Regression models for permanent deformation parameters using in-service pavement data from the SPS-1 experiment

Salama, Hassan; Chatti, Karim; Haider, Syed

doi:10.1080/10298430701607425

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…The progression of flushing on the New Zealand LTPP sites was modeled with a linear regression model format (24)(25)(26). The inde pendent variables that were used in the linear regression included pavement surface thickness, surface age, percentage of heavy com mercial vehicles, rutting, and grade of aggregates in the seal.…”

Section: Progression Of Flushingmentioning

confidence: 99%

Using Multiple Investigative Techniques to Assess Flushing of Chip Seal Surfaces

Kodippily

Henning

Ingham

et al. 2014

Transportation Research Record: Journal of the Transportation R

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Flushing is a defect that has a damaging effect on the functional performance of chip seal pavements. The reported study was conducted to develop techniques to identify and assess flushed pavements effectively. The main aims of the study were first to investigate the effects of chip seal volumetrics on development of flushing and second to develop a model to forecast flushing of chip seal pavements. The study methodology consisted of mechanical testing of chip seal pavement samples and analysis of pavement performance data. Laboratory testing was conducted on samples obtained from flushed chip seal pavements from four regions in New Zealand. Samples were loaded by using a wheel-tracking device, and imaging techniques were used to assess the effects of micromechanical changes on development of flushing. Data analysis was performed on pavement condition data to identify factors providing the best prediction of flushing, and regression analysis was performed to develop a model to predict the initiation and progression of flushing. Study results revealed a direct relationship between flushing and reduction in air void volume that occurred as a result of loading. Data analysis revealed that the combination of factors providing the best indication of flushing was surface thickness, surface age, rut depth, and grade of aggregates. The flushing initiation model had an accuracy of 76%, and the flushing progression model was robust at predicting the quantity of flushing. By using the study outcomes, a pavement condition assessment guideline was developed to aid with assessing and managing flushed chip seal pavements.

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Section: Progression Of Flushingmentioning

confidence: 99%