AF-OS-ELM-MVE: A new online sequential extreme learning machine of dam safety monitoring model for structure deformation estimation

Dam deformation monitoring is an important task in hydraulic engineering projects. Traditional statistical methods and machine learning algorithms have been widely applied in structural safety prediction due to the application’s convenience. However, most machine learning algorithms are offline mode...

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Bibliographic Details
Published in:Advanced engineering informatics Vol. 60; p. 102345
Main Authors: Zhang, Ye, Zhang, Wenwei, Li, Yanlong, Wen, Lifeng, Sun, Xinjian
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.04.2024
Subjects:
AF-OS-ELM
Dam deformation prediction
Hydraulic engineering
MVE
Online learning
Uncertainty quantization
Uncertainty quantization
Dam deformation prediction
AF-OS-ELM
Online learning
MVE
Hydraulic engineering
ISSN:1474-0346, 1873-5320
Online Access:Get full text
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Summary:Dam deformation monitoring is an important task in hydraulic engineering projects. Traditional statistical methods and machine learning algorithms have been widely applied in structural safety prediction due to the application’s convenience. However, most machine learning algorithms are offline models and are not suitable for simulating the complex dynamic process of dam displacement. In addition, dam safety monitoring should quantify the uncertainty of the time-dependent displacement and establish reliable prediction intervals. As a result, an online learning model that can qualify prediction intervals is necessary. In this paper, an online learning algorithm named AF-OS-ELM-MVE is proposed to solve the problems. It is based on the OS-ELM algorithm and optimized using the adaptive forgetting factor mechanism. Through selective forgetting for parameter tuning, it achieves self-adaptation to the underlying physical mechanism between the factors and the displacement data. Mean Variance Estimation (MVE) is also adapted to establish the new hybrid model. It can be used to estimate the variance of the prediction process, and the model can generate prediction intervals based on the minimum probability theory. The AF-OS-ELM-MVE model is verified using long-term monitoring data and the performance comparisons were made with a number of state-of-the-art models. The correlation coefficients of the proposed model in point prediction are above 0.99 for measuring points of different elevations, and at a 95% confidence level, it achieves 100% coverage of real displacements. The results demonstrate the effectiveness of the proposed model in predicting dam deformations and provide a more solid basis for dam safety analysis.
ISSN:1474-0346
1873-5320
DOI:10.1016/j.aei.2023.102345