A　parameter　identification　framework　has　been　developed　based　on　physics-informed　neural　networks　(PINNs).　Physical　constraints　are　taken　into　account　during　the　training　process　of　a　PINN,　creating　a　grey-box　running　mechanism.　Two　information　acquisition　principles　are　proposed　for　training　data　sets　and　physical　constraints.　Specifically,　finite　element　computation　is　incorporated　with　the　uniform　design　to　generate　the　minimum　number　of　training　data　for　PINNs.　Then　multivariate　nonlinear　regression　is　applied　to　the　training　data　to　establish　the　physical　constraints,　which　are　used　as　a　rule　model　added　to　the　loss　function　for　training　evaluation.　This　step　guides　the　training　process　towards　a　physically　or　mechanically　consistent　solution,　instead　of　a　pure　data　association.　Thereby　the　training　of　PINNs　involves　the　physical　governing　laws,　leading　to　a　physicsinformed　data-driven　approach.　Finally,　the　proposed　PINNs　were　used　to　identify　the　stiffness　parameters　of　a　laboratory-scale　frame　model　and　an　actual　frame　structure.