-carrying capacity rather than an increase, creating an unstable condition that can result in surface contact. This behavior is caused by ridge formation, where displaced material accumulates along the damage region and creates protruding features that obstruct local pressure buildup. Experimental analysis using digital microscopy and white light interferometry validates the approximated wear shapes and consistently reveals groove-like damage accompanied by ridge formation. Sensitivity analysis shows that damage location and height have the strongest influence on performance loss, with notable interaction effects. Comparative simulations demonstrate that damage without ridges reduces the impact on bearing performance, highlighting the critical role of damage geometry. The developed framework enables evaluation of crash resistance and supports the design of more robust aerostatic bearings for high-tech applications.