Structural health monitoring with self-organizing maps and artificial neural networks
Abstract
The use of self-organizing maps and artificial neural networks for structural health monitoring is presented in this paper. The authors recently developed a nonparametric structural damage detection algorithm for extracting damage indices from the ambient vibration response of a structure. The algorithm is based on self-organizing maps with a multilayer feedforward pattern recognition neural network. After the training of the self-organizing maps, the algorithm was tested analytically under various damage scenarios based on stiffness reduction of beam members and boundary condition changes of a grid structure. The results indicated that proposed algorithm can successfully locate and quantify damage on the structure.
Collections
- Civil and Environmental Engineering [863 items ]
- Electrical Engineering [2822 items ]
Related items
Showing items related by title, author, creator and subject.
-
Structural Damage Detection in Civil Engineering with Machine Learning: Current State of the Art
( Springer , 2022 , Conference)This paper presents a brief overview of vibration-based structural damage detection studies that are based on machine learning (ML) in civil engineering structures. The review includes both parametric and nonparametric ... -
A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications
( Academic Press , 2021 , Article Review)Monitoring structural damage is extremely important for sustaining and preserving the service life of civil structures. While successful monitoring provides resolute and staunch information on the health, serviceability, ... -
Structural damage detection in real time: Implementation of 1D convolutional neural networks for SHM applications
( Springer , 2017 , Conference)Most of the classical structural damage detection systems involve two processes, feature extraction and feature classification. Usually, the feature extraction process requires large computational effort which prevent the ...