Abstract
The integrity of structural health monitoring data plays an important role in extracting data features. An improved Kriging model based on weighted self-adaptive differential evolution algorithm (KWSDE) is proposed to repair the spatial missing data. In the KWSDE model, the control parameters of variogram in Kriging are optimized by a novel differential evolution (DE) with both scale factor and crossover rate adaptation. Besides, a weighted least-square method is presented for the optimization process to restrain the variable scale of the fitness function, which makes it more suitable for the lack of monitoring data. Finally, a distributed optical fiber monitoring experiment is designed to verify the reliability and functionality of the proposed model. Compared with the Kriging based on DE model (KDE), the interpolation precision of the KWSDE model can be improved by more than 10%, which has great significance for its engineering application.
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