Theoretical And Experimental Studies Of Damage Detection For Hydraulic Steel Structures Based On Dynamic Characteristics

During the last two decades, there have been great interests in the structural damage identification and health diagnosis using vibration measurements, and the majority of studies can been found in civil engineering. However, studies about the damage identification and health diagnosis of the hydraulic structures are still few, especially for the hydraulic steel structures. Supported jointly by the Innovative Funding of the Ministry of Water Resources of China (No.SCX2003-18) and the National Natural Science Foundation of China (No. 50608036), in this dissertation, the studies on damage identification of hydraulic steel structures are performed, in order to provide a feasible idea for the healthy state assessment and damage identification of the hydraulic steel structures (such as the hydraulic radial gate), a large and complex spatial structures. The content is as follows:Firstly, the methods of structural damage identification developed in the recent two decades are reviewed and summarized. Secondly, theoretical developments and application studies about the damage identification methods are performed in the following parts of this dissertation. As for the shortcomings existing in the traditional genetic algorithm based damage identification methods, a modification has been made to the mutation operator in the traditional genetic algorithm, and an improved genetic algorithm based damage identification method is proposed, which can minimize the blindness of solution to some extent and improve the accuracy of identification results obviously. The applicability of a two-storeyed frame structure and a laboratory frame structure with bolted joints all demonstrates the feasibility of this method in structural damage identification. However, it's the main disadvantage to hamper its real application that the convergence speed of the genetic algorithm is inherently slow. Thus a new structural damage identification approach is proposed based on the trust-region method, which obtains the identification results by minimizing a bound constrained nonlinear least-squared problem, and can make the identification process more robust and timesaving. A followed expansion process of the original objective function is also investigated, which is very important for the real application of the proposed damage identification method. The results of numerical studies of the two-storeyed frame structure and experimental studies of the two laboratory-tested frame structures all demonstrate the feasibility and efficiency of the proposed method. A further application study of this method is on a typical hydraulic steel structure, i.e., a hydraulic radial gate. After model updating to the initial finite element model (FEM) with the experimental vibration data under the healthy state, a refined FEM is obtained and used to identify structural damages using the tested modal data under damaged states. The assessment results show that damages can be identified using both the numerical and experimental modal data, which indicate that application of the method in the damage identification of the hydraulic steel structures is feasible and efficiency, and it also provided a new idea of the healthy state assessment and damage identification to the hydraulic steel structures. Finally, the influence of experimental noise on the identification results is also investigated for the sensitivity-based damage identification methods. A novel statistic-based random noise analysis method is proposed, which does not only make the analysis process more efficient but also can analyze the influence of noise on both frequencies and mode shapes for three commonly used sensitivity-based damage detection methods in a similar way. In addition, an improved sensitivity-based damage identification method is also proposed. A one-story portal frame is adopted to evaluate the validity and efficiency of both the proposed noise analysis technique and the improved damage identification method, and the assessment results also show that the improved method is more robust to experimental noise than its normal one.