Institute for Transportation

About the research

This project was intended to create an intelligent prognostics platform for lithium-ion (Li-ion) batteries, which would equip existing battery management systems with the capability to perform predictive maintenance/control for failure prevention. The platform developed in this project consisted of two modules:

• Deep feature learning, which automatically learns the features of (capacity) fade from large volumes of voltage and current measurement data during partial charge cycles and estimates the real-time state of health (SOH) of a battery cell in operation
• Ensemble prognostics, which leverage the current and past SOH estimates in Module 1 to achieve robust prediction of the cell’s remaining useful life

Robust prediction of remaining useful life was achieved by ensemble learning-based prognostics, which synthesized the generalization strengths of multiple prognostic algorithms to ensure high prediction accuracy for an expanded range of battery applications and their operating conditions. The two modules aimed to learn features of fade from partial charge data, assess real-time health of individual battery cells, and predict when and how the cells are likely to fail. A case study involving implantable-grade Li-ion cells was conducted to demonstrate a deep learning approach to online capacity estimation, developed for Module 1.

Funding Sources:
Iowa State University ($50,258.00)
Midwest Transportation Center
USDOT/OST-R ($50,000.00)
Total: $100,258.00

Contract Number: DTRT13-G-UTC37

About the research

Thanks to an array of advanced digital technologies, much of today’s transportation project data are available in digital format. However, due to the fragmented nature of the highway project delivery process, the growing amount of digital data is being archived and managed separately. This makes it difficult for professionals to take full advantage of the efficiencies of digitized data and information. The purpose of this research was to identify current data workflows and areas for improvement for five of the most common types of highway assets—signs, guardrails, culverts, pavements, and bridges—and offer guidance to practitioners on how to better collect, manage, and exchange asset data.

The research team conducted focus group discussions and interviews with highway professionals to capture their knowledge and practices about the data workflows. In addition, the team conducted an extensive review of the literature, manuals, project documents, and software applications regarding the exchanged information. For each type of asset, an information delivery manual (IDM) was developed. Each IDM consists of several process maps (PMs) and one exchange requirement (ER) matrix. A total of 15 PMs and 5 ER matrices were developed.

A set of limitations in current data workflows was identified and a set of recommendations to overcome those limitations was also determined and documented. The conclusion was that current data workflows were designed mostly for contract administration purposes. Thus, more efficient asset-centric data workflows need to be implemented to truly streamline the data workflows throughout an asset’s life cycle and minimize wasted resources in recreating data in the asset maintenance stage.

Funding Sources:
Iowa Department of Transportation
Iowa Highway Research Board ($50,000.00)
Iowa State University ($22,500.00)
Midwest Transportation Center
USDOT/OST-R ($80,000.00)
Total: $152,500.00

Contract Number: DTRT13-G-UTC37

About the research

The relationship between speed and safety continues to be a high-priority research topic as numerous states consider speed limit increases. This study leveraged data from the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) to examine various aspects of driver behavior, including speed limit selection and engagement with in-vehicle distractions, as well as the impacts of these behaviors on crash risk while controlling for the effects of traffic, geometric, and environmental conditions. High-resolution time-series data were analyzed to examine how drivers adapt their speed on roadways with different posted limits, in speed limit transition areas where increases or decreases occur, as well as along horizontal curves, both with and without posted advisory speeds.

The research also involved an investigation of the circumstances under which driver distraction is most prevalent. The factors associated with crash and near-crash events were compared with similar data from normal, baseline driving events across various scenarios to improve understanding of the nature of the precipitating events. Driver responses, including reaction times and deceleration rates, were examined during the course of crash and near-crash events to determine how driver response varied across various scenarios.

Ultimately, this research provided important insights as to how drivers adapt their behavior and how these behaviors, in turn, influence the likelihood of being crash involved.

Funding Sources:
Midwest Transportation Center
USDOT/OST-R ($50,000.00)
Total: $50,000.00

Contract Number: DTRT13-G-UTC37

About the research

The primary objective of this research project was to broadly investigate potential applications of expanded maintenance data (from automated vehicle location [AVL] and roadway image capture technology installed on snowplows) and traffic data (from crowdsourced INRIX probe vehicles) in Iowa throughout multiple winter weather events, with an emphasis on conditions before, during, and after crash events. Other datasets were explored and integrated for demonstration purposes, including data from existing fixed-location cameras and traffic sensors, roadway weather information systems (RWIS) data, roadway characteristics data, and weather and maintenance crew-based operations reports.

