A multi-structure multi-run range (MSMRR) approach for using machine learning with constrained data in pavement engineering

In pavement engineering, the data sets that are typically obtained from experiments are small and cannot be classified as big data. The effective use of machine learning techniques such as artificial neural networks (ANN) for small data is a challenge because of poor accuracy of models. This paper presents a method of multiple structure multiple run and ranging to optimize ANN to produce models with small data sets with high accuracy. In this method, a large number of data fitting ANNs, with different number of neurons, layers, training and validation ratios, and randomized layer weights and biases are run in parallel, and the most accurate ANN is filtered out on the basis of the lowest MSE or highest R. The process is demonstrated with weather and pavement temperature data for a hot mix asphalt (HMA) and an open graded friction course (OGFC) pavement. Models are generated to predict the temperature at a depth of 12.5 mm below the surface. For the HMA pavement, an accuracy of 99.73% was obtained and an optimum structure was found to be with 4 layers, 11 neurons, 70% training ratio, 15% validation ratio. In the case of the OGFC pavement, an accuracy of 99.75% was obtained for an optimum structure with 3 layers, 11 neurons, 75% training ratio, 15% validation ratio. Furthermore, the fitting/regression problem was converted to a classification problem with different ranges, and then ANNs were utilized to develop very accurate classification models with small datasets.