Bayesian　inference　methods　typically　require　a　considerable　amount　of　computation　time　in　the　calculation　of　forward　models.　This　limitation　restricts　the　application　of　Bayesian　inference　methods　for　the　parameter　identification　of　complex　engineering　problems.　We　propose　a　novel　likelihood-free　Bayesian　inference　method　for　structural　parameter　identification.　An　adaptive　Gaussian　surrogate　model　(GSM)　was　integrated　with　the　transitional　Markov　chain　Monte　Carlo　(MCMC)　method　for　Bayesian　inference.　The　log-likelihood　function　was　approximated　with　GSM　and　the　transitional　MCMC　method　was　used　to　generate　the　posterior　distribution　samples.　A　response　reconstruction　technique　was　combined　with　the　likelihood-free　Bayesian　inference　method　for　the　parameter　identification.　Both　numerical　studies　and　experimental　studies　were　conducted　to　verify　the　accuracy　and　efficiency　of　the　proposed　method.　The　results　showed　that　the　proposed　method　could　be　used　to　estimate　the　posterior　probabilities　of　unknown　structural　parameters.　Additionally,　the　proposed　method　was　more　efficient　than　the　delayed　rejection　adaptive　Metropolis　and　Gibbs　sampling　methods.