Smart Compaction for Infrastructure Materials

69A3551847103Compaction is a process of rearranging material particles by various mechanical loadings to densify the materials and form a stable pavement structure. Current methods to assess the compaction quality rely heavily on engineers' experiences or post-compaction methods at selected spots. The experience-based method is prone to cause compaction problems and pavement distresses, particularly when new materials are implemented. Due to the complicated interactions between the compactors and materials, the compaction mechanism of the particulate materials is still unclear. This gap hinders the improvement of compaction quality and the development of intelligent construction. This project was undertaken to investigate the compaction mechanism of the infrastructure material from the mesoscale (particle scale) and develop an innovative compaction monitoring method that determines the compaction condition based on particle kinematics. With the development of sensing technologies, wireless particle-size sensors have become available in research and industry for monitoring particle behaviors during compaction. A wireless sensor, SmartRock, was applied in the project and collected the mesoscale behaviors during compaction. Several lab and field compaction projects were carried out using asphalt mixtures and granular materials, various compaction machines, and pavement structures. It was found that internal particle kinematic behavior is closely correlated to material densification during compaction. The lab and field compaction can be reasonably connected by the particle rotation, and similar three-stage compaction patterns were identified. Three machine learning models were built to predict the compaction condition and the density of the asphalt pavement both in the lab and in the field. The reasonable predictions confirm that the machine learning algorithm is appropriate for compaction prediction. The density results from the pavement cores further verify the applicability and robustness of the intelligent model for compaction prediction. Future studies are still needed to evaluate the model's robustness based on more mixture varieties and field applications

Smart Mobile Platform for Model Updating and Life Cycle Assessment of Bridges

69A3551847103Mobile sensing is an alternative paradigm that offers numerous advantages compared to the conventional stationary sensor networks. Mobile sensors have low setup costs, collect spatial information efficiently, and require no dedicated sensors to any particular structure. Most importantly, they can capture comprehensive spatial information using few sensors. The advantages of mobile sensing combined with the ubiquity of smartphones with the internet of things (IoT) connectivity have motivated researchers to think of cars+smart phones as large-scale sensor networks that can contribute to the health assessment of structures. Working with mobile sensors has several challenges. The signals collected within a vehicle\u2019s cabin are contaminated by the vehicle suspension dynamics; therefore, the extraction of bridge vibration from signals collected within a vehicle is not an easy task. Additionally, mobile sensors simultaneously measure vibration data in time while scanning over a large set of points in space, which creates a different data structure compared with fixed sensors. Since collected data are mixed in time and space, they contain spatial discontinuities. When these challenges are addressed, mobile sensing is a promising data resource enabling crowdsourcing and an opportunity to extract information about infrastructure conditions at an unprecedented rate and resolution. In this regard, deep learning-based frameworks have been developed in this project to (a) resolve the dynamic behavior of a vehicle by estimating input forces to which it is subjected from responses acquired from within a vehicle and (b) learn underlying partial differential equations governing the underlying dynamics of a system from recorded data

Bridge Load Rating and Evaluation Using Digital Image Measurements

9A3551847103The use of digital imaging and other vision-based measurements offers many options for conducting load tests and obtaining the necessary data without having direct contact on the bridge via mounted sensor arrays. The results from this study revealed the sensitivity and accuracy of displacement measurements when compared to measurements from conventional string potentiometers mounted directly to the flange of the girder and 3D point cloud measurements. While there is not one optimal technique for measuring structural displacements based on a video, different techniques have different performance levels, and the applicability of these various methods may vary from case to case. The goal of this study was to compare the performance and accuracy of vision-based displacement measurements in the form of load testing on two bridges in Delaware, and how the data can be used to calibrate finite element models to assess bridge performance based on in-situ conditions. Results from the two diagnostic loads tests are presented. Using the data collected, results are compared to AASHTO live load distribution factors. A finite element bridge model is generated in ABAQUS and calibrated using the field measurements obtained from digital imaging. From the findings, a process to evaluate live load distribution using displacements obtained from vision-based measurement techniques is presented. Lessons learned and the impact of the vision-based measurement techniques deployed for load testing and evaluation are also presented

Landslide Risk Assessment in Cut Locations Using Artificial Intelligence Based on Right-of-Way Videos and Geophysical Data

69A3551847103Sidehill and through cuts are often used in the construction of new railroad rights-of-way to limit the length, curvature, and grade of the route. However, rights-of-way that utilize cuts are susceptible to damage from falling debris driven by slope failure events such as shallow landslides and rockfalls. At-risk slopes, or geohazards, are traditionally analyzed using intensive field investigations and historical failure events to determine their likelihood of failure and the potential consequences of failure. Anticipating slope failures that may occur due to everyday weather events and other catalysts in the region helps protect railroad assets and employees, ensuring safe operations. Many rights-of-way have a large density of geohazards; thus, performing in-situ measurements to determine their failure likelihood requires extensive resources. In addition, installing infrastructure to detect or inhibit debris flow is expensive and often unrealistic for all geohazards. This study aimed to create a new slope stability risk framework for railroad cut sections by processing digital images of railroad rights-of-way recorded by inspection vehicles and related geophysical data. A geohazard-affected track section along the Harrisburg Line was used as the study area. Computer vision techniques were used to identify and quantify geohazard features that indicated slope instability. An object detection model based on deep learning (DL) was trained to detect these slope instability indicators and generate risk scores from rights-of-way inspection videos. Moreover, a landslide inventory was compiled, and a landslide susceptibility model was developed for the study area based on available geophysical data. The object detection model and the landslide susceptibility model were combined using a relative risk assessment framework to determine which sections were most at-risk of landslide, and results were compared with the railroad identified geohazard sections across the study area

