Nondestructive testing has become an integral part of pavement evaluation and rehabilitation strategies in recent years. Pavement evaluation employing the falling-weight deflectometer (FWD) and ground-penetrating radar (GPR) can provide valuable information about pavement performance characteristics and be a very useful tool for project prioritization purposes and estimation of a construction budget at the network level. Traditional obstacles to the use of the FWD and GPR in pavement evaluation at the network level used to be expenses involved in data collection, limited resources, and lack of simplified analysis procedures. Indiana experience in pavement evaluation with the FWD and the GPR at the network level is presented. A network-level FWD and GPR testing program was implemented as a part of a study to overcome those traditional obstacles. Periodic generation of necessary data will be useful in determining how best to quantify structural capacity and estimate annual construction budgets. Three FWD tests per mile on 2,200 lane-mi of the network is recommended annually for network-level pavement evaluation. The information collected will allow the equivalent of 100% coverage of the whole network in 5 years. GPR data are recommended to be collected once every 5 years (if another thickness inventory is needed) after the successful network thickness inventory conducted in this study. GPR data collection is also recommended at the project level and for special projects. Both FWD and GPR data are recommended to be used as part of the pavement management system, together with automated collection of data such as international roughness index, pavement condition rating, rut depth, pavement quality index, and skid resistance.

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Pavement friction has been identified as crucial in traffic safety. Since the Highway Safety Manual prediction algorithm is often based on crash frequency, the crash severity distribution might be assumed unchanged before and after the countermeasure. However, pavement surface treatments can improve the friction to different levels, by which crash severity outcomes may vary greatly. To explore the implicit effects of pavement friction on vehicle crash severity, this paper first validates the extreme gradient boosting model performance and then the Shapley additive explanations interaction values are employed to interpret individual features and the nonlinear interactions among predictors. Under various scenarios, the XGBoost output probability is utilized to convert into dynamic crash severity distributions. Results also indicate that friction becomes more significant when the friction number is less than 38, and immediate corrective actions are needed when the friction number is below 20.

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