Strain gauges based on the micro-strip patch antenna have been increasingly employed in structural health monitoring. However, the lower bandwidth, influenced by the antenna’s geometric properties, limits efficiency of the antenna when major strain, creating drastic variation of the resonant frequency, is applied. The performance of the antenna cannot be guaranteed without also considering the substrate’s varying thickness, caused by manual fabrication and printing procedure. However, all such considerations lead to an increase of multivariate design variables, that in turn, increase uncertainty and computational costs. Thus, the proposed research develops a framework that accurately models the geometric variables of the antenna and efficiently reduces the multivariate dimensions that draw uncertainty preventing accurate system reliability estimation. In the proposed framework, a dimension reduction method is thoroughly conducted by utilizing a critical decision criterion depending on the degree of correlation. Specifically, artificial neural network and probabilistic neural network are employed to correctly estimate the variability of complex system responses. Furthermore, an optimal design of the stretchable patch antenna is developed. This design will allow frequency shifts under tensile strain and still remain within reliable frequency ranges. The proposed approach is beneficial to the process of capturing and managing antenna design variables. The presented example clearly demonstrates the advantage of the obtained optimal design of the stretchable patch antenna compared to an ultra-wideband radar system that often requires complicated design processes and high computational costs.

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