The Road Map To Apply Evolutionary Intelligence To Asphalt Pavement Modelling (original) (raw)
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2009
On 2008 was published (Salini et al, 2008) the guidelines to use an artificial intelligence based approach to create high quality models to asphalt pavements, surpassing most of the well know problems and limitations of the current empiric and empiricmechanistic approaches. This paper describe part of this new approach called INTELLIPave, with focus in show how to totally unknown variables are considered in the model in an implicit way, regardless its nature or complexity, and how the failure criteria is used aside, and no longer inside, of the model.
Frontiers in Built Environment
The pavement management system is recognized as an assertive discipline that works on pavement indices to predict the pavement performance condition. This study used soft computing methods such as genetic algorithms and artificial intelligence to propose a modern generation of pavement indices for road networks in Jordan. The datasets used in this study were collected from multiple roads in Jordan, and 128 data points were used in this study. The input variables are the pavement condition index (PCI) and the international roughness index (IRI) in the artificial neural network (ANN) and gene expression programming (GEP) models. The output variable is the pavement serviceability rate (PSR). The results show an efficient performance benefit of using these techniques. In addition, the ANN and GEP models were able to predict the output variable with a reasonable accuracy, where the ANN model has an R2 value of 0.95, 0.87, and 0.98 for the PCI, IRI, and PSR, respectively. The (R2) values ...
Soft Computing Technique in Prediction of Pavement Condition
Proceedings of WSEAS International Conference on …
Abstract: This paper presents a soft computing technique using neuro fuzzy approach to predict the future pavement condition based on the current pavement age and current pavement condition. The Ohio Department of Transportation (ODOT) database for the asphalt ...
Soft Computing Models to Predict Pavement Roughness: A Comparative Study
Advances in Civil Engineering
Pavement roughness as a critical determinant of public satisfaction can potentially play a major role in road or highway resource allocation to competing pavement resurfacing projects. With this in mind, the aim of the present paper is to develop an accurate model for the prediction of pavement roughness in terms of the International Roughness Index (IRI) using artificial neural networks (ANNs) and support vector machines (SVMs). The modeling is based on pavement roughness data collected periodically for a high-volume motorway during a seven-year period, on a yearly basis. The comparative study of the developed models concludes that the performance of the ANN model is slightly better compared to the SVM in terms of prediction accuracy. Further, the analysis results produce evidence in support of the statement that both models are capable to predict accurately pavement roughness; hence, they are deemed useful for supporting decision making of pavement maintenance and rehabilitation s...
Application of Artificial Intelligence for Optimization in Pavement Management
Artificial intelligence (AI) is a group of techniques that have quite a potential to be applied to pavement engineering and management. In this study, we developed a practical, flexible and out of the box approach to apply genetic algorithms to optimizing the budget allocation and the road maintenance strategy selection for a road network. The aim is to provide an alternative to existing software and better fit the requirements of an important number of pavement managers. To meet the objectives, a new indicator, named Road Global Value Index (RGVI), was created to contemplate the pavement condition, the traffic and the economic and political importance for each and every road section. This paper describes the approach and its components by an example confirming that genetic algorithms are very effective for the intended purpose.
Transportation Research Record: Journal of the Transportation Research Board, 2020
In their pavement management decision-making processes, U.S. state highway agencies are required to develop performance-based approaches by the Moving Ahead for Progress in the 21st Century (MAP-21) federal transportation legislation. One of the performance-based approaches to facilitate pavement management decision-making processes is the use of remaining service life (RSL) models. In this study, a detailed step-by-step methodology for the development of pavement performance and RSL prediction models for flexible and composite (asphalt concrete [AC] over jointed plain concrete pavement [JPCP]) pavement systems in Iowa is described. To develop such RSL models, pavement performance models based on statistics and artificial intelligence (AI) techniques were initially developed. While statistically defined pavement performance models were found to be accurate in predicting pavement performance at project level, AI-based pavement performance models were found to be successful in predict...