Hongbin Xu

Reliable pavement friction performance models are crucial for effective pavement management, affecting both the safety and maintenance costs. Existing models face challenges, including their reliance on laboratory test data, which is not always accessible at the network level, and their inability to capture potential increases in pavement friction shortly after construction or maintenance. To address these issues, this study proposed an exponential decay model that accounts for such potential friction increases as well as exploiting variables that are easily accessible at the network level within a state department of transportation’s pavement management system. Two sets of models were developed: (i) a statewide model for use in areas with limited data availability and (ii) regional models that incorporate adjustment factors for regional conditions. Both model types were validated against actual field measurements, demonstrating acceptable goodness-of-fit for pavement management application. The models showed that increased annual average daily traffic accelerated friction decay, whereas higher truck traffic percentages and higher temperatures were associated with higher terminal coefficients of friction. Conversely, higher precipitation increased the difference between terminal and initial coefficients of friction. Among pavement types, jointed concrete pavements maintained a stable coefficient of friction longer than continuously reinforced concrete pavements and both thick and thin asphalt concrete pavements. This study enhances pavement friction management by providing models that accurately reflect field conditions and regional variations. These models are essential for optimal long-term pavement management, such as the development of 4- and 10-year management plans.

Pavement performance models that can model consistently well across different pavement types, structures, traffic, and environmental conditions as well as throughout pavements’ lives are essential for the efficient management of transportation infrastructures. However, network pavement performance data are inherently imbalanced, and most pavement performance models are built on algorithms that assume balanced data distribution and equal error cost. As a result, the performance of most pavement performance models may vary for different pavement groups. Considering this, the objective of this study was to evaluate the data imbalance of the network pavement performance data and its effect on the performance of pavement deterioration models. Specifically, the analysis result for the longitudinal cracking model is presented in this paper. The data imbalance of the network pavement performance data mainly arises from two sources: the intrinsic pavement characteristics and pavement life expectancies. The intrinsic data imbalance arises either from the design or the management process, whereas the data imbalance introduced by the pavement life expectancies is primarily the result of the pavement management process. The distribution of the data used in this study exhibits substantial skewness owing to the data imbalance caused by the intrinsic pavement characteristics and life expectancies. The existence of the data imbalance leads to the biased performance of the deterioration models toward pavement classes with more data.

To achieve maximum benefit and cost-effectiveness, the application of pavement maintenance and rehabilitation treatments should be performed within optimal timing widows. In pavement management systems, these timing windows are primarily determined using pavement deterioration models and decision trees embedded in the system. However, owing to the prediction errors of the pavement performance models, the obtained time windows might not be optimal, making it difficult to achieve the expected maximum benefit and cost-effectiveness. To date, there is very little research quantifying the effect of model errors on the difference between the obtained benefit and the expected maximum benefit. Considering this, in this study, a reliability-based approach was developed to analyze the effect of model accuracy on the treatment benefit and extra cost. Through a case study, the effect of model accuracy on the treatment benefit was quantified. An increase in the model error was found to decrease the treatment benefit, while the extra cost needed to achieve the same benefit as the reference scenario increased. Although these findings might seem intuitive, this study has provided an analysis method to quantify such disbenefits.

Additive manufacturing (AM) is an innovative technology that creates parts by adding small portions of materials layer by layer, which frees designers to create parts that were not possible to manufacture with subtractive manufacturing processes previously. This led to wide-spread popularity of 3D-printing technology. In this technology. fused deposition modeling (FDM) is the most affordable one in the market now. Therefore, it is vital to understand how the print orientation, which can be customized very easily, affects the mechanical properties of the prints to maximize the strength of the product. This paper aims to present the methodology and results of the experimental characterization of the acrylonitrile butadiene styrene (ABS) 3D-printed part. Tensile characterization of ABS was performed to analyze anisotropic nature of 3D-printed parts caused by its unique manufacturing process. Specimens were printed with six different configurations: four raster ([45/−45], [30/−60], [15/−75] and [0/90]) and three build orientations (0 or flat, 45, and 90 degrees with respect to the build plate, all printed in [45/−45] raster orientation). Dogbone tensile specimens were printed and pulled using the tensile test machine. The young’s modulus, yield strength, ultimate strength, strain at failure, breaking strength were found for each configuration. As the build orientation angle increased and the raster orientation goes from [45/−45] to [0/90], mechanical properties decreased steadily except the Young’s modulus. For build orientation, Young’s modulus decreased first then increased as angle increased, and for the raster orientation, there was no statistically significant difference as raster changed from [45/−45] to [0/90]. Overall, [45/−45] flat configuration is the strongest and the most stable configuration.