Prediction of geogrid-reinforced flexible pavement performance using artificial neural network approach

Abstract

Prediction of geogrid-reinforced flexible pavement performance using artificial neural network approach This study aimed to develop a methodology to incorporate geogrid material into the Pavement ME Design software for predicting the geogrid-reinforced flexible pavement performance. A large database of pavement responses and corresponding material and structure properties were generated based on numerous runs of the developed geogrid-reinforced and unreinforced pavement models. The artificial neural network (ANN) models were developed from the generated database to predict the geogrid-reinforced pavement responses. The developed ANN models were sensitive to the change of base and subgrade moduli, and the variation of geogrid sheet stiffness and geogrid location. The ANN model-predicted geogrid-reinforced pavement responses were then used to determine the modified material properties due to geogrid reinforcement. The modified material properties were finally input into the Pavement ME Design software to predict geogrid-reinforced pavement performance. The ANN approach was rapid and efficient to predict geogrid-reinforced pavement performance, which was compatible with the Pavement ME Design software.

Use of an Artificial Neural Network Approach for the Prediction of Resilient Modulus for Unbound Granular Material

“…ANN models have been successfully developed to predict the crack growth function (e.g., reflective cracking and top-down cracking) in asphalt concrete ( 24 , 25 ). The ANN approach was also utilized to predict the geogrid-reinforced flexible pavement performance ( 26 ). In general, the development of ANN models includes two critical steps: (1) data collection; and (2) construction of ANN architecture.…”

Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning

“…Given the advances of deep learning, there has been significant research using these techniques for Pavement Engineering applications [21][22][23][24]. These applications can be assigned to the following areas: Pavement condition and performance predictions [25][26][27][28], Pavement management systems [29][30][31], pavement performance forecasting [32][33][34], structural evaluations [35][36][37], modelling pavement materials [38][39][40] and pavement image analysis and classification [22,[41][42][43][44]. Pavement Image analysis and classification is the most researched area, where the focus has been split between image classifications, where images are classified based on the distress occurring in the image; and object detection, where distresses are located within bounding boxes or masks within the image.…”

Proposta de Ensaio Reduzido de Módulo de Resiliência de Solos Finos para Aplicação em Pavimentos

“…No Brasil ainda existe um número relativamente pequeno de equipamentos triaxiais de carga repetida, concentrados em universidades e centros de pesquisas. O estabelecimento de alguns modelos baseados em parâmetros geotécnicos mais simples tem sido uma alternativa discutida por alguns autores como Ribeiro (2016), Taskiran (2010), Yildirim e Gunaydin (2011), Alawi e Rajab (2013), Gu et al (2017) e Nguyen e Mohajerani (2017) como forma de prever o MR e, assim, reduzir os custos relacionados à sua execução.…”