Abstract
This study introduces a machine learning approach based on Artificial Neural Networks (ANNs) for the prediction of Marshall test results, stiffness modulus and air voids data of different bituminous mixtures for road pavements. A novel approach for an objective and semi-automatic identification of the optimal ANN’s structure, defined by the so-called hyperparameters, has been introduced and discussed. Mechanical and volumetric data were obtained by conducting laboratory tests on 320 Marshall specimens, and the results were used to train the neural network. The k-fold Cross Validation method has been used for partitioning the available data set, to obtain an unbiased evaluation of the model predictive error. The ANN’s hyperparameters have been optimized using the Bayesian optimization, that overcame efficiently the more costly trial-and-error procedure and automated the hyperparameters tuning. The proposed ANN model is characterized by a Pearson coefficient value of 0.868.
Highlights
The algorithm has detected a region of the search space where the validation error was not significantly changing and, having assessed an over-exploration of a specific zone, it has decided to focus its search on an unexplored area that might have highperforming solutions
The hyperparameters discovered by the Bayesian Optimization (BO) algorithm defined an Artificial Neural Networks (ANNs) with L = 5 layers, N = 37 neurons, and hyperbolic tangent activation function, that was trained for E = 3552 iterations, with a learning rate of α = 0.01 and weight decay β = 1·10−6
The focus was on predicting at once volumetric properties and mechanical characteristics of bituminous mixtures prepared using different types of bitumen and aggregate, binder content and maximum nominal grain size, to support the mix design phase, providing numerical estimations of the investigated parameters without any other costly laboratory test
Summary
Unsuitable mechanical characteristics and volumetric properties of bituminous mixtures may lead to various types of distress in road pavements, generally comprising cracks due to fatigue or low temperature, permanent deformations, stripping, etc. Such failure modes decrease the service life of the pavement and represent serious safety issues for road users. Experimental methods, which require expensive laboratory tests and skilled technicians, are currently used to evaluate the bituminous mixtures’ performance [4,5,6,7,8,9].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
