Bio-based Renewable Additives for Anti-icing Applications (Phase I)

The performance and impacts of several bio-based anti-icers along with a traditional chloride-based anti-icer (salt brine) were evaluated. A statistical design of experiments (uniform design) was employed for developing anti-icing liquids consisting of cost-competitive chemicals such as bio-based compounds (e.g., sugar beet extract and dandelion extract), rock salt, sodium metasilicate, and sodium formate. The following experimentally obtained parameters were examined as a function of the formulation design: ice-melting capacity and ice penetration at 25°F (−3.9°C) and 15°F (−9.4°C), compressive strength of Portland cement mortar samples after 10 freezethaw/deicer cycles, corrosion rate of C1010 carbon steel after 24-hour immersion, and impact on asphalt binder’s stiffness. One viable formula (“best performer”) was tested for freezing point depression phase diagram (ASTM D1177-88) and the friction coefficient of asphalt pavement treated by this anti-icing formulation (vs. 23 wt.% NaCl) at a certain temperature near 25°F or 30°F after being applied at 30 gallons per lane mile (1 hour after simulated trafficking and plowing). Laboratory data shed light on the selection and formulation of innovative bio-based snow and ice control chemicals that can significantly reduce the costs of winter maintenance operations. This exploratory investigation contributes to more systematic study of optimizing “greener” anti-icers using renewable resources

Climate change and transport infrastructures: State of the art

Transport infrastructures are lifelines: They provide transportation of people and goods, in ordinary and emergency conditions, thus they should be resilient to increasing natural disasters and hazards. This work presents several technologies adopted around the world to adapt and defend transport infrastructures against effects of climate change. Three main climate change challenges have been examined: Air temperatures variability and extremization, water bombs, and sea level rise. For each type of the examined phenomena the paper presents engineered, and architectural solutions adopted to prevent disasters and protect citizens. In all cases, the countermeasures require deeper prediction of weather and climate conditions during the service life of the infrastructure. The experience gained supports the fact that strategies adopted or designed to contrast the effects of climate change on transport infrastructures pursue three main goals: To prevent the damages, protect the structures, and monitor and communicate to users the current conditions. Indeed, the analyses show that the ongoing climate change will increase its impact on transport infrastructures, exposing people to unacceptable risks. Therefore, prevention and protection measures shall be adopted more frequently in the interest of collective safety

TRA-901: OPTIMIZING THE LOCATION OF ROAD WEATHER INFORMATION SYSTEMS (RWIS) STATIONS – A SAMPLING DESIGN OPTIMIZATION APPROACH

This study presents an innovative approach to the design of a road weather information monitoring system (RWIS) that optimally combines spatial data on weather-related road surface conditions with data on traffic volume over a state-wide road network. The optimization method minimizes the spatially averaged ordinary kriging variance of hazardous road surface condition (HRSC) frequencies. Since it is desired that an RWIS should also be located at high traffic demand areas, road class data is implemented in the optimization process. Spatial simulated annealing (SSA) is used to search for the optimal RWIS network design by iteratively examining each possible location and accepting designs that ameliorate a weighted sum of average kriging variance and road class detection capability. This novel approach is applied in the optimization of Minnesota RWIS network to illustrate the distinct features of the proposed method, assess the effectiveness of the current location setting, and recommend new additional stations locations. The findings of the study suggest that the method introduced in this study is useful for determining the optimal RWIS station locations and placing a few in addition to the existing stations by incorporating key elements being considered in practice

Synthesis of Technical Requirements and Considerations for Automated Snowplow Route Optimization: Final Report

DOTs and other transportation agencies are increasingly using automated methods for snowplow route optimization, which have been demonstrated to produce significant savings when they result in the implementation of new routes. However, many route optimization projects have fallen short of implementation due to technical/operational issues with the routes produced or institutional barriers to change. These shortcomings can be substantially mitigated with improvements to the process of soliciting, selecting, and managing the route optimization software or service provider. This project’s objective was to provide DOTs with the tools needed to make these improvements. The key lessons from this project are provided in two complementary documents: a Decision Support Guidance document and a Contracting Language Template. The Decision Support Guidance provides DOT staff with an accessible and in-depth discussion of the technical requirements for route optimization and the key decisions DOTs should consider when developing the project scope and managing a provider. The Contracting Language Template provides DOTs with a flexible template to assist with the development of a scope of work for a Request for Proposals (RFP) for automated snowplow route optimization services. The language suggested in the Contracting document is intended to ensure that DOTs and service providers have a shared understanding of the scope of work that the DOT requires and to maximize the likelihood that the project will result in safe, feasible, implementation-ready routes

Automated Measurement of Heavy Equipment Greenhouse Gas Emission: The case of Road/Bridge Construction and Maintenance

