Flexibility matrix identification using the moving vehicle induced responses for beam type bridge. (15th June 2022) - He, Wen-Yu; Li, Jun-Fei; Cheng, Hua-Cai; Li, Zi-Bing; Bu, Jian-Qing
Highlights: A flexibility matrix identification method by using the moving vehicle induced responses for beam type bridges. Proportional relationship between flexibility matrix and matrix formulated by influence lines of several points is derived. The flexibility matrix is formulated based on the measured influence lines and the derived proportional relationship. It can identify the flexibility matrix and estimate the static displacement with high accuracy and efficiency at the cost of few sensors. Abstract: Bridge flexibility matrix is widely used in damage detection and condition assessment. As the mass-normalized mode shapes with high spatial resolution are required in formulating the flexibility matrix, the difficult tasks of measuring excitation and arranging a large number of sensors are normally involved. This paper proposes a flexibility matrix identification method by using the moving vehicle induced responses for beam type bridges. Firstly, the proportional relationship between the modal flexibility matrix and the matrix formulated by the bridge influence lines (ILs) of several points is derived theoretically. Then the quasi-static component response of the moving vehicle induced responses is extracted through the analytical mode decomposition (AMD), and the IL of the measurement point is obtained via polynomial fitting accordingly. Finally the flexibility matrix is formulated based on the measured ILs and the derived proportional relationship, and employed to estimate the static displacement of the bridge. Numerical and experimental examples are conducted to verify the accuracy and feasibility of the proposed method. The results indicate that it can identify the bridge flexibility matrix and estimate the static displacement with high accuracy and efficiency at the cost of few sensors. Besides, the proposed method is insensitive to vehicle parameters (velocity, spring stiffness, wheelbase, and weight), road surface roughness and measurement noise.