Application of data handling techniques to predict pavement performance

The present study discusses the design of pavements and the importance of big data handling in improving their performance. A comprehensive framework based on a simple natural language processing technique is presented to reduce the computational time and error in data handling for pavement applications. The application of the proposed method to automate a graphical user interface (UI) adopted in pavement design is demonstrated. The proposed method was found to reduce the run-time by about 83% as compared to the conventional procedures. The proposed framework is highly flexible and can be adapted to extract data from various file formats and automate UIs at ease. To present the potential of this framework, about 0.2 million data sets representing pavement geometry and material properties were generated using language processing algorithms. Further, robust non-linear regression equations for calculating pavement damage in terms of fatigue and rutting strains were developed by using automated data processing through the pavement design interface