Output-only structural identification with random decrement technique

Most of existing time domain methods fail to identify the unknown structural parameter in the ambient con-dition, because the force location is unknown and the number of unknown forces is usually larger than the number of sensors. A novel output-only structural identification method based on the random decrement (RD) technique and covariance matrix adaptation evolutionary strategy (CMA-ES) is presented. The RD functions of a structure under ambient excitation are extracted and treated as free vibration responses for structural parameter identification. The unknown structural parameters are identified by minimizing the discrepancy between the measured and calculated RD functions with the CMA-ES. Unlike traditional output-only methods, which require the estimation of the input excitation in the process, the proposed method does not require the identification of the input forces, thereby significantly reducing the computational cost. Numerical studies on two-and three-dimensional trusses and experimental studies on a steel frame are presented to demonstrate the accuracy and efficiency of the proposed approach. The effects of measurement noise and measurement duration on the identified results are examined. Results show that the proposed method can obtain satisfactory results even with 20% measurement noise and structural damage less than 10% can be detected. Comparison studies with the genetic algorithm (GA) and particle swarm optimization (PSO) are presented to verify the computational effi-ciency of the proposed method. Results also show that the proposed CMA-ES has a faster convergence speed than the GA and PSO method. The results demonstrate that the proposed technique can be used to detect damage severity and location in structures under ambient conditions.