This paper presents the analysis of pavement mechanistic-empirical design (PMED) climate input data for the state of Tennessee. The climatic data source considered for the analysis is Modern-Era Retrospective Analysis for Research and Application (MERRA). First, the sensitivity analysis using 2(k) factorial design method, considering lower and higher extremes of each climatic input and water table, was conducted to determine the sensitivity of climatic inputs to pavement distress predictions. Then, virtual weather stations (VWSs) were created, and their predicted performance was analyzed in comparison to the existing stations. On sensitivity analysis of the enhanced integrated climatic model (EICM), temperature was the most sensitive climatic input in PMED distress predictions, while humidity had no effect on pavement distress predictions. Performance evaluation of PMED VWSs indicated a significant difference in some of the predicted distresses when comparing PMED VWSs and MERRA stations at identical locations.

Properly managing the quantity of stormwater is essential to avoid infrastructure damage and ensure road safety. Climate change has emerged as a critical challenge to the sustainable development of transportation infrastructure stormwater management systems. As global temperatures rise and extreme weather events become more frequent and intense, civil infrastructure is facing increased vulnerability to flooding, erosion, and structural damage. Researchers have been working to quantify such impacts from climate change with urban development and urban stormwater infrastructure as case studies. Understanding current research of climate impacts on stormwater management is vital for engineers and planners as it enables them to develop resilient and sustainable infrastructure. With climate change influencing precipitation patterns and intensities, informed decisions can lead to effective drainage systems, minimizing flooding risks, protecting ecosystems, and enhancing overall infrastructure resilience. Hence, in order to provide understanding of the research progress, this paper provides a critical review of the literature with regard to the methodologies used to study climate change and its impact on stormwater runoff. Sources of projected climate data and how they are downscaled and used are also reviewed. Finally, it identifies and summarizes future research needs.

In recent years, severe climate changes have led to extremely hot weather to severe rainfalls resulting in flooding and disruption of transportation infrastructure. For example, in the event of flooding, pavements deteriorate faster due to the presence of water on the pavement and hence require more frequent maintenance. Although there have been noticeable changes in weather patterns, the pavement design inputs have not been improved or updated to reflect the weather changes. However, there is noteworthy progress in the research world that is working to assign weight to climate conditions and pavement performance and hence changes to pavement design standards for long sustainable transportation infrastructure. As part of this research review paper, the status of the research and approaches being used to study climate change impacts on pavement performance are identified; hence, a framework for future research and pavement design and maintenance standards can be established. The paper also elaborated on findings related to sources of climate data being used and approaches used to format the climate data to use with standard pavement design, and analysis tools are discussed. The findings presented in this paper are expected to benefit researchers, public agencies, and engineers to set future research direction, design, and operation of pavement-related transportation infrastructure.

This study focuses on improving climate forecasting in Tennessee, which is a challenging task due to global climate change. The research combines advanced technologies, specifically Long Short-Term Memory (LSTM) networks and Artificial Neural Networks (ANN), to create a predictive model tailored to Tennessee's unique climate patterns. The model successfully generates accurate forecasts from historical climate data, showing its reliability and adaptability in capturing long-term climate trends. This research has significant implications for addressing the effects of climate change, bridging the gap between scientific innovation and practical climate solutions, and contributing to a sustainable future.

Tennessee Department of Transportation (TDOT) is among the States Departments of Transportation, taking measures to implement the Pavement Mechanistic-Empirical Design (PMED) approach and analysis from the AASHTO 1993 pavement design procedure. TDOT has funded the calibration of Tennessee distress models and traffic inputs for its interstate and state routes. In this study, local and national calibrated models are used to evaluate the performance of Long-Term Pavement Performance (LTPP) sites in Tennessee using AASHTOWare Pavement Mechanistic-Empirical Design v2.5.5 (PMED v2.5.5). Traffic inputs include level 1, LTPP traffic volume adjustment factors; level 2, Tennessee statewide traffic volume adjustment factors; and level 3 the default national traffic volume adjustment factors. The study also considers LTPP sites that are relatively close to climatic stations, Modern Era Retrospective-analysis for Research and Application (MERRA) and North American Regional Reanalysis (NARR), in distance and elevation. The distances and elevation differences were considered to eliminate the need for creating visual weather stations. The statistical analysis comparing measured and predicted distresses showed that local calibrated distress models with level 2 traffic inputs predicted relatively close to the measured distresses compared to traffic levels 1 and 3. For all traffic levels and local calibration, MERRA predicted better on bottom up cracking and permanent deformation, while NARR predicted better on terminal IRI. For national calibrations, MERRA predicted better on bottom up cracking, Jointed Plain Concrete Pavement (JPCP) transverse cracking, and total transverse cracking, while NARR predicted better on permanent deformation and terminal IRI.

