Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3390/s20185126 | SENSORS |
Keywords | DocType | Volume |
structural health monitoring,damage state classification,spiking neural networks,feature extraction,artificial neural networks | Journal | 20 |
Issue | ISSN | Citations |
18 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lili Pang | 1 | 0 | 0.34 |
| Junxiu Liu | 2 | 125 | 23.91 |
| Jim Harkin | 3 | 325 | 36.82 |
| George Martin | 4 | 0 | 0.34 |
| Malachy McElholm | 5 | 4 | 1.10 |
| Aqib Javed | 6 | 0 | 1.35 |
| Liam Mcdaid | 7 | 270 | 30.48 |