Non-destructive testing methods are essential in civil engineering applications, such as evaluating the compressive strength of masonry. This paper presents a fuzzy signature model based on non-destructive in situ measurements and visual inspection, applying weighted geometric mean aggregation in the signature vertices determined by experts. The weights of the aggregation terms were optimized using the Monte Carlo method, genetic algorithm and particle swarm algorithm to ensure that the evaluation by the signature aligned with the results of destructive tests performed on existing masonry. The results of the methods were compared for single and multiple assembled masonry structures using the same objective function. All three methods provided relatively high confidence in finding the extreme values of the objective function on a generated dataset, which accounted for the correlations observed in actual measurements. Accordingly, validation based on real data yielded the expected results, thus demonstrating the model’s suitability for practical application. This study assessed the inherent, analyzing whether symmetric or asymmetric weight distributions affected evaluation consistency. While symmetric weighting simplified aggregation, asymmetry allowed local structural irregularities to be highlighted. In addition, the cost analysis of the optimization methods revealed a disparity in computational cost increments between the two approaches. The presented work outlines the advantages of the different methods and their applicability to structural assessment.

The aim of this paper is to show that the fuzzy signature model based on non-destructive in-situ measurements and visual inspection can be used to obtain equally reliable results on the strength of masonry as destructive tests. The fuzzy signature model was constructed to describe a masonry based on different measurements of. The aggregations of the signature were given as weighted geometric means. Weights of the input values of the aggregations were determined by two different optimization methods: (i) an iteration procedure was chosen, which showed significant differences in the result; (ii) a genetic algorithm was used, which produced similar but more accurate approximations to the results of the destructive tests. The comparison of the two optimization results gave new conclusions that are valuable information for the further development.

Fuzzy signatures have been successfully used for various engineering applications including the strength and condition assessment of structural materials. In this paper, fuzzy signatures were used to determine the compressive strength of masonry based on groups of related measured values. Handling the uncertainty this way seemed useful because of the subjective parts and influencing noise factors of the measurements which are incorporated as leaves in the signature. For such modeling the structure from where the algebraic framework can be obtained which allows making computations with the fuzzy signatures thus determined. Since multiplicative type aggregations are applied on the various material test results assigned to the leaves of the signatures for the determination of the compressive strength of masonry, fuzzy arithmetic multiplication based on Zadeh’s extension principle was applied here. To perform the fuzzy signature calculations, the scalar product and the n-th root of fuzzy numbers were used.