Assessing　the　collapse　region　in　the　progressive　collapse　of　buildings　is　one　of　the　important　issues　in　urban　planning　for　disaster　recovery.　A　rapid　visual　simulation　method　for　the　progressive　collapse　of　regular　rein-forced　concrete　(RC)　frame　structures　was　proposed.　First,　a　modeling　solution　using　affine　transformation　and　modification　script　was　designed　to　rapidly　create　a　geometric　model.　Second,　a　machine　learning　model　of　the　internal　collapse　area　and　a　horizontal　collapse　propagation　algorithm　were　built　to　predict　the　internal　collapse　region.　Third,　a　visual　simulation　method　based　on　Voronoi　tessellation　and　physics　engine　was　proposed　to　represent　the　collapse　process　of　the　RC　frame.　Finally,　the　accuracy　and　efficiency　of　the　proposed　method　were　validated　using　the　collapse　event　of　the　Murrah　Federal　Building　and　finite　element　models,　and　the　effect　of　the　mesh　size　of　Voronoi　tessellation　and　different　initial　failures　on　a　collapse　scenario　was　further　discussed.