Presented By: Nima Khodadadi, University of Miami
Description: A fundamental aspect of nature-based systems is the ability to optimize. Similarly, since ancient times humans have had a tenancy to naturally focus on optimizing their activities making them more feasible, economical, functional, and practical. Analogously in structural engineering design "structural optimization" is a simulation-driven design technique that identifies and explores high-potential designs, while also rejecting low- potential ones early in the design phase, aiming to solve problems of structural design. However, such optimization methods have not been widely used in the design of concrete structures. This is in part be due to the design and construction of concrete structures involves complex processes, and optimization techniques face serious challenges. Nevertheless, modern meta-heuristic methods of optimization, can provide higher-level procedures or heuristic designed to find, generate, tune, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem or a machine learning problem. In Machine Learning, historical data is used to teach and train the system developed, in order to be able to better predict future behavior. In meta-heuristic approaches, the need to have historical data is not necessary. Instead, the system generates random data and uses them to find an optimal solution that satisfies all the constraints. This iterative process continues until the algorithm reaches a defined criteria. Meta-heuristic algorithms are traditionally used in non-deterministic polynomial-time based problems, where for a given time and effort obtaining a “good” solution is preferred to an “optimum” one. To this end, meta-heuristic algorithms help select the optimal parameters for machine learning and deep learning techniques to train and improve the model's performance. Concrete structures optimization often aims to minimize costs, including those related to concrete material and reinforcement.