Predicting international roughness index by deep neural networks with Levenberg-Marquardt backpropagation learning algorithm

Nicholas Fiorentini; Pietro Leandri; Massimo Losa

doi:10.1117/12.2598005

12 September 2021 Predicting international roughness index by deep neural networks with Levenberg-Marquardt backpropagation learning algorithm

Nicholas Fiorentini, Pietro Leandri, Massimo Losa

Author Affiliations +

Proceedings Volume 11863, Earth Resources and Environmental Remote Sensing/GIS Applications XII; 118630P (2021) https://doi.org/10.1117/12.2598005
Event: SPIE Remote Sensing, 2021, Online Only

Abstract

This paper proposes a methodology based on Artificial Neural Networks (ANNs) for integrating products derived by three on-ground Non-Destructive high-performance Techniques (NDTs) to estimate the International Roughness Index (IRI) of flexible road pavements. About that, IRI of 93 two-lane road sections has been detected by a Laser Profiler (LaP) and considered as output target to be predicted. Structural and geometrical road pavement parameters recognized by Falling Weight Deflectometer (FWD) and Ground Penetrating Radar (GPR) have been considered as input features, along with climate and rainfall information. Accordingly, different ANNs architectures have been trained (by using 70% of samples), validated (15% of samples), and tested (15% of samples) by the Levenberg-Marquardt (LM) backpropagation learning algorithm. We chose the LM since it allows handling limited data and avoiding overfitting issues. Outcomes reveal that a Deep Neural Network (DNN) recognizes hidden patterns between different road surveys and made the integration of NDTs possible and reliable. Specifically, a DNN architecture composed of two hidden layers containing 23 and 12 artificial neurons, respectively, shows a Determination Coefficient (R2) of 0.813 for the training phase, 0.761 for the validation phase, and 0.741 for the test phase. Also, the residual distribution is Gaussian with zero mean. Supported by these findings, we have deployed the DNN including all road sections, obtaining an R2 parameter of 0.762 and a Root Mean Square Error (RMSE) of 0.450 mm/km. Road authorities can consider ANNs and the LM backpropagation learning algorithm for appropriately predicting IRI and efficiently integrating NDT-based outcomes.

Conference Presentation

Citation Download Citation

Nicholas Fiorentini, Pietro Leandri, and Massimo Losa "Predicting international roughness index by deep neural networks with Levenberg-Marquardt backpropagation learning algorithm", Proc. SPIE 11863, Earth Resources and Environmental Remote Sensing/GIS Applications XII, 118630P (12 September 2021); https://doi.org/10.1117/12.2598005

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