International Roughness Index Predictive Model for Rigid Pavements based on LTPP Data.
Naguib, A., EL-Badawy, S., Ibrahim, M. (2020). International Roughness Index Predictive Model for Rigid Pavements based on LTPP Data.. EKB Journal Management System, 40(2), 30-38. doi: 10.21608/bfemu.2020.101239
Ahmed M. Naguib; Sherif Massoud EL-Badawy; Mourad H. Ibrahim. "International Roughness Index Predictive Model for Rigid Pavements based on LTPP Data.". EKB Journal Management System, 40, 2, 2020, 30-38. doi: 10.21608/bfemu.2020.101239
Naguib, A., EL-Badawy, S., Ibrahim, M. (2020). 'International Roughness Index Predictive Model for Rigid Pavements based on LTPP Data.', EKB Journal Management System, 40(2), pp. 30-38. doi: 10.21608/bfemu.2020.101239
Naguib, A., EL-Badawy, S., Ibrahim, M. International Roughness Index Predictive Model for Rigid Pavements based on LTPP Data.. EKB Journal Management System, 2020; 40(2): 30-38. doi: 10.21608/bfemu.2020.101239

International Roughness Index Predictive Model for Rigid Pavements based on LTPP Data.

MEJ- Mansoura Engineering Journal
Article 10, Volume 40, Issue 2, June 2015, Page 30-38  XML PDF (645.05 K)
Document Type: Research Studies
DOI: 10.21608/bfemu.2020.101239
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Authors
Ahmed M. Naguib email 1; Sherif Massoud EL-Badawyorcid 2; Mourad H. Ibrahim3
1Demonstrator, Public Works Dept., Faculty of Engineering, Mansoura University, Egypt
23Associate Professor, Public Works Dept., Faculty of engineering, Mansoura University, Egypt
3Associate Professor, Public Works Dept., Faculty of engineering, Mansoura University, Egypt
Abstract
International roughness index (IRI) is the mathematical measurement of pavement smoothness. In this study, a regression model for IRI prediction for jointed plain concrete pavements (JPCP) was developed based on data from the Long Term Pavement Performance (LTPP) Project. A total of 327data points from 81pavement sections distributed all over the U.S. was used for the model development. The model predicts IRI as a function of pavement age, initial IRI, faulting, number of spelled joints, and number of transverse cracks, precipitation, and freezing index. The goodness of fit statistics of the model show excellent improvement over the previous model implemented in the Mechanistic-Empirical Pavement Design Guide (MEPDG). The model has a high coefficient of determination (R2) of 0.80.In addition the bias in the predicted values of IRI was significantly lower compared to the previous MEPDG regression model.
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