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

• 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.
• Other Authors: 59367651300
• Format: Article
• Published: Nature Research 2025
• Subjects: 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

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...