Shear Wave Velocity Measurements

The Missouri Department of Transportation (MoDOT) commissioned SCI Engineering, Inc. to investigate cost-effective, non-intrusive geophysical methods for determining time-averaged shear wave velocity (Vs) profiles to a depth of 100 feet, based on anticipated updates to AASHTO seismic site classification specifications. Following a comprehensive literature review, eight candidate methods were identified. Of these, four methods—Active Multichannel Analysis of Surface Waves (Active MASW), Passive Multichannel Analysis of Surface Waves (Passive MASW), Active Refraction Microtremor (Active ReMi), and Passive Refraction Microtremor (Passive ReMi)—were selected for field testing based on their practicality and effectiveness.

Field evaluations were conducted at three sites: the SCI Office in O’Fallon, Illinois; the I-270 Chain of Rocks Bridge; and the MLK Connector in Illinois. The sites were selected based on access, availability of existing groundtruth subsurface soil information, as well as representing a variety of subsurface profiles.  Various geophone spacings and array configurations were tested. Performance metrics included depth of investigation, ease of deployment, data quality, and interpretability. Results demonstrated that combining Active and Passive MASW methods offered the most reliable and practical solution, providing consistent results, minimal operational complexity, and shared equipment and software requirements.

SCI Engineering developed a comprehensive User Manual and conducted field demonstrations to train MoDOT personnel in the acquisition, processing, and interpretation of MASW data. Adoption of these methods will streamline MoDOT’s seismic site classification processes, align practices with forthcoming AASHTO requirements, and enhance MoDOT’s internal technical capacity.

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Report number: cmr 25-009
Published: June 2025

Project number: TR202403

Authors: Evgeniy "Eugene" Torgashov, Neil Anderson, and Thomas J. Casey

Performing organization: SCI Engineering, Inc.

Testing Survey Methods for Detecting Bats Roosting in Bridges

Bats are a critical component of our natural world, and many species are at risk. Protecting roosting habitat is one way we can help conserve a variety of species. Although many bats use natural roosts, a growing number are adapting to anthropogenic structures due to habitat encroachment. In this study, we tested methods for detecting bats using bridges as roosts. We visited 20 bridges four times each to test six daytime methods (human visual and hearing, use of an acoustic detector, use of an agitator to induce bat vocalization, visual search with a spotlight, use of a thermal camera, and a borescope) and three evening emergence methods (human visual, thermal camera, and acoustic detector). Occupancy modeling revealed that the most effective way to document bat use at bridges is with an acoustic detector during evening emergence. This was followed by the use of thermal cameras during evening emergence, and the third best model was use of thermal cameras during the day. Surveying longer did not increase detectability in any of the top models. Based on our findings and suggestions in guidance documents for detecting bats in bridges, the first step is to survey a bridge with a spotlight, listening for bat vocalizations, and noting smell. If bats are not detected during the day, using acoustic detectors and thermal cameras during emergence will determine if bats are using bridges and can provide additional data if they are documented using them during the day.

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Report number: cmr 25-008
Published: June 2025

Project number: TR202420

Authors: Piper L. Roby, Crystal Birdsall, and Timothy Divoll

Performing organization: Copperhead Environmental Consulting, Inc.

Audible Alert and TMA Lighting

Truck Mounted Attenuators (TMAs) are designed to mitigate crash severity. Currently, TMA drivers rely on visual checks via driving mirrors to manually trigger warnings thus placing the duty on drivers. To address this limitation, the Automated TMA Warning System (AutoTMA) replaces or augments manual driver interventions with an AI-enabled, sensor-fused platform. By integrating high-definition cameras, LiDAR, and radar with GPU-accelerated multi-task learning, AutoTMA continuously detects and classifies oncoming vehicles, segments lane and drivable areas, and calculates dynamic distance thresholds—safe, warning, and danger—in real time. Validation of the AutoTMA included comprehensive trials within a Unity 3D simulation environment and test-track deployments on Missouri Department of Transportation (MoDOT) TMAs. Through iterative refinements, the system’s response latency has been reduced from three seconds to 0.25 seconds, substantially improving both visual and audible alert accuracy. AutoTMA’s modular architecture and robust sensor calibration mechanisms ensure rapid component replacement and resilience in variable operational conditions. Drawing on insights from prior research, including National Cooperative Highway Research Program (NCHRP) 05 24, the system optimizes lighting and audio cues while integrating adaptive safety zone parameters to overcome the limitations of fixed configurations. Preliminary findings confirm AutoTMA’s ability to detect imminent collisions and deliver timely, context-sensitive warnings—significantly enhancing driver awareness and reducing the probability of TMA-involved crashes. AutoTMA marks a transformative shift in work zone safety protocols, offering a viable pathway for nationwide adoption. Future work will focus on expanding sensor modalities, further refining AI models to boost accuracy, and broadening field trials across diverse environments. By bridging the gap between manual vigilance and automated safety, the AutoTMA system not only improves operational workflows but also holds the promise of shaping policy and accelerating the integration of proactive safety technologies in transportation.

