A　self-sensing　magnetorheological　(MR)　damper　with　embedded　piezoelectric　force　sensor　has　recently　been　devised　to　facilitate　real-time　close-looped　control　of　structural　vibration　in　a　simple　and　reliable　manner.　The　development　and　characterization　of　the　self-sensing　MR　damper　are　presented　based　on　experimental　work,　which　demonstrates　its　reliable　force　sensing　and　controllable　damping　capabilities.　With　the　use　of　experimental　data　acquired　under　harmonic　loading,　a　nonparametric　dynamic　model　is　formulated　to　portray　the　nonlinear　behaviors　of　the　self-sensing　MR　damper　based　on　NARX　modeling　and　neural　network　techniques.　The　Bayesian　regularization　is　adopted　in　the　network　training　procedure　to　eschew　overfitting　problem　and　enhance　generalization.　Verification　results　indicate　that　the　developed　NARX　network　model　accurately　describes　the　forward　dynamics　of　the　self-sensing　MR　damper　and　has　superior　prediction　performance　and　generalization　capability　over　a　Bouc-Wen　parametric　model.