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.
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Kumar, S.P., Beenamol, M. Multiple layer radial basis neural network with remora regression tree optimum feature extraction for structural health monitoring. Asian J Civ Eng 24, 989–999 (2023). https://doi.org/10.1007/s42107-022-00547-4
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DOI: https://doi.org/10.1007/s42107-022-00547-4