Prediction model for corrosion rate of low-alloy steels under atmospheric conditions using machine learning algorithms

Kuang, Jingou; Long, Zhilin

doi:10.1007/s12613-023-2679-5

Cited by 6 publications

(1 citation statement)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When the SHO is iteratively calculated, the control factor h adopts a linearly decreasing adjustment strategy, but in fact, the change in h is nonlinear; in order to better reflect the algorithm's optimization process, this paper introduces a nonlinear adjustment strategy as follows [24][25][26]:…”

Section: Nonlinear Adjustment Of Convergence Factorsmentioning

confidence: 99%

Offshore Wind Power Foundation Corrosion Rate Prediction Model Based on Improved SHO Algorithm

Zhang,

Zou

et al. 2024

Processes

View full text Add to dashboard Cite

To improve the accuracy of offshore wind power foundation corrosion rate prediction and grasp the operation status of equipment in time, an offshore wind power foundation corrosion rate prediction model based on an improved spotted hyena optimization (SHO) algorithm is proposed in this paper. Firstly, in order to reduce the modeling workload of the offshore wind power foundation corrosion prediction model, kernel principal component analysis (KPCA) is used to extract the principal elements of the offshore wind power foundation corrosion rate. Secondly, for the problems in the SHO algorithm, it is easy to fall into local optimums, and the solution accuracy is not high; the SHO algorithm is improved by the convergence factor and Levy flight strategy, which gives the SHO algorithm stronger global search ability and convergence speed. Finally, based on the improved SHO algorithm, an offshore wind power base corrosion rate prediction model is established by optimizing the penalty parameter and kernel function parameter. Simulation results show that the average relative error, root mean square error, and global maximum relative error assimilation coefficient of the combined prediction model in this paper are 2.86%, 0.15, 3.74%, and 0.995, respectively, which are better than other corrosion prediction models.

show abstract

Section: Nonlinear Adjustment Of Convergence Factorsmentioning

confidence: 99%