Analytical and experimental study on the artificial-neural-network-based health monitoring and damage diagnosis of structures
|關鍵字:||類神經網路;結構健康監測;破壞診斷;光纖光柵應變計;振動臺試驗;ANN;structural health monitoring;damage diagnosis;FBG;shaking table test|
The main purpose of this dissertation is attempts to organize an integrated system for the health monitoring and damage diagnosis of smart structures through the extensive analytical and experimental study. Two major researches are investigated in the analytical study, which are (1) development of ANN-based system identification model and (2) development of ANN-based damage diagnosis approach. On the first research, a novel ANN-based system identification (ANNSI) model was proposed for identifying the modal parameters of a structure from its vibratory responses. The modal parameters can be directly estimated from the weighting matrices of a trained ANN, and further be used for diagnosing a structure. Moreover, the proposed global and decentralized monitoring networks can be used for not only identifying but also monitoring the dynamic characteristics of the structure and sub-structure, respectively. On the second research, a damage detection approach, which is based on the damage localization feature (DLF) and an unsupervised fuzzy neural network (UFN), is proposed. It is shown that DLF is correlated with damage location but independent of damage extent. As a result, it is used as indicator to identify the damage location. Detection of structural damage is an inverse problem, and the solving procedure for this problem is a kind of pattern recognition which is very suited to be implemented by unsupervised fuzzy neural networks. Through the use of the UFN, the damage site is located by matching two sets of the damage feature, the analytical DLF which is generated from an analytical model and the measured DLF which is computed according to the identified modal data. Subsequently, estimation of the damage extent is implemented by the proposed algorithms after the damage location is identified. The developed model or approaches in the analytical study are examined by either numerical or laboratory examples. The simulation results reveal the capability and practicability of the proposed methods. In the experimental study, a scaled-down four-story steel frame structure was designed to conduct the health monitoring study on the shaking table. The structural deterioration is simulated by reduction of the story stiffness. Three types of sensors, such as accelerometers, fiber Bragg grating (FBG) sensors, and resistant strain gages (RSGs) were installed on the specimen to measure the structural acceleration and strain responses during the shaking table tests. The experimental program aims to perform four tasks: (1) verification of the proposed ANNSI model; (2) verification of the proposed damage diagnosis strategies; (3) investigation of the capabilities of the fiber Bragg grating sensors for structure monitoring; and (4) exploration of other damage related indicators. It is found from the experimental study that the modal data for each simulated deterioration case can be successfully identified form the acceleration and strain measurements by using the ANNSI model, and the variations of the identified modal data in various deterioration cases are highly correlated with the simulated deterioration states. Meanwhile, most simulated deterioration states can be identified by the proposed damage diagnosis strategies. Additionally, the FBG sensors are revealed promising for the structural monitoring because of their distinguishing advantages. For examples, the system identification becomes easier and more accurate as a result of the feature of low noise effect; and the characteristics of light weight, small size, and multiple sensors along a single fiber make the FBG sensors more potential than the RSGs for practical infrastructures where a great quantity of sensors is usually needed. Based on the results from analytical and experimental study, an integrated health monitoring system is proposed in this dissertation. The system is designed to be capable of on-line system identification, monitoring, diagnosis, and warning. The advantages of the system are: (1) unsuccessful diagnosis by any of the damage strategies will not cause failure in the system performance since the system is integrated with various diagnosis strategies; (2) the system is adaptive because ANNs, which formed the basis of the system, are expected to improve their performance as they acquired more experiences from the environment.
|Appears in Collections:||Thesis|