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Owing to their sufficient ductility, conventional steel moment-resisting frames (MRFs) are widely used as seismic-resistant systems in building structures. However, the ductile behavior introduces significant residual displacement after earthquakes, leading to massive economic losses caused by difficult or even unfeasible repairs. This paper intends to propose a machine learning-driven probabilistic residual displacement-based design method for retrofitting MRFs using self-centering braces (SCBs) and improving the post-earthquake repairability by reducing the residual displacement to a target level. The influence of the SCB's design parameters on the peak and residual displacements of the enhanced MRF are first investigated through parametric dynamic analysis of single-degree-of-freedom (SDOF) systems. The probabilistic residual displacement prediction models are developed using different machine …