Identification of vehicle axle loads from bridge responses using preconditioned least square QR-factorization algorithm.

• A preconditioned least square QR-factorization approach is developed for moving force identification. • The regularization matrix L is introduced to improve the ill-posed problems. • The number of iterations j is introduced to avoid noise disturbance and ensure the robustness. • Preconditioned least square QR-factorization approach is validated through numerical simulation. This paper develops a novel method for moving force identification (MFI) called preconditioned least square QR-factorization (PLSQR) method. The algorithm seeks to reduce the impact of identification errors caused by unknown noise. The biaxial moving forces travel on a simply supported bridge at three different speeds is used to generate numerical simulations to assess the effectiveness and applicability of the algorithm. Results indicate that the method is more robust towards ill-posed problem and has higher identification precision than the conventional time domain method (TDM). In addition, the robustness and ill-posed immunity of PLSQR are directly affected by two kinds of regularization parameters, namely, number of iterations j and regularization matrix L. Compared with the standard form of least square QR-factorization (LSQR), i.e., the regularization matrix L being the identity matrix I n , the PLSQR with the optimal number of iterations j and regularization matrix L has many advantages on MFI and it is more suitable for field trials due to better adaptability with type of sensors and number of sensors. [ABSTRACT FROM AUTHOR]