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Case Study—Spiking Neural Network Hardware System for Structural Health Monitoring

This case study provides feasibility analysis of adapting Spiking Neural Networks (SNN) based Structural Health Monitoring (SHM) system to explore low-cost solution for inspection of structural health of damaged buildings which survived after natural disaster that is, earthquakes or similar activiti...

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Detalles Bibliográficos
Autores principales: Pang, Lili, Liu, Junxiu, Harkin, Jim, Martin, George, McElholm, Malachy, Javed, Aqib, McDaid, Liam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7570929/
https://www.ncbi.nlm.nih.gov/pubmed/32911869
http://dx.doi.org/10.3390/s20185126
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author Pang, Lili
Liu, Junxiu
Harkin, Jim
Martin, George
McElholm, Malachy
Javed, Aqib
McDaid, Liam
author_facet Pang, Lili
Liu, Junxiu
Harkin, Jim
Martin, George
McElholm, Malachy
Javed, Aqib
McDaid, Liam
author_sort Pang, Lili
collection PubMed
description This case study provides feasibility analysis of adapting Spiking Neural Networks (SNN) based Structural Health Monitoring (SHM) system to explore low-cost solution for inspection of structural health of damaged buildings which survived after natural disaster that is, earthquakes or similar activities. Various techniques are used to detect the structural health status of a building for performance benchmarking, including different feature extraction methods and classification techniques (e.g., SNN, K-means and artificial neural network etc.). The SNN is utilized to process the sensory data generated from full-scale seven-story reinforced concrete building to verify the classification performances. Results show that the proposed SNN hardware has high classification accuracy, reliability, longevity and low hardware area overhead.
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spelling pubmed-75709292020-10-28 Case Study—Spiking Neural Network Hardware System for Structural Health Monitoring Pang, Lili Liu, Junxiu Harkin, Jim Martin, George McElholm, Malachy Javed, Aqib McDaid, Liam Sensors (Basel) Article This case study provides feasibility analysis of adapting Spiking Neural Networks (SNN) based Structural Health Monitoring (SHM) system to explore low-cost solution for inspection of structural health of damaged buildings which survived after natural disaster that is, earthquakes or similar activities. Various techniques are used to detect the structural health status of a building for performance benchmarking, including different feature extraction methods and classification techniques (e.g., SNN, K-means and artificial neural network etc.). The SNN is utilized to process the sensory data generated from full-scale seven-story reinforced concrete building to verify the classification performances. Results show that the proposed SNN hardware has high classification accuracy, reliability, longevity and low hardware area overhead. MDPI 2020-09-08 /pmc/articles/PMC7570929/ /pubmed/32911869 http://dx.doi.org/10.3390/s20185126 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Pang, Lili
Liu, Junxiu
Harkin, Jim
Martin, George
McElholm, Malachy
Javed, Aqib
McDaid, Liam
Case Study—Spiking Neural Network Hardware System for Structural Health Monitoring
title Case Study—Spiking Neural Network Hardware System for Structural Health Monitoring
title_full Case Study—Spiking Neural Network Hardware System for Structural Health Monitoring
title_fullStr Case Study—Spiking Neural Network Hardware System for Structural Health Monitoring
title_full_unstemmed Case Study—Spiking Neural Network Hardware System for Structural Health Monitoring
title_short Case Study—Spiking Neural Network Hardware System for Structural Health Monitoring
title_sort case study—spiking neural network hardware system for structural health monitoring
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7570929/
https://www.ncbi.nlm.nih.gov/pubmed/32911869
http://dx.doi.org/10.3390/s20185126
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