Research on intelligent differentiable optimization method for engineering structures with multivariables
Parameter optimization problems are extremely common in all stages of engineering structure design,construction and maintenance.Constrained by the one-way analysis information flow of finite element method,the traditional optimization methods mainly use heuristic algorithms with low optimization efficiency and number of optimizable variables,which are unable to meet the demands of increasingly complex engineering applications.An efficient,differentiable structural optimization method based on deep learning was proposed,which incorporates high-fidelity graphical representations of structures,and utilizes the differentiable characteristics of intelligent agent models to achieve high efficiency of structures with multi-objective optimization of multidimensional variables.A case for the parameter optimization of irregular multi-layer concrete frame structure demonstrates that the method can optimize thousands of variables within minutes and achieve satisfactory outcomes,reducing material utilization amount by 13％compared to manual designs;compared to the weeks of optimization time required by traditional optimization method,the efficiency of the is improved by a factor of 10 000.The differentiable structural optimization method can be embedded with arbitrary engineering objective functions and deep learning-based structural intelligent computational models,and can be extended to be applied to a variety of tasks such as parameter inversion,with a wide range of adaptability to engineering scenarios.
structural optimizationdifferentiable deep learningintelligent designengineering structureinverse problem
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