持有資料: Long short term memory networks for predicting resilient Modulus of stabilized base material subject to wet-dry cycles

Long short term memory networks for predicting resilient Modulus of stabilized base material subject to wet-dry cycles

The resilient modulus (MR) of different pavement materials is one of the most important input parameters for the mechanistic-empirical pavement design approach. The dynamic triaxial test is the most often used method for evaluating the MR, although it is expensive, time-consuming, and requires speci...

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Main Authors:	Al?Zubi M.A., Ahmad M., Abdullah S., Khan B.J., Qamar W., Abdullah G.M.S., Gonz�lez-Lezcano R.A., Paul S., EL-Gawaad N.S.A., Ouahbi T., Kashif M.
其他作者:	59367651300
格式:	Article
出版:	Nature Research 2025
主題:	aluminum oxide calcium oxide ferric oxide silicon dioxide article computer interface least square analysis long short term memory network moisture sensitivity analysis Shapley additive explanation support vector machine
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Long short term memory networks for predicting resilient Modulus of stabilized base material subject to wet-dry cycles

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