Open AccessWeight optimization of steel lattice transmission towers based on Differential Evolution and machine learning classification technique
Author(s) -
Tran-Hieu Nguyen,
Anh-Tuan Vu
Publication year - 2021
Publication title -
frattura ed integrità strutturale
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.368
H-Index - 19
ISSN - 1971-8993
DOI - 10.3221/igf-esis.59.13
Subject(s) - boosting (machine learning) , lattice (music) , transmission tower , differential evolution , artificial intelligence , electric power transmission , power transmission , algorithm , computer science , mathematical optimization , machine learning , engineering , power (physics) , structural engineering , mathematics , tower , electrical engineering , physics , quantum mechanics , acoustics
Transmission towers are tall structures used to support overhead power lines. They play an important role in the electrical grids. There are several types of transmission towers in which lattice towers are the most common type. Designing steel lattice transmission towers is a challenging task for structural engineers due to a large number of members. Therefore, discovering effective ways to design lattice towers has attracted the interest of researchers. This paper presents a method that integrates Differential Evolution (DE), a powerful optimization algorithm, and a machine learning classification model to minimize the weight of steel lattice towers. A classification model based on the Adaptive Boosting algorithm is developed in order to eliminate unpromising candidates during the optimization process. A feature handling technique is also introduced to improve the model quality. An illustrated example of a 160-bar tower is conducted to demonstrate the efficiency of the proposed method. The results show that the application of the Adaptive Boosting model saves about 38% of the structural analyses. As a result, the proposed method is 1.5 times faster than the original DE algorithm. In comparison with other algorithms, the proposed method obtains the same optimal weight with the least number of structural analyses.