A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Runway groove closure prediction modelling by gene expression programming (GEP)
Grooving on the runway is proven to improve frictional braking resistance and reduce the risk of hydroplaning during wet weather and maintain skid-resistant surfaces. Nonetheless, the runway grooves area deteriorates over time, and this groove closure is an outstanding distress that significantly diminishes grooves’ effectiveness. However, the degree of the deterioration of groove dimensions has not been quantified in a mathematical model yet. This paper is pioneering the predictive modelling by GeneXProTools 5.0 software for groove deterioration considering wheel pass number, load intensity, pavement layer thickness, and temperature as independent variables. The value of R2 achieved by the prediction model is 0.911, 0.921 and 0.924 for the training, validation and testing phase, respectively, which indicates that it fits with the experimental data very well. This model will render a meaningful contribution to airport authority in predicting grove deterioration and its serviceable length, leading to maintenance to reinstate the grooving.
Runway groove closure prediction modelling by gene expression programming (GEP)
Grooving on the runway is proven to improve frictional braking resistance and reduce the risk of hydroplaning during wet weather and maintain skid-resistant surfaces. Nonetheless, the runway grooves area deteriorates over time, and this groove closure is an outstanding distress that significantly diminishes grooves’ effectiveness. However, the degree of the deterioration of groove dimensions has not been quantified in a mathematical model yet. This paper is pioneering the predictive modelling by GeneXProTools 5.0 software for groove deterioration considering wheel pass number, load intensity, pavement layer thickness, and temperature as independent variables. The value of R2 achieved by the prediction model is 0.911, 0.921 and 0.924 for the training, validation and testing phase, respectively, which indicates that it fits with the experimental data very well. This model will render a meaningful contribution to airport authority in predicting grove deterioration and its serviceable length, leading to maintenance to reinstate the grooving.
Runway groove closure prediction modelling by gene expression programming (GEP)
Miah, Md Tofail (author) / Oh, Erwin (author) / Chai, Gary (author) / Bell, Phill (author)
Road Materials and Pavement Design ; 24 ; 2929-2958
2023-12-02
30 pages
Article (Journal)
Electronic Resource
Unknown
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