| 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 |
20230220104712.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
230220b 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 |
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| 9 (RLIN) |
20065 |
| Author |
Divesh Ranjan Kumar |
| 245 ## - TITLE STATEMENT |
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| Title |
Prediction of probability of liquefaction using soft computing techniques |
| 250 ## - EDITION STATEMENT |
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| Volume, Issue number |
Vol.103(4), Dec |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Place of publication, distribution, etc. |
USA |
| Name of publisher, distributor, etc. |
Springer |
| Year |
2022 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Pagination |
1195-1208p. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Prediction of liquefaction potential of any soil deposit is itself a very challenging task. The problem becomes even more demanding when it becomes necessary to incorporate the variability of all related parameters. Because the parameters that impact liquefaction potential are inherently unknown, the problem is probabilistic rather than deterministic. In the literature, probabilistic analysis of liquefaction potential has attracted a lot of attention, and it's been shown to be a useful technique for evaluating uncertainty inherent in the problem. Machine Learning (ML) techniques have found their applications in all fields of science and engineering while dealing with problems of stochastic nature. These techniques are capable of finding out the desired outputs very effectively. In this paper, five different ML models namely, extreme gradient boosting (XGBoost), random forest (RF), gradient boosting machines (GBM), support vector regression (SVR), and group method of data handling (GMDH) have been used for evaluation of probability of liquefaction based on standard penetration test data. In this study, analysis has been carried out with six input variable such as, depth of penetration, corrected standard penetration blow number, total vertical stress, fine content, maximum horizontal acceleration, total effective stress, and earthquake magnitude. To examine the capabilities of the suggested models in predicting the probability of liquefaction, several statistical parameters have been examined. To compare the accuracy of the proposed models, Taylor graph, REC curve, and error matrix have been developed. While all of the proposed models could efficiently predict the probability of liquefaction. XGBoost model has been found to give the best prediction among all five models. In summary, XGBoost model attained (R2=0.978 for training and R2=0.799 for testing), GBM model attained (R2=.953 for training and R2=0.780 for testing), RF model attained (R2=.930 for training and R2=0.769 for testing), SVR model attained (R2=.702. for training and R2=0.778 for testing), GMDH model attained (R2=0.650 for training and R2=0.701 for testing). The proposed models can also be utilized as a valid model for forecasting the probability of liquefaction efficiently for complicated real-world earthquake engineering problems. |
| 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 |
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| 9 (RLIN) |
12469 |
| Co-Author |
Samui, Pijush |
| 773 0# - HOST ITEM ENTRY |
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| International Standard Serial Number |
2250-2149 |
| Place, publisher, and date of publication |
Switzerland Springer |
| Title |
Journal of the institution of engineers (India): Series A |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
|---|
| URL |
https://link.springer.com/article/10.1007/s40030-022-00683-9 |
| Link text |
Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
|
| Koha item type |
Articles Abstract Database |
