A novel machine-learning based on the global search techniques using vectorized data for damage detection in structures

DOI10.1016/J.IJENGSCI.2020.103376OpenAlexW3083248010MaRDI QIDQ2212992

Yanyan Li

Publication date: 27 November 2020

Published in: International Journal of Engineering Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ijengsci.2020.103376

zbMATH Keywords

local minimadamage detectionstructural health monitoring (SHM)machine learning (ML)Cuckoo search (CS)vectorized data

Mathematics Subject Classification ID

Computer science (68-XX) Operations research, mathematical programming (90-XX)

Related Items (3)

An enhanced data-driven constitutive model for predicting strain-rate and temperature dependent mechanical response of elastoplastic materials ⋮ Machine learning aided phase field method for fracture mechanics ⋮ A robust unsupervised neural network framework for geometrically nonlinear analysis of inelastic truss structures

Uses Software

• Unnamed Item

Cites Work

• Prediction of load-displacement curve in a complex structure using artificial neural networks: a study on a long bone
• A novel approach to surface defect detection
• Support-vector networks
• Modeling of the viscoelastic properties of thermoset vinyl ester nanocomposite using artificial neural network
• Nearest neighbor pattern classification

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