Fully Automated Design of Super-High-Rise Building Structures by a Hybrid AI Model on a Massively Parallel Machine

Design automation of one-of-a-kind engineering systems is considered a particularly challenging problem (Adeli 1994). Adeli and his associates have been working on creating novel design theories and computational models with two broad objectives: (1) automation and (2) optimization (Adeli and Hung 1995; Adeli and Kamal 1993; Adeli and Zhang 1993; Adeli and Yeh 1989; Adeli and Balasubramanyam 1988a, 1998b; Paek and Adeli 1988; Adeli and Alrijleh 1987). Civil-engineering structures are typically one of a kind as opposed to manufacturing designs that are often mass produced. To create computational models for structural design automation, we have been exploring new computing paradigms. Two such paradigms are neurocomputing and parallel processing. We first created a neural dynamics model for optimal design of structures by integrating penalty function method, Lyapunov stability theorem, Kuhn-Tucker conditions, and the neural dynamics concept (Adeli and Park 1995a). Neural dynamics is defined by a system of first-order differential equations governing time-evolution changes in node (neuron) activations. A pseudoobjective function in the form of a Lyapunov energy functional is defined using the exterior penalty function method. The Lyapunov stability theorem guarantees that solutions of the corresponding dynamic system (trajectories) for arbitrarily given starting points approach an equilibrium point without increasing the value of the objective function. In other words, the new neural dynamics model for structural optimization problems guarantees global convergence and robustness. However, the model does not guarantee that the equilibrium point is a local minimum. We use the Kuhn-Tucker condition to verify that the equilibrium point satisfies the necessary conditions for a local minimum. In other words, a learning rule is developed by integrating the Kuhn-Tucker necessary condition for a local minimum with the formulated Lyapunov function. The neural dynamics model was first applied to a linear optimization problem, the Articles