Abstract
Typhoon Morakot has been the most severe typhoon disaster to strike Taiwan in recent decades causing tremendous damage to bridge surroundings in 2009. However, we still lack a means of assessing post-typhoon damage for follow-up rebuilding. This paper presents an integrated model that automatically measures changes in rivers, areas of damage to bridge surroundings, and changes in vegetation. The proposed model is based on a neurofuzzy mechanism enhanced by the self-organising map optimisation algorithm and also includes the particular functions of dilation, erosion, and skeletonisation to deal with river imagery. High resolution FORMOSAT-2 satellite imagery from before and after the invasion period is adopted. A bridge is randomly selected from the 129 destroyed due to the typhoon for applications of the model. The recognition results show that the river average width has increased 66% with a maximum increase of over 200%. The ruined segment of the bridge is located exactly in the most scoured region. There has also been a nearly 10% reduction in the vegetation coverage. The results yielded by the proposed model demonstrate a pinpoint accuracy rate of 99.94%. This study successfully develops a tool for large-scale damage assessment as well as for precise measurement after disasters.
| Original language | English |
|---|---|
| Pages (from-to) | 24-35 |
| Number of pages | 12 |
| Journal | Civil Engineering and Environmental Systems |
| Volume | 31 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 2014 |
Keywords
- ANN
- bridge
- damage assessment
- fuzzy
- optimisation
- remote sensing imagery
- SOM
- typhoon disaster
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