Graphene produced by different methods can present varying physicochemical properties and quality, resulting in a wide range of applications. The implementation of a novel method to synthesize graphene requires characterizations to determine the relevant physicochemical and functional properties for its tailored application. We present a novel method for multilayer graphene synthesis using atmospheric carbon dioxide with characterization. Synthesis begins with carbon dioxide sequestered from air by monoethanolamine dissolution and released into an enclosed vessel. Magnesium is ignited in the presence of the concentrated carbon dioxide, resulting in the formation of graphene flakes. These flakes are separated and enhanced by washing with hydrochloric acid and exfoliation by ammonium sulfate, which is then cycled through a tumble blender and filtrated. Raman spectroscopic characterization, FTIR spectroscopic characterization, XPS spectroscopic characterization, SEM imaging, and TEM imaging indicated that the graphene has fifteen layers with some remnant oxygen-possessing and nitrogen-possessing functional groups. The multilayer graphene flake possessed particle sizes ranging from 2 µm to 80 µm in diameter. BET analysis measured the surface area of the multilayer graphene particles as 330 m2/g, and the pore size distribution indicated about 51% of the pores as having diameters from 0.8 nm to 5 nm. This study demonstrates a novel and scalable method to synthesize multilayer graphene using CO2 from ambient air at 1 g/kWh electricity, potentially allowing for multilayer graphene production by the ton. The approach creates opportunities to synthesize multilayer graphene particles with defined properties through a careful control of the synthesis parameters for tailored applications.

In this paper, the accuracy of two mesh-free approximation approaches, the Gravity model and Radial Basis Function, are compared. The two schemes' convergence behaviors prove that RBF is faster and more accurate than the Gravity model. As a case study, the interpolation of temperature at different locations in Tennesse, USA, are compared. Delaunay mesh generation is used to create random points inside and on the border, which data can be incorporated in these locations. 49 MERRA weather stations as used as data sources to provide the temperature at a specific day and hour. The contours of interpolated temperatures provided in the result section assert RBF is a more accurate method than the Gravity model by showing a smoother and broader range of interpolated data.

In this paper, the accuracy of two mesh-free approximation approaches, the Gravity model and Radial Basis Function, are compared. The two schemes' convergence behaviors prove that RBF is faster and more accurate than the Gravity model. As a case study, the interpolation of temperature at different locations in Tennesse, USA, are compared. Delaunay mesh generation is used to create random points inside and on the border, which data can be incorporated in these locations. 49 MERRA weather stations as used as data sources to provide the temperature at a specific day and hour. The contours of interpolated temperatures provided in the result section assert RBF is a more accurate method than the Gravity model by showing a smoother and broader range of interpolated data.

Sensitivity analysis is conducted on AASHTOWare Pavement Mechanistic Empirical Design (PMED) by using a Design of Experiment method that considers 2k factorial design, an unbiased method that analyses the effect of climatic inputs and depth of water table to flexible pavement distress predictions. This study used three LTPP sites located in Tennessee; these sites are of Functional Classes 1, 2 and 7. From the analysis of climatic inputs and depth of water table, temperature was observed to be the most sensitive climatic input followed by Wind speed and the depth of water table. Percent sunshine has a less significant influence on the distress predictions while relative humidity was observed to have a negligible effect to the flexible pavement distress predictions.

With cutting edge deep learning breakthrough, numerous innovations in many fields including civil engineering are stimulated. However, a fundamental issue that civil engineering research community currently facing is lack of a publicly available, free, quality-controlled and human-annotated large dataset that supports and drives civil engineering deep learning research and applications on such as intelligent transportation including connected vehicle, structural health monitoring, and bridge inspection. This paper is a general discussion about demanding needs and construction of a long-anticipated dataset for researchers and engineers in civil engineering and beyond for providing critical training, testing and benchmarking data. The establishment of such a free dataset will remove a major hurdle and boost deep learning research in civil engineering and we hope this work will urge researchers, engineers, government agencies and even computer scientists to work together to start building such datasets. A framework has been developed for the proposed database. Also, some pilot study databases were developed for concrete crack detection, pavement crack detection using normal and infrared thermography, as well as pedestrian and bicyclist detection. A convolution neural network model called Faster RCNN was deployed to check the detection accuracy and a 98% detection accuracy of the proposed datasets was obtained.

