Schmidt hammer rebound method, a rapid technique for predicting uniaxial compressive strength (UCS), is extensively utilized in rock engineering. However, initially designed for concrete testing, this method encounters limitations when adapted for rock testing due to pronounced differences in density, mineral matrix strength, and anisotropy between concrete and rocks. Consequently, this paper introduces an innovative integrated optimization design approach that combines a Kriging surrogate model with a hybrid particle swarm optimization and gray wolf optimization (PSO-GWO) algorithm. The optimization objectives focus on minimizing damage to samples post-testing, enhancing sensitivity to different lithologies, and strengthening the correlation between rebound height (RH) and UCS. After identifying the relevant design parameters, the proposed method obtains responses and deviations within the range of design parameters, even with limited experimental data. Recognizing that rock is a damaged material, this study establishes a method to quantify the initial damage (Di) of rock samples. The sensitivity analysis elucidates the actual impact of Di on the objective functions and the interplay among design factors. Finally, accounting for the trade-offs between optimization objectives and practical applications, a set of Pareto optimal solutions and uncertainty predictions are generated for laboratory and in-situ testing conditions, respectively. This enables decision-makers to select solutions that align best with their specific research requirements and priorities. This research not only offers innovative perspectives on the application of the Schmidt hammer in rock mechanics but also presents a feasible solution for optimizing measurement equipment for damaged materials, such as rocks.
