Backcalculation analysis of pavement layer moduli is typically conducted based on falling weight deflectometer (FWD) measurements; however, the stationary nature of FWD requires lane closure and traffic control. To overcome these limitations, a number of continuous deflection devices were introduced in recent years. The objective of this study was to develop a methodology to incorporate traffic speed deflectometer (TSD) measurements in the backcalculation analysis. To achieve this objective, TSD and FWD measurements were used to train and to validate an artificial neural network (ANN) model that would convert TSD deflection measurements to FWD deflection measurements. The ANN model showed acceptable accuracy with a coefficient of determination of 0.81 and a good agreement between the backcalculated moduli from FWD and TSD measurements. Evaluation of the model showed that the backcalculated layer moduli from TSD could be used in pavement analysis and in structural health monitoring with a reasonable level of accuracy.

Crack sealing prevents the ingress of water in the pavement structure, thus preventing the weakening of the pavement and delaying its deterioration. Earlier studies indicate that sealing pavements in areas with a high ground water table (GWT) may prevent moisture from escaping upwards through cracks in asphalt pavements, therefore, accelerating stripping. The objective of this study was to provide guidelines for using crack sealing to minimize moisture entrapment under cracks, thus reducing stripping on low volume roadways. To achieve this, a calibrated Finite Element (FE) model was used to model a field experiment consisting of cracked and crack-sealed asphalt pavement sections. Sensitivity analysis was then conducted to compare crack-sealed and unsealed sections under different GWT levels, air relative humidity, air temperatures, rain intensities, and asphalt hydraulic conductivities. Results indicate that crack sealing could be applied under common rain intensities in Louisiana and any GWT depth without potential for stripping because of moisture entrapment if the hydraulic conductivity of the original pavement does not exceed 2 × 10–6 m/s. Yet, crack sealing should be applied after a dry period to ensure that the existing moisture in the original pavement is minimal. A non-linear regression model was developed for use in the Southern United States to help determine whether crack sealing should be used to avoid moisture damage in a cracked pavement at a given site based on the GWT and air relative humidity without the need for FE simulations. This can be a useful tool when planning maintenance activities.

Chip seal is a preventive maintenance technique typically applied on relatively low traffic roads to reduce pavement deterioration rate and to defer the need for costly rehabilitation activities. This study aims to address the common challenges with chip seal application in hot and humid climates such as Louisiana to ensure the maximum benefits are achieved. Specifically, this study evaluated the short and long-term field performances and optimal timing of chip seal by analyzing the cracking, roughness, and overall pavement conditions of 47 flexible and composite pavement sections in Louisiana. Furthermore, potential moisture damage in asphalt concrete after chip seal application was assessed. Results indicated that chip seal extended pavement service life by 4–17 years based on the pre-treatment pavement conditions and pavement type (flexible or composite). Based on the cost benefit analysis, it is recommended to use alternating cycles of asphalt overlay and chip seal on low volume roads (less than 5,300 vehicles per day) with chip seal applied when the pavement condition index (PCI) of the pavement drops to a value between 70 and 74. In this case, significant monetary savings could be achieved when compared with adding chip seals at different time periods (outside the recommended range of PCI between 70 and 74). Results also showed that the application of chip seal does not seem to contribute to moisture damage. Instead, shallow groundwater conditions present in the State seem to contribute to moisture damage in asphalt pavements owing to moisture entrapment underneath the asphalt layer.

One of the most common methods used to treat longitudinal and transverse cracks is crack sealing (CS), which is categorized as a preventive maintenance method. Field performance and cost-effectiveness of this treatment widely vary depending on pavement conditions and installation of the material. The objective of this study was to evaluate the field performance and cost-effectiveness of CS in flexible and composite pavements in hot and wet climates such as Louisiana, and to develop a model that would quantify the expected benefits of CS given project conditions. To achieve this objective, 28 control sections that were crack-sealed between 2003 and 2010 were monitored for at least four years. These sections included flexible and composite pavements, sealed and unsealed segments, and varying traffic levels. The performance of these sections was evaluated for the random cracking index (RCI) and roughness index (RI). Based on the results of this analysis, it was concluded that CS only has a significant impact on random cracking. When compared with untreated segments, CS extended pavement service life (PSL) by two years. When compared with the original pavement, CS extended PSL by 5.6 and 3.2 years for flexible and composite pavements, respectively, if applied at the correct time. The cost-benefit analysis indicated that CS is cost-effective whether asphalt emulsion or rubberized asphalt sealant is used. A non-linear regression model was developed to predict the extension in PSL because of CS without the need for performance data based on the average daily traffic (ADT), pavement type, and prior pavement conditions.

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Louisiana’s $6.3 million microsurfacing program is amongst the largest microsurfacing programs in the United States. As microsurfacing seals the road surface, the effectiveness of this treatment in such a setting has been a concern in recent years by linking it to moisture damage caused by the trapped moisture underneath the pavement especially in areas with shallow groundwater table. Furthermore, the cost-effectiveness and optimal timing of microsurfacing applications are also not well established for the south-central United States. The objective of this study was to assess the short-term and long-term effects of microsurfacing treatments and to evaluate whether microsurfacing is a major contributor to moisture damage. Field performance of 27 sections where microsurfacing treatments were applied between 2003 and 2008 was monitored for at least eight years. Results indicated that microsurfacing is most effective in addressing rutting damage as compared with the other performance indicators. Microsurfacing extended the service life of the pavements by 4.9–8.8 years. The effectiveness of microsurfacing was found to be optimum when applied to pavements with pre-treatment conditions ranging from 80 to 85. Results of this study also showed that microsurfacing-treated sections exhibited higher percentage of moisture damage as compared with the untreated sections in all the districts. Therefore, an in-depth assessment of the effects of microsurfacing on moisture damage in asphalt pavements is recommended.

Waterborne paint is the most common marking material used throughout the United States. Because of budget constraints, most transportation agencies repaint their markings based on a fixed schedule, which is questionable in relation to efficiency and economy. To overcome this problem, state agencies could evaluate the marking performance by utilizing measured retroreflectivity of waterborne paints applied in the National Transportation Product Evaluation Program (NTPEP) or by using retroreflectivity degradation models developed in previous studies. Generally, both options lack accuracy because of the high dimensionality and multi-collinearity of retroreflectivity data. Therefore, the objective of this study was to employ an advanced machine learning algorithm to develop performance prediction models for waterborne paints considering the variables that are believed to affect their performance. To achieve this objective, a total of 17,952 skip and wheel retroreflectivity measurements were collected from 10 test decks included in the NTPEP. Based on these data, two CatBoost models were developed with an acceptable level of accuracy which can predict the skip and wheel retroreflectivity of waterborne paints for up to 3 years using only the initial measured retroreflectivity and the anticipated project conditions over the intended prediction horizon, such as line color, traffic, air temperature, and so forth. These models could be used by transportation agencies throughout the United States to 1) compare between different products and select the best product for a specific project, and 2) determine the expected service life of a specific product based on a specified threshold retroreflectivity to plan for future restriping activities.

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