| 000 -LEADER |
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| fixed length control field |
a |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20231222094303.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
231222b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
AIKTC-KRRC |
| Transcribing agency |
AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| 9 (RLIN) |
22553 |
| Author |
Kapadia, Harsh |
| 245 ## - TITLE STATEMENT |
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| Title |
Implementation of computer vision technique for crack monitoring in concrete structure |
| 250 ## - EDITION STATEMENT |
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| Volume, Issue number |
Vol.104(1), Mar |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Place of publication, distribution, etc. |
USA |
| Name of publisher, distributor, etc. |
Springer |
| Year |
2023 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Pagination |
111-123p. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
Assessment of structural health is essential for safe and efficient functioning of built environment. Physical inspection of structures for its health monitoring is time-consuming, costly and risky. Advances in image acquisition, processing techniques, and computational resources have made computer vision a cost effective and an accurate technique for structural health assessment. Recent evolution of Convolutional Neural Network has reduced human effort and made it easy to develop algorithms for identification of structural defects. One of the primary defects in concrete is crack. Concrete cracking occurs due to many reasons like shrinkage, heaving, premature drying, excessive loading etc. and it leads to reduction in strength of structures. This paper presents a computer vision system developed for crack monitoring of concrete cubes subjected to compressive loading. Camera is used to capture real-time images when concrete cubes are subjected to loading. Images are processed using the convolutional neural network to identify crack and subsequently features of cracks like number, location, length, and area are extracted. The outcome of present system demonstrated better and accurate real-time monitoring of cracking when concrete is subjected to loading. The proposed computer vision-based approach is a step forward in Structural Health Monitoring of real-life concrete structures like buildings, bridges, and pavements. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| 9 (RLIN) |
4642 |
| Topical term or geographic name entry element |
Humanities and Applied Sciences |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| 9 (RLIN) |
22554 |
| Co-Author |
Patel, Paresh V. |
| 773 0# - HOST ITEM ENTRY |
|---|
| International Standard Serial Number |
2250-2149 |
| Title |
Journal of the institution of engineers (India): Series A |
| Place, publisher, and date of publication |
Switzerland Springer |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
|---|
| URL |
https://link.springer.com/article/10.1007/s40030-022-00695-5 |
| Link text |
Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Koha item type |
Articles Abstract Database |
