@article{polo-mendoza_duque_masin_turbay_acosta_2023, title={Implementation of deep neural networks and statistical methods to predict the resilient modulus of soils}, volume={24}, ISSN={["1477-268X"]}, DOI={10.1080/10298436.2023.2257852}, abstractNote={ABSTRACT The 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.}, number={1}, journal={INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING}, author={Polo-Mendoza, Rodrigo and Duque, Jose and Masin, David and Turbay, Emilio and Acosta, Carlos}, year={2023}, month={Dec} }
 @article{covilla-varela_turbay_polo-mendoza_martinez-arguelles_cantero-durango_2023, title={Recycled Concrete Aggregates (RCA)-based asphalt mixtures: A performance-related evaluation with sustainability-criteria verification}, volume={403}, ISSN={["1879-0526"]}, DOI={10.1016/j.conbuildmat.2023.133203}, abstractNote={This investigation aims to explore the feasibility of using Recycled Concrete Aggregate (RCA) as a partial replacement for Natural Aggregates (NAs) in the production of Hot Mix Asphalt (HMA) and Warm Mix Asphalt (WMA). As WMA technology, Iterlow T (a chemical additive) was selected. For both types of asphalt mixtures, the RCA dosages of 0, 15, 30 and 45% by weight of coarse aggregates were considered. Notably, four different RCA sources were assessed, and the alkali-silica reaction and attached mortar quantity were analyzed for each one. In order to examine the effect of the coarse RCA on the performance-related properties of the asphalt mixtures, a test protocol was conducted. This protocol comprised four tests, i.e., stiffness modulus, moisture susceptibility, rutting resistance, and fatigue life. Additionality, a sustainability-criteria verification was performed employing the Life Cycle Assessment (LCA) and Life Cycle Cost Analysis (LCCA) methodologies. Respectively, the LCA and LCCA estimate each alternative's environmental impacts and production costs. Consequently, the main finding obtained were: (i) as the coarse RCA content increases, asphalt mixtures exhibit a reduction in their mechanical performance; (ii) the performance-related properties of the HMAs and WMAs with a 15% RCA dosage are acceptable, but more elevated amounts generate highly noticeable deterioration; and (iii) 15% of coarse RCA minimizes the environmental burdens and maximizes the cost-effectiveness of the asphalt mixtures fabrication. Thus, the main contribution to the literature that achieves this research is to perform a comprehensive multi-criteria examination on the potential of the RCA as a recycled material to minimize the depletion of non-renewable resources.}, journal={CONSTRUCTION AND BUILDING MATERIALS}, author={Covilla-Varela, Elvis and Turbay, Emilio and Polo-Mendoza, Rodrigo and Martinez-Arguelles, Gilberto and Cantero-Durango, Julio}, year={2023}, month={Nov} }