An artificial neural network base prediction model and sensitivity analysis for Marshall Mix Design

2016-06-03
Öztürk, Hande Işık
Saglik, Ahmet
Demir, Birol
Gungor, Ahmet Gurkan
This study presents an artificial neural network (ANN) model to predict the Hot Mix Asphalt (HMA) volumetrics of mixtures prepared by following Marshall mix design procedure. The input data set of the model is determined to be the aggregate gradation, bulk specific gravity of aggregates, and binder content of the mixture based on the available data. The proposed ANN model utilizes one-layer Levenberg-Marquardt backpropagation to predict the theoretical maximum specific gravity of the loose mixture (Gmm) and the bulk specific gravity of the compacted mix (Gmb). The ANN was trained using data obtained from numerous roads with a total of 835 dif erent mix designs. The estimated HMA volumetrics, Gmb and Gmm, are used to calculate key design criteria such as percent of air voids (Va), Voids in the Mineral Aggregate (VMA), and Voids Filled with Asphalt (VFA). The results revealed that the ANN is able to predict volumetrics within a promising accuracy. The proposed ANN model was able to predict the Va within ±1.0% range 90% of the time and within ±0.5% range 55% of the time. The reasonable predictions of the model leads to significant time, cost and labor savings with respect to traditional Marshall Mix Design by limiting the number of trials to reach to the optimum mix design. With the developed ANN model, Marshall mix design can take 1.5 to 3 days with little validation ef ort in the laboratory. In addition, the model could be used as a practical Quality Control tool for roadway agencies to verify the mix designs.
Citation Formats
H. I. Öztürk, A. Saglik, B. Demir, and A. G. Gungor, “An artificial neural network base prediction model and sensitivity analysis for Marshall Mix Design,” presented at the 6th Eurasphalt & Eurobitume Congress (1-3 June 2016), Prague, Czech Republic, 2016, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/74767.