TY - JOUR
T1 - Case Study - Spiking Neural Network Hardware System for Structural Health Monitoring
AU - Pang, Lili
AU - Liu, Junxiu
AU - Harkin, Jim
AU - Martin, George
AU - McElholm, Malachy
AU - Javed, Aqib
AU - McDaid, LJ
PY - 2020/9/8
Y1 - 2020/9/8
N2 - 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.
AB - 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.
KW - Artificial neural networks
KW - Damage state classification
KW - Feature extraction
KW - Spiking neural networks
KW - Structural health monitoring
UR - http://www.scopus.com/inward/record.url?scp=85090429240&partnerID=8YFLogxK
UR - https://pure.ulster.ac.uk/en/publications/case-study-spiking-neural-network-hardware-system-for-structural-
U2 - 10.3390/s20185126
DO - 10.3390/s20185126
M3 - Article
C2 - 32911869
SN - 1424-8220
VL - 20
SP - 1
EP - 14
JO - Sensors
JF - Sensors
IS - 18
M1 - 5126
ER -