Institute for Transportation

About the research

This study investigated the structural performance of fully encased steel H-piles used in bridge foundations, with a specific focus on the effects of unbraced pile height resulting from scour conditions. While concrete encasement is widely used to protect piles, its structural contribution is often excluded from axial capacity calculations in current design practice. This research aimed to evaluate the effectiveness of fully encased pile bents in enhancing pile stability and to refine an existing assessment tool for more accurate capacity estimations.

The project combined full-scale laboratory testing and finite element (FE) modeling. Two major tests were performed: a three-pile fully encased bent subjected to service-level loads and a single encased pile tested to failure. Results showed increased stiffness, increased load-sharing behavior, and a significant gain in axial capacity due to encasement.

Validated FE models were used to conduct parametric studies assessing the effects of encasement length, pile length, and axis orientation. Findings confirmed that full encasement and weak-axis restraint significantly improve axial performance.

This research supports the inclusion of concrete encasement in pile capacity evaluations and provides engineers with a validated tool for more resilient and cost-effective bridge foundation design.

About the research

Rural intersections account for , representing a significant but poorly understood safety problem. objective of this study was to use second Strategic Highway Research Program Naturalistic Driving Study data and Roadway Information Database data to observe driver behavior at high-speed rural intersections. The overarching goal was to better understand how drivers react at rural intersections. The study’s researchers observed driver behavior firsthand by using video, vehicle and driver kinematic, and roadway data to determine how roadway, driver, environmental, and vehicle factors interact to affect driver safety at rural intersections. The research team conducted various analyses to examine driver behavior at different intersection types: two-way stop-controlled intersections, T-intersections, and all-way stop-controlled intersections. The analyses included the points at which drivers reacted to intersections, stopping behavior at intersections, and driver behavior surrounding safety-critical events. The models showed that several factors influence driver behavior at an intersection, including type of movement through the intersection, presence of a vehicle or vehicles on an opposing or major approach, presence of certain countermeasures, and vehicle speeds upstream of the intersection. The results can ultimately lead to better intersection design, more informed selection of traffic control devices, effective countermeasures, and targeted information to inform policy decisions.

About the research

As utility infrastructure increasingly occupies public roadway rights of way (ROWs), Iowa faces growing challenges in coordinating utility installations and managing space efficiently. The lack of standardized oversight across cities, counties, and the Iowa Department of Transportation (DOT) has led to inconsistent practices, disorganized utility placement, and costly delays in highway construction. This research aimed to develop a comprehensive utility management approach by identifying best practices for planning, documenting, and managing utility installations within public ROWs. Through surveys of current practices, gap analysis, and stakeholder engagement, the study produced a framework for improved utility coordination. Key recommendations include statewide standardization of policies, mandatory digital as-built documentation, designated utility corridors, enhanced permitting and oversight, and clear protocols for managing abandoned utilities. The research also resulted in the development of training programs and outreach efforts to support implementation. By adopting these strategies, Iowa can achieve more efficient, coordinated, and sustainable utility management, ultimately reducing project costs and preserving ROW integrity for future infrastructure needs.

About the research

Although young drivers log fewer miles than any age group except the elderly, the number of crashes and fatalities they experience is disproportionately high. Driver education is an important part of helping young drivers learn the basic skills of driving, but current requirements for driver education in Iowa do not take a comprehensive or data-driven approach to identifying and addressing novice needs.

The main goal of this research was to enhance Iowa’s driver education curriculum and ensure that it addresses both Iowa-specific and general safety issues to prepare novice drivers for a rapidly changing driving environment. Best practices in driver education and teaching trends in Iowa, in other states, and nationally were identified; a crash analysis was carried out; and driver education instructors and parents were surveyed to determine problem areas that could be addressed by the driver education curriculum. A series of informational videos was then developed to supplement driver education curriculums, and the research team drafted recommendations for changes to the Iowa driver education curriculum, the Iowa Code, and Iowa Administrative Rules.

About the research

Effective and timely bridge inspections are crucial for extending bridge lifespans and preventing catastrophic failures. Traditional inspection methods often involve manual visual assessments and can be time-consuming, labor-intensive, and prone to human error. Recent technological advancements in unmanned aerial vehicles (UAVs), artificial intelligence (AI), and machine learning (ML) offer promising solutions to these challenges. When high-quality images captured by UAVs are analyzed using AI and ML algorithms, structural defects can be detected and quantified with greater precision and efficiency than manual inspections.

The primary objective of this research was to enhance the accuracy and efficiency of structural inspections by integrating UAV technology for image capture and AI-based detection models for analysis. High-resolution images of bridge components were collected using UAVs operating at various distances and angles and were then processed through a custom-developed convolutional neural network (CNN) to detect critical defects such as cracking and spalling. The model’s performance was assessed through multiple case studies, and its ability to detect and quantify defects under different conditions was validated against field data. This approach yielded significant improvements over traditional bridge inspection methods in terms of the precision with which structural vulnerabilities were identified and accurately quantified defect dimensions.

