Precision Estimates and Statements for Performance Indices from the Indirect Tensile Cracking Test at Intermediate Temperature

The Virginia Department of Transportation (DOT) has taken initiatives to implement the Balanced Mix Design (BMD) method to assure the long-term service life of its pavement network from a mixture quality standpoint. As part of this initiative, the cracking tolerance (CT) index obtained from the indirect tensile (IDT) test at intermediate temperature in accordance with ASTM D8225-19 was selected for evaluating the cracking potential of dense-graded surface asphalt mixtures. This prompted the need to generate the precision estimates (repeatability and reproducibility) for the test method for proper implementation during quality measurement practices. Thus, this interlaboratory study was undertaken to determine the precision estimates of the CT index calculated from the IDT test and to develop the associated precision statements. In addition, fracture strain tolerance (FST) and indirect tensile strength were included. Two asphalt mixes with significantly different CT index values were designed for the evaluation. Forty-six pairs of five replicate sets of compacted specimens (one set for each mix) were sent to 41 participating laboratories to be tested at 25°C. The test results were checked for data quality. The effects of loading rate and manufacturers on the indices were also evaluated. The test results indicated that one third of the sets were not tested in full accordance with the ASTM standard, indicating a need for training. The results also indicated that the specified loading rate of 50 ± 2 mm/min in ASTM D8225-19 for the IDT test may need revision. Finally, the precision estimates and associated statements for the three indices were presented.

Review From Multiple Perspectives for the State of the Practice on the Use of Recycled Asphalt Materials and Recycling Agents in Asphalt Concrete Surface Mixtures

The objective of this paper is to provide information from multiple perspectives on the current state of the practice with regard to using recycled materials and recycling agents (RAs) in asphalt concrete mixtures. This information was collected through a survey of U.S. transportation agencies and RA suppliers combined with a search of RA-related specifications and pilot projects previously constructed. Moreover, a case study describing the Virginia Department of Transportation’s experience with RAs provides a tangible example of how at least one agency is approaching the potential implementation of these technologies. This practice review was achieved by documenting the experience, lessons learned, and best practices of multiple asphalt experienced contractors and asphalt binder suppliers in the Virginia area. This paper follows a similar survey conducted in 2014 as part of NCHRP 09-58 and provides a second look at the use of RAs across North America. Not all state departments of transportation have experience with using RAs. Factors preventing the use of RAs included specification limitations, lack of expertise in processing recycled materials, supporting data, and negative prior experiences. Developing a performance-based testing framework is mandatory for the successful use of RAs. In general, good and frequent communication with the RA supplier is critical and necessary during the planning stages, the production of mixtures, and the continuous quality control by the supplier to resolve issues when they arise. Finally, a strong quality control and quality assurance-testing program should be implemented to ensure that materials meet the properties needed to produce a good-performing mixture.

Validation of Performance-Based Specifications for Surface Asphalt Mixtures in Virginia

The Virginia Department of Transportation (VDOT), like many owner agencies, is interested in ways to facilitate the increased durability of asphalt mixes in an effort to make its roadway network more sustainable, longer lasting, and more economical. The balanced mix design (BMD) method proposes to address this through the incorporation of performance criteria into mix design and acceptance. VDOT has committed to the implementation of the BMD method in an effort to improve asphalt mix performance. The purpose of this study was to continue advancing efforts toward implementation of BMD through the evaluation of 13 asphalt mixes using performance-indicating laboratory tests, validation of the initial performance tests selected for BMD use, and validation of the initial test threshold criteria. Based on the results, the asphalt pavement analyzer (APA) rut test, indirect tensile cracking test (IDT-CT), and Cantabro test were found suitable for continued use in BMD. The current threshold criteria for all three tests were found reasonable based on additional mix testing. The study recommends that APA rut test and IDT-CT results should be compared and correlated to fundamental rutting and cracking tests, respectively, as well as to performance predictions obtained from mechanistic-empirical pavement design simulations, and to field performance for full assurance that test threshold values are appropriate. It was further recommended to evaluate the Cantabro, IDT-CT, and APA rut tests to determine acceptable variability and establish precision statements.

