Identifying dynamical system models from measurements is a central challenge in the structural dynamics community. In particular, traditional nonlinear system identification methods require a steady excitation input that is not always available in many practical applications. In this research line, we integrate sparse regression algorithm and measurement instruments to establish a nonlinear system identification platform. The proposed platform is able to predict both numerical model and modal property of structural systems by processing their free vibration response data.