Time Domain Structural Parameters Identification And Networked Implementation

Many civil infrastructures are now deteriorating due to aging, misuse, lacking proper maintenance, and, in some cases, overstressing as a result of increasing load demands and changing environments. Failure of these infrastructures often leads to a high social consequence. It is therefore critical to evaluate their current reliability, performance, and condition for the prevention of potential catastrophic events. Structural identification and damage detection have become an increasingly important research topic for health monitoring, performance assessment and safety evaluation of engineering structures.On one hand, because of the ability to approximate arbitrary continuous functions, neural networks have drawn considerable attention in civil engineering for identification in a non-parametric manner. However, because of the nonparametric characteristics, most of the proposed methods for structural health monitoring and damage detection with neural networks can just be used to give a qualitative indication or information that damage might be present in the structure, no quantitative identification can be determined. On the other hand, remote structural health monitoring system can provide abundant information for structural identification and damage detection due to its self-monitoring, long-term and on-line characteristics, and it has been applied in some important infrastructures. A networked structural laboratory (NetSLab) platform originally for networked remote collaborative test has been developed at Hunan University recently. The NetSLab platform provides a potential way for remote data transformation. In this study, modifications have been made on the developed NetSLab platform in order to realize structural dynamic response time series measurement transfer.Firstly, a comprehensive review on the traditional damage detection techniques, vibration-based global identification methodologies, and the application of artificial neural network in civil engineering are made. Secondly, a novel two neural networks based structural parameters identification methodology with the direct use of structural acceleration time series is proposed and validated by a shaking table test of a model structure. The rationality of the methodology is explained and the theory basis for the construction of the two neural networks is described according to the discrete time solution of the state space equation. An evaluation index called the root mean square of the acceleration prediction difference vector (RMSAPDV) is defined and employed to identify structural parameters. Based on the trained acceleration-based neural network modeling for a reference structure, and the parameter evaluation neural network that describes the relation between structural parameters and the components of the corresponding RMSAPDVs, the structural parameters of the model frame structure with known mass distribution are identified by the direct use of acceleration measurements. Results show that structural parameters can be identified with acceptable accuracy. The performance of the proposed methodology for a damaged model structure is also studied. Finally, after the updating of the NetSLab, a networked structural parameters identification system based on the novel identification methodology and the updated network platform is developed and test on remote time series file transfer is carried out. Results show that the updated network platform has the potential of becoming a practical tool for remote health monitoring of civil infrastructures.