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Report number: cmr 25-007
Published: June 2025

Project number: TR202309

Authors: Yaw Adu-Gyamfi, Carlos Sun, Mark Amo-Boateng, Gahan Gandi, and Neema Jakisa Owor 

Performing organization: University of Missouri-Columbia/Missouri Center for Transportation Innovation

Evaluation of Stripping Tests for Asphalt Mixtures to Replace AASHTO T283 Method in Missouri

Currently, many paving agencies in the U.S. including the Missouri Department of Transportation (MoDOT) use the AASHTO T¬-283 method (Tensile Strength Ratio (TSR) test) to determine the moisture damage susceptibility of asphalt mixtures. However, the TSR test has been shown to have a poor correlation with field results based on a review of literature and based on observations reported by MoDOT. In addition, the TSR test is time consuming and may be redundant in light of current requirements to conduct the Hamburg Wheel Tracking Test (HWTT) as part of balanced mix design. As a result, further research on these test methods was conducted. For this research five asphalt mixtures were investigated. The mixtures were subjected to the TSR test and HWTT. The Stripping Inflection Point (SIP) parameter was computed from HWTT using the Iowa method. The SIP parameter was found to be superior to the TSR test in correlating to field performance. Comparison of the RT-Index results with the SIP parameter suggested that the RT index is likely a weak indicator of moisture damage in asphalt mixtures. Based on the results obtained in this limited study, a framework was proposed to replace the TSR method. The framework is as follows; first, the mixtures are screened for rut depths lower than 4.0 mm at 20,000 passes in the Hamburg test. If the mixture exhibits low rut depths in the Hamburg test (less than or equal to 4 mm), it is highly likely that it is resistant to moisture damage and therefore judged as non-stripping. Second, if the rut depth is greater than 4.0 mm then the slope ratio is computed. If the slope ratio is found to be less than 2.0, then the mixture can be categorized as non-stripping. Finally, if the slope ratio is greater than or equal to 2.0, then the SIP is determined. A minimum threshold of 15,000 passes was chosen as the SIP threshold for initial implementation of the framework. Mixtures possessing SIP values less than 15,000 are scored as failing the stripping requirement.

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Report number: cmr 25-006
Published: May 2025

Project number: TR202306

Authors: William Buttlar, Punyaslok Rath, Jim Meister, and Katie Distelrath 

Performing organization: University of Missouri-Columbia/Missouri Center for Transportation Innovation

Asset Characterization Using Automated Methods

Increasing and intensifying flood events pose serious challenges to highway agencies in maintaining water crossing assets and assessing potential flood hazards. This project aimed to develop an automated flood risk assessment program for small water crossing assets, focusing on culverts with less than a 20-foot span not listed in the 2023 National Bridge Inventory (NBI). Information on such small assets is often limited and not readily available. Using a Geographic Information System (GIS), the project identified potential non-NBI water crossing sites and conducted field surveys to collect the asset information on Missouri's highways. Based on the field survey data, the project developed a GIS-based flood risk assessment tool for small water crossing assets, called the Missouri Automated Culvert Analysis Tool (MoACAT) QGIS Plugin, using publicly available high-resolution Light Detection and Ranging (LiDAR) data. The tool was built with Python scripts that process four sequential steps on the Quantum Geographic Information System (QGIS), the most popular non-proprietary open-source GIS software program. The project conducted a pilot study of culverts in Cass County, Missouri. The plugin hydraulic results were compared against HEC-RAS 2D modeling results. The results indicate that the tool has the potential to identify small water crossing assets and predict flood overtopping efficiently.  

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Report number: cmr 25-005
Published: April 2025

Project number: TR202311

Authors: Sungyop Kim, Donald Baker, Jejung Lee, Aaron Sprague, and Charles Mwaipopo

Performing organization: University of Missouri-Kansas City and Water Resources Solutions, Inc.

TMA Truck Safety

This study evaluates the effectiveness of in-vehicle safety countermeasures in reducing injury risk for TMA (Truck-Mounted Attenuator) truck occupants during collisions. With increasing incidents involving TMAs in work zones, understanding the protective impact of advanced safety features has become crucial. A review of historical TMA crash reports revealed that rear-end collisions are the primary issue, with whiplash injuries being the most common type of injury among drivers. Current in-vehicle safety countermeasures were examined, including active headrests, reactive seatbacks, and anti-whiplash systems, which were tested across six simulated collision scenarios incorporating varying vehicle weights, speeds, and impact angles. Using a biomechanical simulation model and telematic data, results indicated that active headrests, particularly with 40 mm travel level, consistently reduced injury criteria values (NIC, Nij, Nkm), effectively lowering head and neck injury risks in both straight and angled collisions. In contrast, the reactive seatback and anti-whiplash systems demonstrated mixed efficacy, performing well in low-impact conditions but poorly in high-impact scenarios. Limited high-impact telematic data, particularly with 80,000-pound vehicles, highlight the need for further validation for high-impact collision scenarios. Findings suggest that integrating advanced head restraint systems could significantly enhance TMA truck driver safety.

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Report number: cmr 25-004
Published: March 2025

Project number: TR202316

Authors: Praveen Edara, Zhu Qing, Carlos Sun, Henry Brown, Trent Guess, and Johnathan Stokes 

Performing organization: University of Missouri-Columbia

Consultant Support for Intelligent Compaction and Paver-Mounted Thermal Profiling Projects in 2024-2025

The 2024 MoDOT IC-PMTP Annual Report highlights achievements and challenges in implementing Intelligent Compaction (IC) and Paver-Mounted Thermal Profiling (PMTP) technologies. Key updates included aligning data validation tools with updated PMTP specifications and addressing challenges such as contractor’s paving boundary data manipulation and data loss. Improved PMTP thermal segregation trends were noted, though IC coverage declined due to data loss issues. Feedback meetings emphasized the preconstruction Global Navigation Satellite System (GNSS) and cellular surveys, stricter paving boundary validation, and updated PMTP specifications. Training and support efforts in 2025 will address these challenges and changes, ensuring continued improvement in paving quality and IC/PMTP data reliability.

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Report number: cmr 25-003
Published: February 2025

Project number: TR202421

Authors: Dr. George K. Chang, Amanda Gilliland, and Dr. S. Subramanian

Performing organization: The Transtec Group, Inc.