Comparisons of Faulting-Based Pavement Performance Prediction Models

Abstract

Faulting prediction is the core of concrete pavement maintenance and design. Highway agencies are always faced with the problem of lower accuracy for the prediction which causes costly maintenance. Although many researchers have developed some performance prediction models, the accuracy of prediction has remained a challenge. This paper reviews performance prediction models and JPCP faulting models that have been used in past research. Then three models including multivariate nonlinear regression (MNLR) model, artificial neural network (ANN) model, and Markov Chain (MC) model are tested and compared using a set of actual pavement survey data taken on interstate highway with varying design features, traffic, and climate data. It is found that MNLR model needs further recalibration, while the ANN model needs more data for training the network. MC model seems a good tool for pavement performance prediction when the data is limited, but it is based on visual inspections and not explicitly related to quantitative physical parameters. This paper then suggests that the further direction for developing the performance prediction model is incorporating the advantages and disadvantages of different models to obtain better accuracy.

Predictive Analytics for Roadway Maintenance: A Review of Current Models, Challenges, and Opportunities

“…Deterministic models are in the form of mathematical functions. Probabilistic models use probability distributions to forecast a range of possible conditions or the probability of a particular condition in the future [21][22][23]. However, in a machine learning model, Artificial Intelligence (AI) is leveraged to find relationships between contributing factors and the condition/life expectancy of assets, based on the learning process from historically recorded data.…”

“…In Ordered-probit, the probability concept is used to forecast the condition of an asset in discrete ordinal levels. Finally, Bayesian analysis incorporates existing knowledge of condition measurements with the information from historical observations to provide a probabilistic prediction model [22,23,40].…”

“…Time-consuming training process and instability in local minimum points during training were addressed in the literature as shortcomings of the ANN. Additionally, selecting the optimal architecture of neural nets and choosing proper training algorithms have been addressed as challenging issues in an ANN model [22].…”

“…Hence, these models provide a deep understanding of the relationships between mechanical responses and the condition. However, the accuracy of results is questionable due to the complexity of considering all parameters that potentially influence the condition [22].…”

“…Mechanistic-empirical procedures use mechanistic functions and historical observations to come up with a prediction model. These models determine functional forms and descriptive variables by leveraging mechanistic methods [21,22]. These models are often applied at the project level and rarely at the network level.…”