Multi-Level Laboratory Performance Evaluation of Conventional and High Polymer-Modified Asphalt Mixtures

Asphalt concrete (AC) overlays have been one of the most common treatments used by the Virginia Department of Transportation (VDOT) for maintaining/rehabilitating pavements. However, when the overlay is placed on existing composite pavements or cracked AC pavements, differential movements across any cracks or joints can result in physical tearing of the AC overlay. Thus, the long-term performance of many AC overlays will highly depend on their ability to resist cracking. The purpose of this study was to assess the viability of using high polymer-modified (HP) AC mixtures in Virginia as a crack mitigation technique or when deemed appropriate as a tool for increased resistance to rutting and cracking on higher volume facilities. Another objective was to assess the ability of various testing protocols to discern the performance of pavements through a comprehensive evaluation of three conventional polymer-modified (PMA) and five HP field-produced mixtures placed in Virginia. This included laboratory testing at multiple levels of complexity (basic, intermediate, and advanced) on collected asphalt binders, plant-produced asphalt mixtures, and field cores. The performance characteristics of PMA and HP mixes were evaluated in the laboratory in relation to durability and resistance to rutting and cracking. Based on the mixes tested, stone matrix asphalt (SMA) mixes showed better performance than dense-graded surface mixes (SM) regardless of the asphalt binder type. Moreover, HP mixes showed better performance than PMA mixes regardless of the mixture type. Overall, SMA-HP mixes showed the most promising performance among all evaluated mixes.

Predicting increases in COVID-19 incidence to identify locations for targeted testing in West Virginia: A machine learning enhanced approach

During the COVID-19 pandemic, West Virginia developed an aggressive SARS-CoV-2 testing strategy which included utilizing pop-up mobile testing in locations anticipated to have near-term increases in SARS-CoV-2 infections. This study describes and compares two methods for predicting near-term SARS-CoV-2 incidence in West Virginia counties. The first method, Rt Only, is solely based on producing forecasts for each county using the daily instantaneous reproductive numbers, Rt. The second method, ML+Rt, is a machine learning approach that uses a Long Short-Term Memory network to predict the near-term number of cases for each county using epidemiological statistics such as Rt, county population information, and time series trends including information on major holidays, as well as leveraging statewide COVID-19 trends across counties and county population size. Both approaches used daily county-level SARS-CoV-2 incidence data provided by the West Virginia Department Health and Human Resources beginning April 2020. The methods are compared on the accuracy of near-term SARS-CoV-2 increases predictions by county over 17 weeks from January 1, 2021- April 30, 2021. Both methods performed well (correlation between forecasted number of cases and the actual number of cases week over week is 0.872 for the ML+Rt method and 0.867 for the Rt Only method) but differ in performance at various time points. Over the 17-week assessment period, the ML+Rt method outperforms the Rt Only method in identifying larger spikes. Results show that both methods perform adequately in both rural and non-rural predictions. Finally, a detailed discussion on practical issues regarding implementing forecasting models for public health action based on Rt is provided, and the potential for further development of machine learning methods that are enhanced by Rt.

Predicting increases in COVID-19 incidence to identify locations for targeted testing in West Virginia: A machine learning enhanced approach

During the COVID-19 pandemic, West Virginia developed an aggressive SARS-CoV-2 testing strategy which included utilizing pop-up mobile testing in locations anticipated to have near-term increases in SARS-CXoV-2 infections. In this study, we describe and compare two methods for predicting near-term SARS-CoV-2 incidence in West Virginia counties. The first method, Rt Only, is solely based on producing forecasts for each county using the daily instantaneous reproductive numbers, Rt. The second method, ML+ Rt, is a machine learning approach that uses a Long Short-Term Memory network to predict the near-term number of cases for each county using epidemiological statistics such as Rt , county population information, and time series trends including information on major holidays, as well as leveraging statewide COVID-19 trends across counties and county population size. Both approaches used daily county-level SARS-CoV-2 incidence data provided by the West Virginia Department Health and Human Resources beginning April 2020. The methods are compared on the accuracy of near-term SARS-CoV-2 increases predictions by county over 17 weeks from January 1, 2021- April 30, 2021. Both methods performed well (correlation between forecasted number of cases and the actual number of cases week over week is 0.872 for the ML+Rt method and 0.867 for the Rt Only method) but differ in performance at various time points. Over the 17-week assessment period, the ML+Rt method outperforms the Rt Only method in identifying larger spikes. We also find that both methods perform adequately in both rural and non-rural predictions. Finally, we provide a detailed discussion on practical issues regarding implementing forecasting models for public health action based on Rt, and the potential for further development of machine learning methods that are enhanced by Rt.