The Mechanistic-Empirical Pavement Design Guide addresses climate effects on pavement design in a comprehensive way, which allows for investigating the effect of climate on pavement performance. However, it requires detailed climate inputs, which might not be readily available for most of the state departments of transportation. The AASHTOWare Pavement Mechanistic-Empirical Design (PMED) version 2.3 (v2.3) climate database encompasses 12 weather stations in the state of Tennessee, which does not satisfactorily represent all climatic regions in the state. The terrain in Tennessee varies from flat in the west to mountainous in the east. To evaluate the effectiveness of the updated AASHTOWare PMED v2.3 climate data input, this study analyses the performance of selected pavements in the state of Tennessee using the Modern-Era Retrospective Analysis for Research and Applications (MERRA) and the AASHTOWare PMED v2.3 databases as sources of PMED climate data inputs. A comparative analysis of the two climate data sources is conducted using eight long-term pavement performance (LTPP) sites in the state of Tennessee. The study revealed that MERRA as a climate data source for the state of Tennessee offers better geographic coverage, and therefore provides more precise distress predictions than the AASHTOWare PMED v2.3 climate database.

We propose coupling the state-of-the-art computer technology deep learning and unmanned aerial vehicles (UAV) to automatically detect and assess the health condition of civil infrastructure such as bridges and pavements. UAV carrying high resolution camera and infrared thermography camera to collect a large amount of image data from the target infrastructure, which serves as inputs of trained deep neural networks for damage classification and condition assessment. Details of the framework that may guide the automation process are explained. We demonstrated preliminary application of using UAV and deep neural network in concrete crack and asphalt pavement distress classification. Challenges and needs for deployment of UAV and deep learning are briefly discussed in the end.

Many transportation agencies lack sufficient funds to maintain and repair roads, which results into increased pavement maintenance cost. Pavement Management System (PMS) has demonstrated to be an essential tool for proper management of infrastructure and proper utilization of available funds. The University of Tennessee at Chattanooga utilized Micropaver software as PMS tool to conduct a pavement management analysis of principal arterials in the City of Chattanooga. The study used the City of Chattanooga pavement database to create the current and future pavement conditions. Maintenance and repair (M&R) planning analysis was also performed in order to determine the most cost-effective treatment and suggest the optimum utilization of funds for the city. An analysis of five budget scenarios was conducted for a five-year plan using the critical pavement condition index (PCI) method (ASTM D6433). Results show that the backlog elimination budget would be the best scenario because it increases the pavement condition and eliminates the backlog of major maintenance and repairs over the five-year period. The unlimited budget seems though ideal, it does not improve pavement condition. Maintaining current condition and limited budget scenarios would increase both the backlog and the total cost of maintenance and repairs over the analysis period.

The study applied the Markov chain (MC) model that uses a transition matrix to transmit the probability of monitored pavement markings being in one service life state then changing into another service life state over a time interval. The service life prediction by MC models were then compared with those from linear models, testing if there were any clear advantages of using one model over the other in terms of predicting longevity of the marking retroreflectivity. The retroreflectivity data were collected by monitoring the coefficient of dry retroreflective luminance for 2 years using a handheld retroreflectometer. Using the MC model, the study found that the pavement marking retroreflectivity (PMR) degradation follows an exponential curve trend whereby the degradation rates decrease as the time increases. Significant differences were found in the deterioration of the markings based on the colors (white or yellow) and line type (center, lane line, or edge line). White thermoplastic edge lines on two-lane roadways were found to have a better performance (low deterioration rates) compared with the same lines on four-lane highways. Based on the transition probability matrix (TPM), it was observed that retroreflectivity is in an excellent or good state for a short period of time(54% probability) but is in a fair or poor state for a longer time (92% probability), suggesting the trend has a higher degradation rate at the beginning and a lower rate near the failure state. Keeping the minimum failure states at 150 and 100 mcd/m(2)/lx for white and yellow markings, respectively, the service life of white markings was found to be approximately 4 years (49.5 months) and it was found to be about 2.4 years (29 months) for yellow markings. The MC model findings were compared with those obtained through linear regression, which showed that white thermoplastic pavement markings take approximately 3.5 years (42 months) to deteriorate to failure state level, while yellow thermoplastics take about 2.1 years (25 months). The study concluded that there is a clear difference between the prediction using MC models compared with linear models, with MC models being more cost effective in terms of maintenance and replacement scheduling due to a longer life prediction. (C) 2018 American Society of Civil Engineers.

Open graded friction course (OGFC) is a thin surface layer on pavements constructed with an open gradation asphalt mixture comprising of mostly coarse aggregate with very little fines to ensure a higher air voids content. This layer improves road users' safety in wet conditions. The University of Tennessee conducted a survey to states Departments of Transportation (DOT's) to gain knowledge on the usage, benefits, and challenges of OGFC. This paper presents the results of the survey that was sent to 52 states including District of Columbia and Puerto-Rico. Forty states (77%) responded to the survey. The responses indicate that 45% of the respondents still use OGFC, 42% used in the past they are no longer using it and 13% never used OGFC. The use is more concentrated in the Southern states than the Northern states of the US.