The main aim of this study is to develop an automated smart software platform to improve the time-consuming and laborious process of model updating. We investigate the key techniques of model updating based on intelligent optimization algorithms, that is, accuracy judgment methods for basic finite element model, parameter choice theory based on sensitivity analysis, commonly used objective functions and their construction methods, particle swarm optimization, and other intelligent optimization algorithms. An intelligent model updating prototype software framework is developed using the commercial software systems ANSYS and MATLAB. A parameterized finite element modeling technique is proposed to suit different bridge types and different model updating requirements. An objective function library is built to fit different updating targets. Finally, two case studies are conducted to verify the feasibility of the techniques used by the proposed software platform. 1. Introduction The use of monitoring information from structural health monitoring systems facilitates the updating of finite element models with real-time and online structures. By doing so, we can not only update the structural benchmark analyses model but also continuously track the changes in the target physical parameters and the characteristic index in any location [1–3]. The automation and intelligence of the model updating process is the key problem that hinders the achievement of these goals in structural health monitoring system [4, 5]. After 20 years of research, existing structural health monitoring systems at home and abroad have entered the third stage, where the emphasis is on the processing and utilization of data to implement data-based online early warnings and assessments of individual health status [5, 6]. During this stage, the model-based assessment method is simply regarded as a supplementary approach that is used in an offline manner and it has not become the main focus of the online algorithm. Model updating has been studied only to provide a benchmark model for this method [1, 6]. In recent years, the theory and technology of model updating have continued to progress, especially in computational model updating (CMU) and model validation [7, 8] research, some of the techniques used when updating parameter selection [9, 10], uncertainty processing techniques for updating results from continuous monitoring information [11, 12], and alternative technologies that employ small numbers of calculations of complex finite element calculation during a modified iteration step [13–16].
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