Prediction method of ultimate bearing capacity of derrick steel structures based on firefly algorithm

被引：0
作者：
Li, Xiaodong [1 ]
机构：
[1] Xinjiang Applied Vocational Technology College, Kuytun
来源：
International Journal of Modelling, Identification and Control | 2024年 / 45卷 / 04期
关键词：
derrick steel structure; dynamic response parameters; firefly algorithm; load parameters; prediction; RBF neural network; ultimate bearing capacity;
D O I：
10.1504/IJMIC.2024.144035
中图分类号：
学科分类号：
摘要：
In order to overcome the problems of high relative error rate of load detection, low prediction accuracy and long time consumption in traditional prediction methods, a prediction method of ultimate bearing capacity of derrick steel structures based on firefly algorithm is proposed. The vibration system equation of derrick steel structure is constructed and simplified, so as to identify the dynamic response parameters. The load parameters of derrick steel structure are detected by combining the results of vibration differential equation. According to the load parameter detection results, the ultimate bearing capacity prediction model based on RBF neural network optimised by firefly algorithm is established, and the ultimate bearing capacity prediction results are obtained. The experimental results show that the relative error rate of load detection of this method varies in the range of 2.5%~4.8%, the prediction accuracy is always above 92.6%, the time consumption varies from 0.47 s to 0.84 s. Copyright © 2024 Inderscience Enterprises Ltd.
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页码：252 / 261
页数：9
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