Sensitivity analysis of crack propagation in pavement bituminous layered structures using a hybrid system integrating Artificial Neural Networks and Finite Element Method

Gajewski, Jakub; Sadowski, Tomasz

doi:10.1016/j.commatsci.2013.09.025

Cited by 83 publications

(39 citation statements)

References 26 publications

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“…Attoh-Okine [22] also analyzed the road pavement condition using a back-propagation NN to develop a deterioration model for the international roughness index (IRI) of the road pavement. Various input variables and a NN algorithm were also utilized to predict cracking of the road pavement, rutting depth, IRI, the condition rating index (CRI), visual condition index (VCI), and the present serviceability index (PSI) [23][24][25][26][27][28][29]. In addition, it was found that the prediction performance when using the NN algorithm was higher than that achieved using the conventional statistical analysis [30,31].…”

Section: Literature Reviewmentioning

confidence: 99%

Development of the Road Pavement Deterioration Model Based on the Deep Learning Method

Choi

2019

Electronics

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In Korea, data on pavement conditions, such as cracks, rutting depth, and the international roughness index, are obtained using automatic pavement condition investigation equipment, such as ARAN and KRISS, for the same sections of national highways annually to manage their pavement conditions. This study predicts the deterioration of road pavement by using monitoring data from the Korean National Highway Pavement Management System and a recurrent neural network algorithm. The constructed algorithm predicts the pavement condition index for each section of the road network for one year by learning from the time series data for the preceding 10 years. Because pavement type, traffic load, and environmental characteristics differed by section, the sequence lengths (SQL) necessary to optimize each section were also different. The results of minimizing the root-mean-square error, according to the SQL by section and pavement condition index, showed that the error was reduced by 58.3–68.2% with a SQL value of 1, while pavement deterioration in each section could be predicted with a high coefficient of determination of 0.71–0.87. The accurate prediction of maintenance timing for pavement in this study will help optimize the life cycle of road pavement by increasing its life expectancy and reducing its maintenance budget.

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Section: Literature Reviewmentioning

confidence: 99%

Development of the Road Pavement Deterioration Model Based on the Deep Learning Method

Choi

2019

Electronics

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“…Pavement research engineers have made significant efforts to obtain a better understanding of cracking mechanisms in pavements, with most studies focusing on crack initiation (Alkaissi and Khafagy, 2009;Gajewski and Sadowski, 2014) and crack propagation (Svasdisant et al, 2002;Wang et al, 2003) in pavements. Casey et al (2012) investigated the stress intensity factors of mode one and mode two cracking at the tip of a short crack and their influence on the continued propagation of cracks further into the pavement.…”

Section: Introductionmentioning

confidence: 99%

Analytical method for evaluating top-down cracking in pavements

Gao¹

2016

Proceedings of the Institution of Civil Engineers - Transport

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Top-down cracking is a type of cracking that significantly impacts traffic safety and comfort and reduces a pavement's service life. Here, a cement concrete pavement containing a top-down crack is investigated under traffic load using a theoretical analysis method. In order to simplify the problem, the cement concrete pavement is modelled as an elastic plate on a Winkler foundation. Fourier transform methods and a dislocation density function are used to determine the stress intensity factors of a crack tip. A parametric study is conducted to isolate the effects of crack length, pavement stiffness and distance between the crack and the edge of a vehicle wheel. The results of the study show that crack length and load position significantly affect the stress intensity factors. However, stress intensity factors are less affected by the elastic modulus of the pavement material.

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“…Therefore, the inverse analyses were repeated continuously each time a researcher performed any new FE simulation model, which lead to discontinuity of FE and ANN inverse analysis in solving engineering problems (Djavanroodi, Omranpour, & Sedighi, 2013;Hosseini & Kadkhodayan, 2014;Kotkunde, Deole, Gupta, Singh, & Aditya, 2014). In some research, the FE and ANN inverse analysis is being considered as a statistical method or technique which treats the whole process as a black box (Gajewski & Sadowski, 2014;Xiang, Xiang, & Wu, 2014) without solving the complex engineering problem that includes basic information and detailed knowledge of material science, continuum mechanics theory, and FE methodologies.…”

Section: Introductionmentioning

confidence: 99%

Incorporating feedforward neural network within finite element analysis for L-bending springback prediction

Jamli

Ariffin

Wahab

2015

Expert Systems with Applications

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Sensitivity analysis of crack propagation in pavement bituminous layered structures using a hybrid system integrating Artificial Neural Networks and Finite Element Method

Cited by 83 publications

References 26 publications

Development of the Road Pavement Deterioration Model Based on the Deep Learning Method

Development of the Road Pavement Deterioration Model Based on the Deep Learning Method

Analytical method for evaluating top-down cracking in pavements

Incorporating feedforward neural network within finite element analysis for L-bending springback prediction

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