Furthermore, the research incorporated the development of three-dimensional (3D) models of bridge structures using commercially available software to enable detailed structural assessments. High-resolution UAV imagery was successfully integrated into 3D modeling software to generate detailed models of bridge structures enabling comprehensive structural assessments and allowing for the quantification of detected defects. The results demonstrate the potential of UAV-based inspections combined with AI-powered detection models to revolutionize bridge inspection practices by offering a more reliable, efficient, and cost-effective approach to infrastructure maintenance and supporting more informed decision-making for infrastructure safety and longevity.

About the research

This project is a task order under the main Federal Highway Administration (FHWA)-sponsored project, “Infrastructure Research and Technology Deployment Program.”

The final report reviews state department of transportation (DOT) practices related to bridge load rating, posting, and permitting procedures, analyzing publicly available documents. It examines previous versions of the National Bridge Inspection Standards (NBIS) and the most recent 2022 update, alongside the American Association of State Highway and Transportation Officials (AASHTO) Manual for Bridge Evaluation (MBE) and its interim revisions. The review highlights both common and unique practices across states in assessing bridge load-carrying capacity, which is essential for preservation and public safety. Routine load ratings are typically based on design plans, field measurements, and inspection reports, while more advanced methods incorporate sophisticated analytical techniques like two-dimensional (2D) and three-dimensional (3D) finite element models.

In 2024, peer exchange meetings were held in Salt Lake City, UT, and Pittsburgh, PA, where participating transportation agencies discussed five key topics: state truck size and weight limits, consideration of deterioration in bridge load rating, timely re-rating and posting, structural analysis for permit loads, and research and technology. The final report summarizes the meetings and their contributions to advancing bridge load rating practices across the U.S.

About the research

The traditional method of tracking material deliveries to roadway and bridge construction sites has been for inspectors to collect paper tickets from haul truck operators. The Iowa Department of Transportation (DOT), however, is a national leader in the innovative alternative to this method: e-ticketing.

As defined by the Federal Highway Administration’s (FHWA’s) Every Day Counts, Round 6 (EDC-6) initiative, e-ticketing is the provision of “an electronic means to produce, transmit, and share materials data and track and verify materials deliveries.”

The digitization of this data collection and processing procedure has numerous benefits:

• Increased safety for job site construction inspectors through a reduction in their exposure to work zone vehicles
• Time savings through real-time access to data and reduced processing times
• Higher-quality project paperwork through more consistent and efficient project documentation
• Standardization of the data collected, allowing for easier access and analysis that might define future improvements and/or quantification of program impacts

About the research

The design of drilled shafts in Iowa currently relies on the American Association of State Highway and Transportation Officials (AASHTO) LRFD Bridge Design Specifications. To improve design efficiency at the state level, a series of research projects was conducted to develop the Drilled SHAft Foundation Testing (DSHAFT) database, a regional database facilitating the collection, storage, and efficient access of load test data from Iowa and other states, and to utilize the collected data to establish regional resistance factors that are reflective of the uncertainties associated with predicting drilled shaft capacity under Iowa’s specific geological conditions and construction practices. Resistance factors established in a 2019 study for various resistance prediction methods generally showed improvements over those recommended by AASHTO.

The present research aimed to validate the proposed resistance factors and formulate design recommendations for implementation. To this end, the DSHAFT database was further expanded with additional test data. Additionally, regression analyses were conducted on test data from Iowa to develop local resistance predictions that may provide more accurate estimates of drilled shaft capacity locally. Results from the analysis indicated that a linear correlation between soil parameters and measured unit side resistance was the best fit for most soil types. Moreover, settlement data were collected at several production shafts that were part of a few Iowa DOT bridge replacement projects to evaluate the field performance of drilled shafts designed under the current Iowa DOT guidelines. Various challenges were encountered during the data collection process. Some of the data indicated unexpected negative settlements, and further investigation is needed to develop appropriate conclusions. Design recommendations were formulated based on all findings, and design examples were developed to illustrate the application of the design recommendations.

About the research

Biodiesel is a sustainable fuel for use in any diesel engine and can be adopted in varying blends from 5% biodiesel (B5) to 100% biodiesel (B100). Blends up to B20 can be used in most diesel engines with minor or no modifications. For biodiesel blends greater than 20%, advanced technologies can be integrated into existing vehicles to ensure compatibility and successful performance. B5 and B20 are the most commonly used biodiesel blends, while B100 is less commonly used due to pricing, a lack of regulatory incentives, and performance concerns.

A number of agencies have expressed interest in evaluating the use of B100 in their maintenance vehicle fleets, but concerns with engine maintenance and performance have hampered adoption. As part of a pilot program, the Iowa Department of Transportation invested in B100 conversion kits for several new and existing snowplows in July 2020.

The objective of this research was to conduct an independent evaluation of the impact of adopting B100 biodiesel on the performance of the Iowa DOT snowplow fleet, including impacts on fuel economy, maintenance, carbon reduction, and driver/maintenance personnel concerns. The resulting information can be used to address concerns and assist agencies in decision-making regarding the adoption of biodiesel in their fleets.

About the research

The objectives of this study are as follows: (1) Provide communication and information sharing among member states. Discuss research needs and provide research ideas to be developed through TRB (and other research opportunities). (2) Provide a technology and knowledge exchange forum to enhance the practical knowledge of member states concerning asset management implementation. (3) Enhance the working knowledge of the asset management community.