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A novel weighted multi-output neural network (NN) model is proposed for predicting the deterioration of rigid pavements based on Iowa pavement management system data. This first-of-a-kind model simultaneously predicts four pavement condition metrics concerning rigid pavements, including IRI, faulting, longitudinal crack and transverse crack. It provides an opportunity to efficiently evaluate pavement conditions and to make treatment decisions based on multi-condition metrics, such as the pavement condition index (PCI) for budget allocation models. Compared to traditional single-output NN models, this multi-output model is capable of incorporating correlations among different condition metrics. During model training, each condition metric is assigned a weight to reflect its relative importance. When the weights equal to those in the formula for the multi-condition metric, the prediction performance for PCI is optimal (13% lower MSE than optimal, single-output models). The multi-output model improves on the prediction performance for three of the four individual condition metrics compared to optimal single-output models. Results show that the consideration of correlations could improve the prediction performance for single and multi-condition metrics. Finally, variable weighting is critical for achieving the optimal balance of prediction performance among the various metrics as dictated by the needs of the decisionmaker.

This paper proposes a new probabilistic treatment path dependence (PTPD) model for budget allocation within pavement network management problems. During the evaluation of treatment alternatives for a segment in a pavement network, the model considers benefits of both the evaluated treatment and its following actions. It also incorporates the influence of treatment cost and deterioration uncertainties. Treatments are selected for each segment in the pavement network using a risk-based optimization model. Three case studies are presented to illustrate the application and benefits of this new model. Results of the first two cases show that the risk-aversion coefficient in the model influences segment-level treatment selections and pavement network performance. The third case shows that the PTPD model performs better than a benefit-cost ratio model due to the incorporation of uncertainties and treatment path dependence. To obtain a similar performance level, the conventional model requires a 10.4% higher annual budget for the given case study. The results presented here suggest that elements of this model – notably consideration of uncertainty in deterioration and cost, treatment path dependency, and explicit risk trade-offs – could be incorporated into asset management tools to improve the cost-effectiveness of pavement network planning.

Life-cycle cost analysis (LCCA) is a way to evaluate the long-term cost effectiveness of different pavement designs or treatment actions. Owing to the existence of uncertainties, many probabilistic LCCA models have been proposed. They mainly use a prescribed treatment schedule or determine schedules by mechanistic-empirical analysis, potentially leading to the overestimation of life-cycle cost (LCC). In this paper, a new probabilistic simulation-optimization LCCA model is proposed. This new model determines treatment schedules by minimizing total LCC, including agency and user cost, which is different from current probabilistic models. In addition, it also incorporates uncertainties of treatment costs and deterioration processes. Two case studies are presented. The first one shows the influence of treatment schedule uncertainties on LCC distributions. After considering treatment schedule uncertainties, a tighter LCC distribution is estimated. The second case study compares the new model and a conventional prescribed-schedule model from the perspective of pavement design selection. The results show that the simulation-optimization model could lead to different preferred pavement designs than the prescribed-schedule model.

Performance-based planning is an important tool for allocating treatment resources across a pavement network from a set of candidate treatments with a budget constraint. Existing research focuses on improving allocation decisions through changes in the optimization algorithm without considering the consequences of how optimization analyses are framed. In this paper, both environmental and economic performance is evaluated for different problem framing in the form of different treatment strategies that consist of treatment materials, treatment types, and evaluation period. Results show that the proposed strategy that uses both concrete and asphalt, different treatment types, and a long evaluation period could reduce GHG emissions and improve pavement network performance based on the Iowa U.S. route network. Compared to a conventional 5-year asphalt-only strategy, proposed strategy can accomplish this with an annual budget that is 32% smaller and reduce associated GHG emissions by 21%. These results contribute to achieving a sustainable pavement network.