A benefit of analyzing crash experience over multiple events is that possible trends may be identified. Overall, this project promotes the use of extensive, rich datasets to investigate weather-related impacts on mobility and safety and evaluate possible opportunities for improving winter maintenance operations. The Iowa DOT may use these data resources to supplement existing efforts to monitor traffic, weather, and surface conditions and direct their corresponding actions and reactions.

Funding Sources:
Iowa Department of Transportation ($30,000.00)
Midwest Transportation Center
USDOT/OST-R ($29,883.00)
Total: $59,883.00

Contract Number: DTRT13-G-UTC37

About the research

Due to their lower application temperature, lower energy consumption, and lower viscosity, asphalt emulsions are gaining popularity in the United States and worldwide. About 3 million tons of asphalt emulsion are produced in the US, which accounts for 5% to 10% of the total asphalt consumption. However, there is still a lack of understanding of the mechanisms by which asphalt emulsions are produced and how they work.

This research project reviews current literature available on asphalt emulsions to determine the present state of the practice, documents the development of an asphalt laboratory at Iowa State University, reports asphalt emulsion results, and creates an action plan for future work.

Two standard emulsions, one non-modified and one polymer-modified, were produced in the laboratory using a state-of-the-art laboratory-scale asphalt emulsion mill, emulsion properties were compared and experience was gained. To determine standards by which to compare emulsions to the performance grade (PG) system of binder classification, master curves of elastic modulus (G*) were plotted between emulsion residue and their base binders. The elastic recovery of polymer-modified asphalt emulsion residue was also tested to compare its performance with polymer-modified binders. Finally, sweep tests were conducted on laboratory-prepared chip seal samples to compare the aggregate mass lost between samples prepared with asphalt emulsion and hot applied asphalt.

Funding Sources:
Iowa Department of Transportation
Iowa Highway Research Board ($45,000.00)
Midwest Transportation Center
USDOT/OST-R
Total: $45,000.00

Contract Number: DTRT13-G-UTC37

About the research

For this project, the researchers developed a new structural health monitoring–facilitated condition-based management (SHM-CBM) maintenance prioritization system. This system represents an important step toward more widely integrating SHM into practice.

The kernel of the proposed SHM-CBM system is establishing a ranking index for each bridge in a particular inventory that establishes a maintenance funding priority for each bridge. A higher ranking index value indicates a lower maintenance funding priority. The ranking index is computed using both National Bridge Inventory (NBI) and SHM data, as well as user inputs.

A case study of the I-80 Sugar Creek Bridge showed that replacement could be postponed by up to 37 years using SHM-CBM because the condition of the bridge was determined to be better than what was previously assumed. This potential extension of service life in combination with expected maintenance, repair, and monitoring costs were used in a cost-benefit analysis that showed SHM system implementation is financially justifiable.

The SHM-CBM approach has the following advantages over current decision-making approaches:

• Continuous and near-real-time SHM data are used in decision making
• Wide range of quantitative data can be gathered using SHM (e.g., strain and temperature, chloride infiltration, tilt, and corrosion)
• Reduced uncertainty regarding structural performance
• Elimination or reduction of over-maintenance and deterioration or failure due to a lack of information about a bridge’s true condition

Funding Sources:
Iowa Department of Transportation ($63,195.00)
Midwest Transportation Center
USDOT/OST-R ($66,002.00)
Total: $129,197.00

Contract Number: DTRT13-G-UTC37

About the research

Maintaining optimal mobility on high-volume arterial traffic corridors is important to transportation agencies and the public. Corridor performance often can be enhanced by updating traffic signal timing, but most agencies find it necessary to prioritize their retiming efforts based on resource constraints. To facilitate prioritization, a set of arterial corridor performance measures was developed using INRIX probe vehicle data. These commercially available data are derived from in-vehicle global positioning system (GPS) observations transmitted wirelessly, eliminating the need for supplemental traffic observation infrastructure to be installed in the field.

The main objective of this study was to present a methodology to compare arterial corridors in terms of mobility-based performance measures. This process can help agencies select the corridors that are in need of signal retiming and can help identify corridors suited for adaptive signal control implementation. The two-step methodology began by identifying the number of days in a year with abnormal traffic patterns and comparing the volume-normalized performance of the remaining segments to identify corridors that are problematic on normal days.

