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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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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. |
format | Online Article Text |
id | pubmed-7570929 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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