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Multiple layer radial basis neural network with remora regression tree optimum feature extraction for structural health monitoring

Multiple layer radial basis neural network with remora regression tree optimum feature extraction... Structural Health Monitoring's (SHM) damage detection system gives us a wealth of information about the condition of the investigated property at the time of the inspection. To make the prediction automatically, a novel Multiple Layer Radial Basis Neural Network with Remora Regression Tree Optimum Feature Extraction based on Mean Estimating Null Filler is proposed. Pre-processing is the first step in this framework, which is then followed by feature extraction and prediction. The pre-processing framework makes use of a novel mean estimating null filler and mean normalization. Additionally, a novel remora regression tree optimal feature extraction is utilized. To create an algorithm for identifying the right response variable, this technique divides the tree based on specific features. Finally, the extracted features are loaded into the proposed Multiple Layer Radial basis neural network, which accurately predicts structural damage by combining an RBN unit and MLP. As a result, the proposed system performs better than the existing approaches. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Asian Journal of Civil Engineering Springer Journals

Multiple layer radial basis neural network with remora regression tree optimum feature extraction for structural health monitoring

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Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
1563-0854
eISSN
2522-011X
DOI
10.1007/s42107-022-00547-4
Publisher site
See Article on Publisher Site

Abstract

Structural Health Monitoring's (SHM) damage detection system gives us a wealth of information about the condition of the investigated property at the time of the inspection. To make the prediction automatically, a novel Multiple Layer Radial Basis Neural Network with Remora Regression Tree Optimum Feature Extraction based on Mean Estimating Null Filler is proposed. Pre-processing is the first step in this framework, which is then followed by feature extraction and prediction. The pre-processing framework makes use of a novel mean estimating null filler and mean normalization. Additionally, a novel remora regression tree optimal feature extraction is utilized. To create an algorithm for identifying the right response variable, this technique divides the tree based on specific features. Finally, the extracted features are loaded into the proposed Multiple Layer Radial basis neural network, which accurately predicts structural damage by combining an RBN unit and MLP. As a result, the proposed system performs better than the existing approaches.

Journal

Asian Journal of Civil Engineering – Springer Journals

Published: Jun 1, 2023

Keywords: Structural health monitoring; Damage detection; Deep learning; Multiple layer perceptron; Radial basis neural network

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