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

Authors: Xiaodong Li

Addresses: Xinjiang Applied Vocational Technology College, Kuitun, 833200, China

Abstract: 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.

Keywords: firefly algorithm; derrick steel structure; ultimate bearing capacity; prediction; dynamic response parameters; load parameters; RBF neural network.

DOI: 10.1504/IJMIC.2024.144035

International Journal of Modelling, Identification and Control, 2024 Vol.45 No.4, pp.252 - 261

Received: 29 Apr 2024
Accepted: 22 Aug 2024
Published online: 21 Jan 2025 *

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