Prediction of soil–water characteristic curves of fine-grained soils aided by artificial intelligent models
By: Li, Yao
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Contributor(s): Vanapalli, Sai K.
Publisher: USA Springer 2022Edition: Vol.52(5), Oct.Description: 1116-1128p.Subject(s): Civil EngineeringOnline resources: Click here
In:
Indian geotechnical journalSummary: The advantages associated with the artificial intelligence technology can be exploited to reliably and reasonably predict the soil–water characteristic curves (SWCC) of fine-grained soils alleviating conventionally used cumbersome and time-consuming experimental procedures. In this paper, multivariate adaptive regression splines (MARS) are used as a tool along with the aid of phyisco-empirical model for predicting SWCCs of fine-grained soils. The key input variables for the proposed MARS model are derived from the grain-size distribution curve. The significance of key input variables in the model analyzed using two different sensitivity analyses investigations suggests that the SWCC behavior of fine-grained soils is strongly influenced by the clay content. Therefore, a relationship between the upper and the lower bound residual suction and clay content values has been developed and used in the MARS model. Based on all the derived information, a MARS-aided design method has been developed combining with widely used physico-empirical model and SWCC fitting equation, for rapid yet reliable technique for predicting SWCCs of fine-grained soils.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Articles Abstract Database
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School of Engineering & Technology (PG) Archieval Section | Not for loan | 2022-2415 |
The advantages associated with the artificial intelligence technology can be exploited to reliably and reasonably predict the soil–water characteristic curves (SWCC) of fine-grained soils alleviating conventionally used cumbersome and time-consuming experimental procedures. In this paper, multivariate adaptive regression splines (MARS) are used as a tool along with the aid of phyisco-empirical model for predicting SWCCs of fine-grained soils. The key input variables for the proposed MARS model are derived from the grain-size distribution curve. The significance of key input variables in the model analyzed using two different sensitivity analyses investigations suggests that the SWCC behavior of fine-grained soils is strongly influenced by the clay content. Therefore, a relationship between the upper and the lower bound residual suction and clay content values has been developed and used in the MARS model. Based on all the derived information, a MARS-aided design method has been developed combining with widely used physico-empirical model and SWCC fitting equation, for rapid yet reliable technique for predicting SWCCs of fine-grained soils.
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