Intelligent damage identification method for large structures based on strain modal parameters

Abstract

Early damage detection not only improves the safety and reliability of structures but also reduces maintenance cost. However, damage detection is difficult to implement in large structures under ambient excitation because of the limitation of sensors, the uncertainty of ambient excitation, and the global properties of modal frequencies and displacement modes. This paper proposes a new damage detection method that employs the real encoding multi-swarm particle swarm optimization algorithm and fitness functions evolved from strain modes to find the optimal match between measured and simulated modal parameters and to determine the actual condition of structures. The proposed method requires low-frequency modes and incomplete modes and does not require mass normalization of parameters, thus making the method suitable for nondestructive dynamic damage detection of large structures under ambient excitation. Taking a concrete guide wall structure as an example, this paper studied the global searching performance and the sensitivity of the proposed method. The efficiency of the proposed method was analyzed by using different noise levels and sensor numbers. Results show that the proposed method is effective and can be applied in many types of large structures.