research-article

Online Tensor-Based Learning Model for Structural Damage Detection

Authors:

Basem Suleiman,

Seid Miad ZandaviAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 15, Issue 6

Article No.: 107, Pages 1 - 18

https://doi.org/10.1145/3451217

Published: 19 May 2021 Publication History

Abstract

The online analysis of multi-way data stored in a tensor

has become an essential tool for capturing the underlying structures and extracting the sensitive features that can be used to learn a predictive model. However, data distributions often evolve with time and a current predictive model may not be sufficiently representative in the future. Therefore, incrementally updating the tensor-based features and model coefficients are required in such situations. A new efficient tensor-based feature extraction, named Nesterov Stochastic Gradient Descent (NeSGD), is proposed for online

(CP) decomposition. According to the new features obtained from the resultant matrices of NeSGD, a new criterion is triggered for the updated process of the online predictive model. Experimental evaluation in the field of structural health monitoring using laboratory-based and real-life structural datasets shows that our methods provide more accurate results compared with existing online tensor analysis and model learning. The results showed that the proposed methods significantly improved the classification error rates, were able to assimilate the changes in the positive data distribution over time, and maintained a high predictive accuracy in all case studies.

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Cited By

Suleiman BAnaissi AYan WBello AZou STong L(2024)A Federated Learning Anomaly Detection Approach for IoT EnvironmentsProceedings of the Second International Conference on Advances in Computing Research (ACR’24)10.1007/978-3-031-56950-0_18(206-218)Online publication date: 29-Mar-2024
https://doi.org/10.1007/978-3-031-56950-0_18
Anaissi AMandiola MAmin SAlyassine W(2024)Understanding Consumers Attitudes Towards SustainabilityProceedings of the Second International Conference on Advances in Computing Research (ACR’24)10.1007/978-3-031-56950-0_12(137-150)Online publication date: 29-Mar-2024
https://doi.org/10.1007/978-3-031-56950-0_12
Jia YAnaissi ASuleiman B(2023)ResNLS: An improved model for stock price forecastingComputational Intelligence10.1111/coin.1260840:1Online publication date: 12-Nov-2023
https://doi.org/10.1111/coin.12608
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Index Terms

Online Tensor-Based Learning Model for Structural Damage Detection
1. Computing methodologies
  1. Machine learning
    1. Learning settings
      1. Online learning settings

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 15, Issue 6

June 2021

474 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3465438

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

Issue’s Table of Contents

Copyright © 2021 Association for Computing Machinery.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 May 2021

Accepted: 01 February 2021

Revised: 01 February 2021

Received: 01 August 2020

Published in TKDD Volume 15, Issue 6

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Cited By

Suleiman BAnaissi AYan WBello AZou STong L(2024)A Federated Learning Anomaly Detection Approach for IoT EnvironmentsProceedings of the Second International Conference on Advances in Computing Research (ACR’24)10.1007/978-3-031-56950-0_18(206-218)Online publication date: 29-Mar-2024
https://doi.org/10.1007/978-3-031-56950-0_18
Anaissi AMandiola MAmin SAlyassine W(2024)Understanding Consumers Attitudes Towards SustainabilityProceedings of the Second International Conference on Advances in Computing Research (ACR’24)10.1007/978-3-031-56950-0_12(137-150)Online publication date: 29-Mar-2024
https://doi.org/10.1007/978-3-031-56950-0_12
Jia YAnaissi ASuleiman B(2023)ResNLS: An improved model for stock price forecastingComputational Intelligence10.1111/coin.1260840:1Online publication date: 12-Nov-2023
https://doi.org/10.1111/coin.12608
Salehi HGorodetsky ASolhmirzaei RJiao P(2023)High-dimensional data analytics in civil engineeringEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106659125:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.106659

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