Predicting strain and stress fields in self-sensing nanocomposites using deep learned electrical tomography (Record no. 53397)
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| fixed length control field | 02005nam a2200265Ia 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | MX-MdCICY |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250625162427.0 |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | CICY |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) | |
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) | B-19259 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250602s9999 xx |||||s2 |||| ||und|d |
| 245 10 - TITLE STATEMENT | |
| Title | Predicting strain and stress fields in self-sensing nanocomposites using deep learned electrical tomography |
| 490 0# - SERIES STATEMENT | |
| Volume/sequential designation | Smart Materials and Structures, 31(4), p.045024, 2022 |
| 520 3# - SUMMARY, ETC. | |
| Summary, etc. | Conductive nanocomposites, enabled by their piezoresistivity, have emerged as a new instrument in structural health monitoring. To this end, studies have recently found that electrical resistance tomography (ERT), a non-destructive conductivity imaging technique, can be utilized with piezoresistive nanocomposites to detect and localize damage. Furthermore, by incorporating complementary optimization protocols, the mechanical state of the nanocomposites can also be determined. In many cases, however, such approaches may be associated with high computational cost. To address this, we develop deep learned frameworks using neural networks to directly predict strain and stress distributions-thereby bypassing the need to solve the ERT inverse problem or execute an optimization protocol to assess mechanical state. The feasibility of the learned frameworks is validated using simulated and experimental data considering a carbon nanofiber plate in tension. Results show that the learned frameworks are capable of directly and reliably predicting strain and stress distributions based on ERT voltage measurements. |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | DEEP LEARNING |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | ELECTRICAL RESISTANCE TOMOGRAPHY |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | NANOCOMPOSITES |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | PIEZORESISTIVITY |
| 700 12 - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Chen, L. |
| 700 12 - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Hassan, H. |
| 700 12 - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Tallman, T. N. |
| 700 12 - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Huang, S. S. |
| 700 12 - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Smyl, D. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://drive.google.com/file/d/1pVYa-SZxkvrd3E8m1QuiD6FGXRBw7NF0/view?usp=drivesdk">https://drive.google.com/file/d/1pVYa-SZxkvrd3E8m1QuiD6FGXRBw7NF0/view?usp=drivesdk</a> |
| Public note | Para ver el documento ingresa a Google con tu cuenta: @cicy.edu.mx |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Clasificación local |
| Koha item type | Documentos solicitados |
| Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection | Home library | Current library | Shelving location | Date acquired | Total checkouts | Full call number | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Clasificación local | Ref1 | CICY | CICY | Documento préstamo interbibliotecario | 25.06.2025 | B-19259 | 25.06.2025 | 25.06.2025 | Documentos solicitados |