Investigating plasticity and swelling properties of improved expansive soil designs based on hybrid deep learning optimization for high pay load applications

Abstract This research investigates the improvement of expansive soils by incorporating fly ash, focusing on its impact on soil engineering properties such as plasticity index (PI), swelling potential (SP), California bearing ratio (CBR), maximum dry density (MDD), and unconfined compressive strength (UCS). The different mix proportions comprised of CM (control mix), FSM 1, FSM 2, FSM 3, FSM 4, and FSM 5 (where FSM is fly ash stabilized mix), were subjected to tests after a curing period of 7, 14, and 28 days. Using the experimental result, FSM 4 was determined to be the most effective mix regarding UCS, PI, SP, CBR, and MDD. The response surface methodology (RSM) was used to model the five properties of the most effective mix, producing R 2 values of 0.9331 for UCS, 0.9189 for CBR, 0.9368 for PI, 0.9286 for SP, and 0.9421 for MDD in order to increase prediction accuracy. The hybrid quantum neural network–Krylov subspace optimization model (QNN‐KSO) was introduced and proved to be the best out of the RSM, deep neural network–grey wolf optimization, and random forest–artificial bee colony out of all six criteria, as it resulted in more reliable and accurate prediction of UCS, CBR, PI, SP, and MDD. The hybrid QNN‐KSO model produced excellent performance while minimizing the root mean squared error while achieving an R 2 value of 0.99 making this an improved modeling technique for soil stabilization.