Research On The Beam Structural Health Diagnosis Based On Moving Load

As an important part of transportation, Bridge is the focus of the national infrastructure construction, but also a symbol of economic development and technological progress. In recent years, the bridge structure health monitoring technology has become a research hotspot in engineering field. Health monitoring system and theoretical research has also made great progress. However, due to health monitoring system itself multidisciplinary nature, large bridge structure and the complexity and uncertainty of environment, many of the key technology of the bridge structures health monitoring system still exist many problems from theory to practical application.This paper combines theory with experiment; some key technical problems in beam structure health monitoring process were studied. Based on the technology of structural damage identification on the vibration characteristics, this paper has carried on the exploration of signal processing, feature parameter identification of bridge structural health monitoring system using the advanced method in the field of information theory, signal processing, time-frequency analysis, and statistical analysis. The major work of this paper can be presented in the following aspects:(1) According to the moving characteristics of traffic load, the moving load is put on beam structure, and the vibration response data were analyzed. When putting the moving load on the beam structure, characteristic parameters of vibration response data caused by damage will be amplified, and its extraction accuracy can be improved.(2) The observation data obtained in the process of bridge structure health monitoring is complex in nonlinear and non-stationary, sample entropy can effectively represent the signal complexity, estimate the signal nonlinear. This paper proposes using sample entropy to extract structural damage information, and improves this method using empirical mode decomposition and neural network.(3) When moving load is put on the damaged structure, vibration signal is non-stationary, and vibration response statistics of damage structure will change with time and the load. This paper presents the time-frequency analysis method for structural damage identification, discusses the cross-term problem of the time-frequency analysis, analyze the method of suppressing cross-term interference, and combine the information entropy and neural network for structural damage identification.(4) Higher-order statistics signal has a good non-gaussian, non-stationary signal processing ability. This paper proposes the high-order spectrum analysis method for damage structure vibration signal. Due to the high-order spectrum analysis results is two-dimensional or even more higher-dimensional, it contains a huge amount of information. This paper puts forward a bispectrum valid values entropy analysis method, combined with pattern recognition ability of neural network for structural damage identification.(5) In order to fully excavating the structural damage information contained in the higher-order spectrum analysis results, it is necessary to using the effective dimension reduction analysis method. This paper presents an improved supervised locality preserving projection dimension reduction method. Feature vectors can be extracted from the high-order spectrum analysis results. The damage identification process is divided into two information fusion modules, and then completed with neural network.