Distributed Intelligent Bridge Monitoring System (DIBMS) is a set of system integrating both hardware and software to automatically collect and analyze data from a remote bridge. The real-time data can be preprocessed in the sub-workstation on the bridge and then transferred to the main server with a wired or wireless Internet access. DIBMS is based on the statistical analysis of the static and dynamic characteristics of structures. In order to automate the procedure of processing and analyzing all the raw data, a Fuzzy-logic diagnosis system was developed for the monitoring system with Bootstrap Method. In general, the estimation of parameters from measurement always contains systematic perturbations and random fluctuations. The systematic perturbations can be identified and characterized. Based on historical data, a set of correction models has been built to remove the influence from systematic perturbations. Random fluctuations are difficult to be captured by traditional statistical methods. But with the Bootstrap Method, we can minimize the random error. A modern bridge is such a complicated system that it is difficult and inefficient to analyze it by conventional mathematic tools. The fuzzy logic theory overcomes the difficulty of defining an explicit relationship between the current system states and the actions required to achieve a certain system performance by using linguistic rules. Traditional bridge health diagnostic assessments can not systematically cover all relevant modelling possibilities for explaining observations from the field measurement. The introduction of a fuzzy logic algorithm into this system facilitates the rule-based bridge monitoring expert system. By applying the system on an in-service PC bridge, it has been verified that this fuzzy logic expert system is effective and reliable for the bridge health evaluation.
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