The Application Of Genetic Algorithm In The Identification Of Structural Modal Parameters

Modal parameter identification is the main content of the modal analysis which plays an important role in structural modeling, sensitivity analysis, damage detection and structural condition monitoring. At present, great achievements have been made in the structural modal parameter identification research, but because of the nonstationarity, nonlinearity and uncertainty for the structural response signals, the modal parameter identification has still a lot of problems to be studied. In this paper, combining with the random decrement technique and signal filtering technique, modal parameter identification of a time invariant structure by using the genetic algorithm (GA) is researched. The main tasks are as follows:1. Modal parameter identification is briefly introduced. At the same time, the frequency domain methods and time domain methods of structural modal parameter identification are summarized and compared, and advantages and disadvantages for those methods are listed respectively. The development situations of the modal parameter identification at home and abroad are summarized.2. This paper discusses the basic concepts, computing processes and basic principles of the GA. The schema theorem and building block assumptions are introduced in detail and the convergence of the genetic algorithm are analyzed. The problem of the premature convergence of the genetic algorithm is also discussed. Ackley function to be minimized as an example is optimized. The results show that the optimization results of the GA are independent on the initial solution. Constraint of the fitness function is less and the GA has global search feature.3. Based on the MATLAB platform, GAs are applied to the frequency domain identification method based on frequency response function and time domain identification method based on mode decomposition. Simulation signals resulting from the systems with single degree of freedom and multi degrees of freedom are used to parameter identification respectively. The impact of the different noise levels on GA identification results is studied. The results show that using GA to identify the modal parameters has high accuracy, fast convergence, strong resistance to noise, and strong robustness.4. Based on measured data from the Ningbo station temporary railway bridge monitoring system, the application of the GA to modal parameter identification in engineering application is studied. Combining the GA with the random decrement technique and signal filtering, parameter identification programs are developed based on the MATLAB platform. By the measured signal, natural frequencies, damping ratios for the Ningbo station railway temporary bridge are identified. Identification results and finite element calculation values are compared. The results show that the application of the GA to the modal parameter identification for large-scale practical projects is feasible and effective.