The proposed methodology was applied to 12 corridors in Des Moines, Iowa, and 1 in Omaha, Nebraska. Three corridors were found to have a high number of anomalous days. Among the remaining corridors, three were identified as under-performing on normal days. In addition, the impact of implementing an adaptive signal control system on one corridor (University Avenue) was evaluated, where small improvements in travel rate and daily variation were observed, but the overall variability increased.

Funding Sources:
Federal Highway Administration State Planning and Research Funding
Iowa Department of Transportation ($47,758.00)
Midwest Transportation Center
USDOT/OST-R ($46,040.00)
Total: $93,798.00

Contract Number: DTRT13-G-UTC37

About the research

More than 50% of roadways in Iowa are classified as unpaved. The performance and long-term sustainability of such roads are dependent on the quality of the surfacing material, which varies considerably by location. The large, unbound particles form an unstable road surface that becomes rough, developing potholes and corrugations as the “floating” material is scattered by vehicles or washed away by rain. As a result, such roads require more frequent maintenance and reconstruction, which becomes very expensive for Iowa counties. Therefore, it is important to construct unpaved roads with materials that can sustain their performance for a considerable amount of time with less maintenance. This problem can be addressed economically in locations that are close to sources of considerable amounts of biofuel co-products (BCPs).

Loess soil was mixed with four different biofuel co-products: lignosulfonate, glycerin bottoms, crude glycerine, and glycerin 95. The soil was mixed with 4, 8, 12, and 16% BCP by weight. Results of the study showed that lignosulfonate improved the unconfined compressive strength (UCS) of the loess soil to some extent, while such trends were not observed for the mixtures prepared with glycerin products. Leaching tests focused on the pH and leaching of metals such as Cr, Al, Fe, As, and Zn from soil mixtures. The addition of the BCP did not influence the pH of the loess soil, and none of the mixtures leached metals that were above the detection limit of the equipment. These results indicate that BCPs do not pose any environmental threat when used as a dust control or stabilizing agent in unpaved roads.

Funding Sources:
Iowa Department of Transportation
Iowa Highway Research Board ($40,000.00)
Midwest Transportation Center
USDOT/OST-R ($40,000.00)
Total: $80,000.00

Contract Number: DTRT13-G-UTC37

About the research

This study explores the gap between quantitative and qualitative assessment of engineering resilience in the domain of complex transportation infrastructure systems. A conceptual framework was developed for modeling engineering resilience, and then a Bayesian network was employed as a quantitative tool for the assessment and analysis of engineering resilience. A case study involving a transportation system for an aircraft manufacturing supply chain was employed to demonstrate the developed research and tools. The developed resilience quantification and analysis approach using Bayesian networks could empower system designers to have a better grasp of the weaknesses and strengths of their own systems against system disruptions induced by adverse failure events.

Funding Sources:
Midwest Transportation Center
USDOT/OST-R ($50,000.00)
Wichita State University ($50,000.00)
Total: $100,000.00

Contract Number: DTRT13-G-UTC37

About the research

The purpose of this research project was to develop visualization techniques and methods that display pavement performance data and information in a communication-friendly format for a variety of end users. Several possible scenarios of end users and possible questions that they might use visual analyses to answer were envisioned. The researchers believed that the two primary types of end users were county office administrators examining the status of the roads in their counties and the state and engineers working with the data sets. Accordingly, several visualizations are presented that would be useful for both of these end-user groups. For each, a short description is given along with a sample question that the visualizations could be used to answer. The data used to generate these interactive visuals were provided by the Iowa Department of Transportation (DOT) and outline numerous indicators of pavement performance for the year 2013. Software programs used to produce the visual analyses were Microsoft Excel 2010 and Tableau 9.0.

The visual analyses are interactive and are designed to provide a dashboard at the county level. They can also be modified to display information and visuals at other levels of county groupings.

To further develop visualizations in a clear and insightful way, the researchers generated interactive visuals using data provided by the Iowa DOT and covered the period from 2013 to 2015. This data set contains numerous indicators of pavement performance by road segment, county, and district. Tableau 10.3 was used conduct the visual analysis.

This work focused on identifying information about the different types of cracks, relationships between them, relationships between condition and distress data, and performance in Iowa districts and counties. Counties were divided into five county types based on their size and proximity to major cities, as described by U.S. Census data from 1970 to 2010. These five county types included central city metropolitan, outlying metropolitan, regional center, small urban, and rural.

Funding Sources:
Midwest Transportation Center
USDOT/OST-R ($42,536.00)
Total: $42,536.00

Contract Number: DTRT13-G-UTC37