Pavement Friction Management (PFM) - A Step Toward Zero Fatalities

dc.contributor.authorNajafi, Shahriaren
dc.contributor.committeechairFlintsch, Gerardo W.en
dc.contributor.committeememberGuo, Fengen
dc.contributor.committeememberTaheri, Saieden
dc.contributor.committeememberTrani, Antonio A.en
dc.contributor.departmentCivil and Environmental Engineeringen
dc.description.abstractIt is important for highway agencies to monitor the pavement friction periodically and systematically to support their safety management programs. The collected data can help implement preservation policies that improve the safety of the roadway network and decrease the number of skidding-related crashes. This dissertation introduces new approaches to effectively use tire-pavement friction data for supporting asset management decisions. It follows a manuscript format and is composed of five papers. The first chapter of the dissertation discusses the principles of tire pavement friction and surface texture. Methods for measuring friction and texture are further discussed in this chapter. The importance of friction in safety design of highways is also highlighted. The second chapter discusses a case study on developing pavement friction management program. The proposed approach in this chapter can be used by highways agencies to develop pavement friction management program. Contrary to general perception, that friction is only influencing wet condition crashes, this study indicated that friction is associated with both wet and dry condition crashes. The third and fourth chapters of the dissertation introduce a soft-computing approach for pavement friction management. Artificial Neural Network and Fuzzy Logic approach are presented. The learning ability of Neural Network makes it appealing as it can learn from examples; however, Neural Network is generally complicated and hard to understand for practical purposes. The Fuzzy system on the other hand is easy to understand. The advantage of Fuzzy system over Artificial Neural Network is that it uses linguistic and human like rules. Sugeno Neuro-Fuzzy approach is used to tune the proposed Fuzzy Logic model. Neuro-Fuzzy approach has the benefit of incorporating both 'learning ability' of neural network and human ruled based decision making aspect of fuzzy logics. The application of the fuzzy system in real-time slippery spot warning system is demonstrated in chapter five. Finally, the sixth chapter of the dissertation evaluates the potential of grinding and grooving technique to restore friction properties of the pavement. Once sleek spots are identified through pavement friction management program, this technique can be used to restore the friction without compromising the roadway smoothness.en
dc.description.degreePh. D.en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.titlePavement Friction Management (PFM) - A Step Toward Zero Fatalitiesen
dc.typeDissertationen Engineeringen Polytechnic Institute and State Universityen D.en


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