Developing Performance Prediction Models for Pavement Management Systems in Local Governments in Absence of Age Data

Abstract

Pavement Management Systems (PMS) use a strategic and data-driven approach to optimize budget allocation to various maintenance and rehabilitation (M&R) projects. Performance prediction models are used in PMS to determine the optimum timing for M&R interventions on every pavement section. Many local roadway agencies use empirical regression models which are based on past condition and age data. Often, these agencies are faced with limited resources for data collection and a high staff turnover rate, which all result in inadequate or unreliable construction history and pavement age data. This paper recommends a simple practical approach for local governments to develop performance prediction models in the absence of reliable pavement age data. Also, best practices for data pre-processing and validation of the model prediction capability are synthesized. Instead of using regression models based on condition and age, the pavement deterioration rate at each condition level is estimated. Similar to the Markovian transition probability concept, it is assumed that deterioration rates for every family of pavements are independent of time and only dependent on the current condition level. For every pair of subsequent condition measurements on a section, the difference in condition score is normalized by the difference in measurement time. These deterioration rates are then classified into bins based on the initial condition level for every pair of measurements. The average deterioration rate for all data records in each bin is then used to build a deterioration curve. This approach is demonstrated in this paper using real but anonymous agency data.

Estimation of Flexible Pavement Structural Capacity Using Machine Learning Techniques

“…In general, there are two approaches to pavement performance prediction [5,16]: deterministic and probabilistic approaches. In the former, for each set of input variables values, a single value is predicted as the pavement performance.…”

“…On the contrary, a probabilistic approach predicts the pavement performance based on the probability of occurrence. The main probabilistic methods are [5]: Machine learning, Markov probability matrices, survivor curves, and Bayesian inference techniques. ML is a soft computing technique widely utilized to develop the performance models of pavement [20,21].…”

“…Contrary to the common perception, ML techniques do not act as a black box but rather as statistical training methods. Many ML techniques are used to model pavement performance, including artificial neural networks (ANN) and support vector regression (SVR) [5,22]. In the above techniques, the machine seeks to detect the intercommunication of outputs and inputs by taking biases and weights into account [5].…”

“…Many ML techniques are used to model pavement performance, including artificial neural networks (ANN) and support vector regression (SVR) [5,22]. In the above techniques, the machine seeks to detect the intercommunication of outputs and inputs by taking biases and weights into account [5]. The performance of an in-service pavement determined by the introduction and calculation of an index.…”

“…Thus, the prediction of pavement performance is one of the critical steps of PMS. In PMS, performance prediction models employed for the following purposes [5]:…”