Image processing and deep learning approach to evaluate earthquake resistance of urban buildings
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.
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| Γλώσσα: | English |
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BRAC University
2018
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| Διαθέσιμο Online: | http://hdl.handle.net/10361/11025 |
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10361-11025 |
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10361-110252022-01-26T10:08:22Z Image processing and deep learning approach to evaluate earthquake resistance of urban buildings Ahmed, Yasin Alam, Tawsiful Shakil, Md. Hasibur Rahman Hossain, Md. Tanjidul Alam, Dr. Md. Ashraful Department of Computer Science and Engineering, BRAC University Earthquake Deep learning Image processing Machine learning Image processing--Digital techniques. Natural disasters. This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. Cataloged from PDF version of thesis. Includes bibliographical references (pages 42-44). The capital of Bangladesh, Dhaka, is one of the most densely populated cities in the world, sits atop the world’s largest river delta at close to sea level, which can trigger a massive earthquake resulting in death of millions of people. To minimizing such casualties, marking risky buildings can be an efficient approach as these buildings have more chance to collapse. In this paper, a new approach has been introduced on spotting these buildings by taking visual view through images and calculating the risk factors using Image Processing and Deep Learning. By following FEMA154 method of calculating risk factors of C3 type URM INF buildings, Image processing has been applied on the image data to get results and SVM has been run on the manual data of the risk factors. We used neural network model VGG16 and altered it to a newer version for beam detecting via images. The buildings have been shown in the map marking red or green to let people know about the vulnerability of these buildings. Yasin Ahmed Tawsiful Alam Md. Hasibur Rahman Shakil Md. Tanjidul Hossain B. Computer Science and Engineering 2018-12-18T09:49:22Z 2018-12-18T09:49:22Z 2018 2018 Thesis ID 13301118 ID 13301114 ID 13301016 ID 13101153 http://hdl.handle.net/10361/11025 en BRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 44 pages application/pdf BRAC University |
| institution |
Brac University |
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Institutional Repository |
| language |
English |
| topic |
Earthquake Deep learning Image processing Machine learning Image processing--Digital techniques. Natural disasters. |
| spellingShingle |
Earthquake Deep learning Image processing Machine learning Image processing--Digital techniques. Natural disasters. Ahmed, Yasin Alam, Tawsiful Shakil, Md. Hasibur Rahman Hossain, Md. Tanjidul Image processing and deep learning approach to evaluate earthquake resistance of urban buildings |
| description |
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. |
| author2 |
Alam, Dr. Md. Ashraful |
| author_facet |
Alam, Dr. Md. Ashraful Ahmed, Yasin Alam, Tawsiful Shakil, Md. Hasibur Rahman Hossain, Md. Tanjidul |
| format |
Thesis |
| author |
Ahmed, Yasin Alam, Tawsiful Shakil, Md. Hasibur Rahman Hossain, Md. Tanjidul |
| author_sort |
Ahmed, Yasin |
| title |
Image processing and deep learning approach to evaluate earthquake resistance of urban buildings |
| title_short |
Image processing and deep learning approach to evaluate earthquake resistance of urban buildings |
| title_full |
Image processing and deep learning approach to evaluate earthquake resistance of urban buildings |
| title_fullStr |
Image processing and deep learning approach to evaluate earthquake resistance of urban buildings |
| title_full_unstemmed |
Image processing and deep learning approach to evaluate earthquake resistance of urban buildings |
| title_sort |
image processing and deep learning approach to evaluate earthquake resistance of urban buildings |
| publisher |
BRAC University |
| publishDate |
2018 |
| url |
http://hdl.handle.net/10361/11025 |
| work_keys_str_mv |
AT ahmedyasin imageprocessinganddeeplearningapproachtoevaluateearthquakeresistanceofurbanbuildings AT alamtawsiful imageprocessinganddeeplearningapproachtoevaluateearthquakeresistanceofurbanbuildings AT shakilmdhasiburrahman imageprocessinganddeeplearningapproachtoevaluateearthquakeresistanceofurbanbuildings AT hossainmdtanjidul imageprocessinganddeeplearningapproachtoevaluateearthquakeresistanceofurbanbuildings |
| _version_ |
1814307409089265664 |