Application of a Machine Learning Algorithm for the Structural Optimization of Circular Arches with Different Cross-Sections

作     者：Jonathan Melchiorre Amedeo Manuello Bertetto Giuseppe Carlo Marano Jonathan Melchiorre;Amedeo Manuello Bertetto;Giuseppe Carlo Marano

作者机构：Department of Structural Building and Geotechnics Politecnico di Torino. Corso Duca degli Abruzzi Torino Italy 

出 版 物：《Journal of Applied Mathematics and Physics》 (应用数学与应用物理（英文）)

年 卷 期：2021年第9卷第5期

页      面：1159-1170页

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

主　　题：Structural Optimization Genetic Algorithm Boundary Value Problem Arch Volume Minimization 

摘      要： Arches are employed for bridges. This particular type of structures, characterized by a very old use tradition, is nowadays, widely exploited because of its strength, resilience, cost-effectiveness and charm. In recent years, a more conscious design approach that focuses on a more proper use of the building materials combined with the increasing of the computational capability of the modern computers, has led the research in the civil engineering field to the study of optimization algorithms applications aimed at the definition of the best design parameters. In this paper, a differential formulation and a MATLAB code for the calculation of the internal stresses in the arch structure are proposed. Then, the application of a machine learning algorithm, the genetic algorithm, for the calculation of the geometrical parameters, that allows to minimize the quantity of material that constitute the arch structures, is implemented. In this phase, the method used to calculate the stresses has been considered as a constraint function to reduce the range of the solutions to the only ones able to bear the design loads with the smallest volume. In particular, some case studies with different cross-sections are reported to prove the validity of the method and to compare the obtained results in terms of optimization effectiveness.