Road/bridge construction and maintenance projects are major contributors to greenhouse gas (GHG) emissions such as carbon dioxide (CO2), mainly due to extensive use of heavy-duty diesel construction equipment and large-scale earthworks and earthmoving operations. Heavy equipment is a costly resource and its underutilization could result in significant budget overruns. A practical way to cut emissions is to reduce the time equipment spends doing non-value-added activities and/or idling. Recent research into the monitoring of automated equipment using sensors and Internet-of-Things (IoT) frameworks have leveraged machine learning algorithms to predict the behavior of tracked entities. In this project, end-to-end deep learning models were developed that can learn to accurately classify the activities of construction equipment based on vibration patterns picked up by accelerometers attached to the equipment. Data was collected from two types of real-world construction equipment, both used extensively in road/bridge construction and maintenance projects: excavators and vibratory rollers. The validation accuracies of the developed models were tested of three different deep learning models: a baseline convolutional neural network (CNN); a hybrid convolutional and recurrent long shortterm memory neural network (LSTM); and a temporal convolutional network (TCN). Results indicated that the TCN model had the best performance, the LSTM model had the second-best performance, and the CNN model had the worst performance. The TCN model had over 83% validation accuracy in recognizing activities. Using deep learning methodologies can significantly increase emission estimation accuracy for heavy equipment and help decision-makers to reliably evaluate the environmental impact of heavy civil and infrastructure projects. Reducing the carbon footprint and fuel use of heavy equipment in road/bridge projects have direct and indirect impacts on health and the economy. Public infrastructure projects can leverage the proposed system to reduce the environmental cost of infrastructure project

Estimating the Application Rate of Liquid Chloride Products Based on Residual Salt Concentration on Pavement

This technical report summarizes the results of laboratory testing on asphalt and concrete pavement. A known quantity of salt brine was applied as an anti-icer, followed by snow application, traffic simulation, and mechanical snow removal via simulated plowing. Using a sample from this plowed snow, researchers measured the chloride concentration to determine the amount of salt brine (as chloride) that remained on the pavement surface. Under the investigated scenarios, the asphalt samples showed higher concentrations of chloride in the plowed-off snow, and therefore lower concentrations of chlorides remaining on the pavement surface. In comparison, the concrete samples had much lower chloride concentrations in the plowed-off snow, and much higher chloride concentrations remaining on the pavement surface. An interesting pattern revealed by the testing was the variation in the percentage of residual chloride on the pavement surface with changes in temperature. When pavement type was not considered, more residual chloride was present at warmer temperatures and less residual chloride was present at colder temperatures. This observation warrants additional testing to determine if the pattern is in fact a statistically valid trend. The findings from the study will help winter maintenance agencies reduce salt usage while meeting the defined Level of Service. In addition, findings will contribute to environmentally sustainable policies and reduce the level of salt usage (from snow- and ice-control products) introduced into the environment

Identifying Best Practices for Snowplow Route Optimization

Well-designed winter maintenance routes result in snow and ice control service that is both more effective, because roads are cleared more rapidly, and more cost efficient, because deadheading, route overlap and other inefficiencies are reduced or eliminated. There are an increasing number of computerized tools to facilitate the routing process, but these tools are not yet widely used by winter maintenance practitioners. The purpose of this report is to provide practitioners with an overview of computerized route optimization processes and concrete recommendations about how to ensure that route improvement efforts produce actionable results. Recommendations are synthesized from nine recent and ongoing snowplow routing projects using a variety of computerized routing tools. Project descriptions, based on interviews with project personnel, focus on project goals, optimization software features used, and lessons learned. Multiple route optimization projects report route length reductions on the order of 5% to 10%, with reductions as high as 50% reported in one case. These snowplow route optimization projects show that route optimization is a powerful tool for improving routing efficiency but that it does not replace the need for expert judgment in the route design process. Successful route optimization projects rely on close cooperation between experienced winter maintenance professionals and the individuals conducting the route optimization as well as a highly accurate, snowplow-routing specific representation of the road network. Successful projects also include time to review and revise new routes to identify potential problem spots prior to implementation

Optimizing Anti-icing Operation for Winter Roadway Treatment Using A Decision-Making Tool

Managing winter roadway treatment can be a challenge where winter is not severe, but snowfall is experienced a few times a year. Winter weather makes the road dangerous and challenging to travel. Most US states have approached and implemented different winter road maintenance practices to make transportation of goods, services, and people uninterrupted. However, the state of Georgia has always struggled to deal with winter weather. Recently, there has been some progress. The Georgia Department of Transportation prepared a winter road treatment plan in 2019, and they are still working on improving it. Increasing emphasis on pre-treating the road rather than relying heavily on snow plowing and other post-treatment is the current trend in winter road maintenance. Pre-treatment reduces chemical use and has several other benefits. In this research, a pre-treatment requirement model was developed to calculate the amount of brine required to melt different snow and ice amounts. In the last three years, Georgia faced a few snow events; three were selected for analysis using the developed model. The study revealed that adjusting the pre-treatment amount at smaller snow events can eliminate the need for post-treatment. The model suggests that different parts of a route require different amounts of pre-treatment. The application of the brine amount can be adjusted based on snow accumulation prediction by the model. The model sensitivity analysis showed that more snow is accumulated at lower temperatures, and the effectiveness of brine in melting snow diminishes. Higher wind speed increases snowmelt resulting in lowered brine application requirements. The decision-making tool can optimize the amount of brine used by suggesting location and pre-treatment amount. The output of the model can be used in better decision making on winter road pre-treatment