An intelligent data fusion framework for structural health monitoring

We propose a structural health monitoring framework that is based on the hybrid intelligent systems to compute composite structure health index as structure health indicator. With our proposed health structure monitor framework, it provides a decision-level analysis for structure health monitoring. In contrast, recent studies focus on signal processing, mechanical modelling and computer based systems, which are not robust enough to provide decision-level analysis confronted with heterogeneous data sources. Therefore, data fusion technique is required to alleviate possibilities of data conflicts, lack of conciseness and incompleteness that arise from the heterogeneous data sources to compute composite structure health index, which is essential for risk analysis. Our proposed intelligent data fusion framework is based on hybrid adaptive resonant theory neural network (superior learning from dynamic data) and adaptive fuzzy inference (superior reasoning with dynamic fuzzy rules derived from time series data formulated membership functions) systems. The outcome of our proposed framework is a structure composite health index, an aggregated index via optimal weighted variables. We implement our framework using a set of GPS and Wind velocity sensors output from a real bridge to evaluate our framework. These sensors data used in our study are collected from long cable-bridge. Our case study showed that our proposed intelligent data fusing framework is capable of reporting the structural status correctly.