Deep Learning-Based Visual Identification of Signs of Bat Presence in Bridge Infrastructure Images: A Transfer Learning Approach

Bat inventory surveys on bridges, structures, and dwellings are an important step in protecting threatened and endangered bat species that use the infrastructure as roosting locations. Observing guano droppings and staining is a common indicator of bat presence, but it can be difficult to verify whether certain stains originated from bats or other sources such as water seeps, rust staining, asphalt leaching, or other structural deterioration mechanisms. While humans find it hard to distinguish bat indicators without training, from a computer vision perspective they show different features that, coupled with expert opinion, can be used for automated detection of bat presence. To facilitate bat presence detection and streamline bat surveys, this paper leverages recent advances in visual recognition using deep learning to develop an image classification system that identifies bat indicators. An array of state-of-the-art convolutional neural networks were investigated. To overcome the shortage of data, parameters previously trained on large-scale datasets were used to transfer the learned feature representations. Using a pool of digital photographs collected by Virginia Department of Transportation (VDOT), a visual recognition model was developed and achieved 92.0% accuracy during testing. To facilitate the application of the developed model, a prototype web application was created to allow users to interactively upload images of stains on structures and receive classification results from the model. The web application is being deployed by VDOT in a pilot study and the success of the proposed approach is expected to help facilitate bat inventory surveys and the resulting conservation efforts.

Development of Roughness Prediction Models for Life Cycle Assessment Studies of Recycled Pavement Projects

The few existing life cycle assessment studies considering pavement recycling techniques usually omit the stages of maintenance and rehabilitation (M&R) and use. The reason for this omission is the lack of information about how the pavement’s performance evolves over time and absence of methods to determine the M&R frequency and service life for completed projects. As a result, the deterioration of pavement recycling projects in the long term is not clearly understood. Few projects have available data, the majority of which are on low volume primary and secondary roads. This paper describes an approach to develop a family of roughness models for recycling projects in Colorado using functional data analysis, and individual models for selected projects in Virginia to support ongoing life cycle assessment (LCA) studies. In the case of Colorado, full depth reclamation (FDR) projects will most likely deteriorate following an average group rate of 1.4 in./mi/year, with an initial international roughness index (IRI) between 52 and 70 in./mi. For the individual roughness models developed for Virginia projects, the initial IRI values and the rate of change for the treatments analyzed were found to range between 49 and 107 in./mi and between 0.7 and 5.2 in./mi/year, respectively, depending on the recycling method and type of stabilization treatment. The results of an LCA case study show that, in addition to recycling, Virginia Department of Transportation can achieve statewide emission reduction goals if focus is placed on achieving smoother roads while measures are taken to keep the annual rates of deterioration low.

A retrospective analysis of bull:cow ratio effects on pregnancy rates of beef cows previously enrolled in fixed-time artificial insemination protocols

This retrospective study aimed to determine if the number of cows exposed per bull affects pregnancy rates of cows returning to estrus after fixed-time artificial insemination (FTAI). Data were compiled over the course of 13 breeding seasons (6 fall and 7 spring seasons) between 2010 and 2017 from the Virginia Department of Corrections herd. Available records contained data from 17 farms and 324 groups of cows (average 47 cows/group). Multiparous cows and heifers (average age per group: 5.11 ± 0.14 years; n = 14,868) were exposed to FTAI. After FTAI, animals were placed on pasture with bulls diagnosed as fertile by a breeding soundness exam for natural service of cows who did not become pregnant to FTAI (n = 7,248; average 22 cows/group). Animals were classified as pregnant to FTAI, to natural service on first return to estrus, or to natural service on second or subsequent estrus determined by fetal aging at pregnancy diagnosis. The bull:cow ratio for the total number of cows exposed ranged from 1:9 to 1:73 with an average of 1:31. The bull:cow ratio considering only open cows exposed after FTAI ranged from 1:2 to 1:44 with an average of 1:14. There was significant negative, small correlation between the bull:cow ratio for total number of cows exposed and return to estrus pregnancy rate in fall breeding seasons (P = 0.01, r 2 = 0.04) but not in spring (P = 0.90). There was a significant negative, small correlation between bull:cow ratio of open cows exposed and pregnancy rates to first return to estrus in fall herds with a single sire (P < 0.001, r 2 = 0.11). There was no correlation in fall herds using multiple sires or spring herds (P ≥ 0.12). Bull:cow ratio accounted for only 1 – 11% of variation in the pregnancy rates, thus we conclude that a decreased bull:cow ratio (up to 1:73) did not affect natural service return to estrus pregnancy rate. Cattlemen may consider a reduced number of bulls needed for natural service breeding after FTAI, which can decrease bull related costs and increase the economic feasibility of adopting FTAI protocols.