JPCP Slab-Level condition forecasting for slab replacement planning using 3D pavement surface data

Abstract

ABSTRACT With the aging of jointed plain concrete pavements (JPCP), state departments of transportation (DOT) have an urgent need to reliably predict and plan the annual slab replacement need. With the advancement of 3D laser technology, it has become feasible to extract the slab-level pavement condition, including cracking types and severity levels. This paper presents a JPCP slab-based forecasting methodology using a multi-stage Markov chain model that predicts slab-level cracking types and severity levels for planning and estimating slab replacement projects. Six years of accumulated slab-level pavement condition data were used to develop and validate the proposed forecasting model. The validation shows that using the proposed slab-level JPCP multi-stage Markov chain forecasting method is promising in its ability to predict JPCP segment’s cracking at different severity levels. A case study on I-16 JPCP in Georgia has demonstrated the feasibility of using the proposed model to predict slab-level JPCP condition and to estimate the slab replacement quantity. The proposed methodology enables state DOTs to leverage the 3D pavement surface data that is already being collected to forecast JPCP slab-level condition.