ABSTRACTThe Resilient Modulus (Mr) is perhaps the most relevant and widely used parameter to characterise the soil behaviour under repetitive loading for pavement applications. Accordingly, it is a crucial parameter controlling the mechanistic-empirical pavement design. Nonetheless, determining the Mr by laboratory tests is not always possible due to the high consumption of time and financial resources. Thus, developing new indirect approaches for estimating the MR is necessary. Precisely, this article investigates the application of Deep Neural Networks (DNNs) and statistical methods to predict the Mr of soils. For that purpose, the Long-Term Pavement Performance (LTPP) database was implemented. It includes 64 701 datasets resulting from coarse-grained and fine-grained soil samples considering a wide range of grain size distribution and subjected to different stress levels. The input parameters were the bulk stress, octahedral shear stress, and the percentage of soil particles passing through the different sieves (3", 2", 3/2", 1", 3/4", 1/2", 3/8", No. 4, No. 10, No. 40, No. 80, and No. 200) and the output was the Mr. The results suggest that while conventional mathematical models are unable to predict the influence of the grain size distribution and stress level on the Mr, the proposed DNNs were able to reproduce very accurate predictions. Notably, the proposed computational models have been uploaded to a GitHub repository and have become a valuable tool for forecasting the Mr when experimental measurements are not feasible.KEYWORDS: Deep neural networksresilient modulusstatistical methodsUS soils Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe authors publicly share the results of this investigation; that data can be accessed in the GitHub repository through the following link: https://github.com/rpoloe/MR_DNN.Additional informationFundingThe authors appreciate the financial support given by the Czech Science Foundation grant No. 21-35764J. The first and third authors acknowledge the institutional support by the Center for Geosphere Dynamics (UNCE/SCI/006).