This research aims at developing a universal methodology for automated calibration of microscopic properties of modelled granular materials. The proposed calibrator can be applied for different experimental setups. Two optimization approaches: (1) a genetic algorithm and (2) DIRECT optimization, are used to identify discrete element method input model parameters, e.g., coefficients of sliding and rolling friction. The algorithms are used to minimize the objective function characterized by the discrepancy between the experimental macroscopic properties and the associated numerical results. Two test cases highlight the robustness, stability, and reliability of the two algorithms used for automated discrete element method calibration with different setups .

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Design optimization of reinforced concrete plane frames using genetic algorithm-based methodology is presented in this paper. Most of the approaches reported in the literature consider the design variables to be continuous, and the optimal solution obtained requires further modification to make it constructible. Since the area of reinforcement after detailing is different from that obtained theoretically, use of area of reinforcement as a continuous variable cannot provide rational solutions. Aspects such as detailing and placing of reinforcement in beams and columns and other issues related to construction are brought into the optimal design model presented in this paper. Genetic algorithm-based methodologies provide ideal techniques for handling such issues and generate rational optimal solutions. Examples of reinforced concrete plane frames are solved, and results are presented. Emphasis is placed on genetic modeling aspects, which provide mechanisms for considering realistically the practical issues, resulting in a rigorous optimal design model providing rational solutions.

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