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The damage identification of truss constructions was investigated in this work. Damage detection is defined through an inverse optimization problem. A function defined as a combination of mode shapes and natural frequencies is examined to minimize damage structures. This guided approach considerably reduces the computational cost and increases the accuracy of optimization. This index mostly exhibits an acceptable performance. Gold Rush Optimization (GRO), an artificial intelligence system based on the power of human thinking and decision-making, was employed to address damage detection. The programming was done in MATLAB. Validation and verification were carried out using a 10, 25, 200, 272, and 582 bar truss. A comparison between the GRO, MCSS, PSO and TLBO is conducted to show the efficiency of the GRO in finding the global optimum. The results show that utilizing the proposed function and the GRO optimization technique to discover truss damaged structure in the quickest time possible is both reliable and stable.