Mixed linear modeling techniques for enhancing pavement performance predictions

Eleni Bardaka, Purdue University

Abstract

The use of appropriate advanced modeling techniques for predicting the performance of pavements that have received rehabilitation treatments may reap substantial benefits to a Pavement Management System (PMS). If the modeling technique is appropriately chosen on the basis of practicality, precision, the intended use of the model, and the nature of the pavement data, its applicability to PMS can be enhanced greatly. Pavement rehabilitation data typically constitutes of repeated measurements that form an unbalanced three-level nested structure, which makes the analysis quite challenging. This thesis proposes an enhanced methodological framework for pavement rehabilitation treatment analysis that uses mixed linear modeling techniques. Mixed models constitute a statistical technique that includes both fixed effects and random effects. The proposed framework is demonstrated using data from the Indiana Interstate network. In applying the developed framework, agencies can not only statistically quantify the post-rehabilitation performance of pavements, but also develop estimates and ranges of treatment service lives and thus update or refine the treatment service lives that are currently published in their pavement design or preservation manuals. These procedures are demonstrated analytically using a case study. The proposed framework can also be used by highway agencies as part of their network-level needs assessment because it offers a more reliable estimation of future physical and fiscal needs, as shown in the case study presented in this thesis.

Degree

M.S.C.E.

Advisors

Labi, Purdue University.

Subject Area

Management|Statistics|Civil engineering

COinS