Estimating　the　reliability　of　civil　structures　after　earthquake　events　is　a　challenging　problem　because　the　un-certainties　of　random　variables　are　usually　unknown.　This　paper　presents　a　data-driven　approach　for　post-earthquake　reliability　assessments　of　civil　structures.　The　measured　vibration　data　are　used　to　update　the　probability　density　functions　of　random　variables.　Two　approximate　Bayesian　computation　techniques　are　introduced　to　generate　the　posterior　probability　density　functions　of　structural　parameters.　One　is　the　likelihood-free　Bayesian　inference　method,　in　which　the　weighted　sum　of　squared　errors　term　in　the　likelihood　function　is　approximated　by　a　Gaussian　process　model　and　the　posterior　probability　density　functions　is　generated　using　the　Metropolis-Hastings　algorithm.　The　second　method　is　the　variational　inference　approach,　in　which　the　posterior　probability　density　function　is　approximated　by　a　Gaussian　mixture　model.　Both　the　proposed　techniques　can　provide　an　approximation　of　the　posterior　probability　density　functions　of　the　unknown　parameters.　Further-more,　the　updated　probability　density　functions　are　applied　for　reliability　assessments.　Numerical　studies　on　a　steel　frame　structure　and　a　reinforced　concrete　structure　are　conducted　to　verify　the　accuracy　and　efficiency　of　the　proposed　techniques.　Results　show　that　the　posterior　probability　density　functions　of　unknown　structural　parameters　can　be　updated　and　the　reliability　of　the　structure　and　components　can　be　estimated.