Development of a Practical Risk Framework for Railway Bridge Stiffness Transitions [Project Summary]

69A3551847103This document is a technical summary of the CIAMTIS report, Development of a Practical Risk Framework for Railway Bridge Stiffness Transitions, funded as part of the US DOT Region 3 University Transportation Center

Credit Support and Infrastructure Investment: The Case of the Transportation Infrastructure Finance and Innovation Act (TIFIA) Program

69A3551847103Infrastructure project development in the United States frequently relies on debt financing. Project sponsors typically borrow money for project design and construction through private capital markets, but the Transportation Infrastructure Finance and Innovation Act (TIFIA) program of the U.S. Department of Transportation (USDOT) also provides a complementary public financing program for surface transportation infrastructure development projects. This research empirically evaluates whether and how the TIFIA program\u2019s creditworthiness profile, measured via credit ratings, changed between the Moving Ahead for Progress in the 21st Century (MAP-21) and Fixing America\u2019s Surface Transportation (FAST) Act periods. The findings provide meaningful insights, and follow-up research will be important

Calibration of WVDOH IRI-Based PSI and SCI Equations

69A3551847103The West Virginia Department of Transportation \u2013 Division of Highways (WVDOH) uses a condition assessment manual, which engages various indices combining broad categories of distresses for evaluating asphalt/concrete pavements. The indices assist WVDOH in planning pavement management strategies in a cost-effective and timely manner. Therefore, the reliability of the condition evaluation information is essential for WVDOH to develop credible pavement management strategies. However, it has been over 20 years since the manual\u2019s development in 1997. WVDOH observed a few issues with some of the indices, particularly the present serviceability index (PSI) and structural cracking index (SCI), as follows: (1) inconsistency between the smoothness acceptance limits expressed in international roughness index (IRI) and PSI values estimated from the IRI limits and (2) the current SCI equation heavily favoring lower cracking severities since the use of automated data collection vehicles. This project was conducted to calibrate the current WVDOH PSI and SCI equations to resolve these issues. Phase 1 of this project calibrated the current PSI equation by comparing the two sets of IRI values calculated from the golden-car parameters and the quarter-car parameters of model passenger vehicles found in the literature review. The current SCI equation was calibrated in Phase 2 by analyzing the historical alligator and longitudinal crack data collected from 1998 to 2021. The results of this project will provide WVDOH with the potential to handle the issues and an opportunity to enhance the state\u2019s pavement management practices. Also, the approaches used in this project can be applied to other transportation agencies with similar issues as cost-effective methods to calibrate outdated pavement condition indices

Durability Assessment of Externally Bonded Fiber-Reinforced Polymer (FRP) Composite Repairs in Bridges

CIAM-UTC-REG16Although carbon fiber-reinforced polymer (CFRP) composite has been extensively used to rehabilitate many deficient bridges, data warranting their long-term performance is lacking. Current durability testing of CFRP composite involves accelerated conditioning as a part of material specification requirements to ensure that they maintain mechanical and physical properties during service life. However, without field data, relating accelerated conditioning test data to real-time outdoor exposure is not reliable. Work conducted at the University of Delaware in the early 1990s resulted in the first full-scale application of externally bonded CFRP on publicly owned bridges in the United States. As such, these bridges offer a unique opportunity to study CFRP durability over a time span of well over two decades. This report provides information on the durability of CFRP composite installed on the Foulk Road concrete bridge in Wilmington, Delaware and Bridge 1-704 in Newark, Delaware. Field evaluation and laboratory testing of CFRP samples collected from several girders were employed to investigate CFRP degradation and bond quality. The results indicate that after more than two decades of service life, the condition of CFRP repairs in the Foulk Road bridge was found to have considerably deteriorated. The condition of CFRP in Bridge 1-704 was found to be functional and performing as expected; however, some evidence of deterioration was noted

Development of a Practical Risk Framework for Railway Bridge Stiffness Transitions

69A3551847103The objective of this research was to take advantage of historic measurement cycles to develop a risk index for bridge transitions that takes into account the stiffness differential (mean stiffness between zones), stiffness variation (variation around the mean), train axle load, train operating speed, rate of degradation of the transition zone, length of the bridge, and other factors. The data utilized to achieve this objective were vertical track deflection data and railway operating data for approximately 500 miles of railway with nearly 100 bridges. This research activity resulted in a framework for practically implementing a risk index for bridge transitions that allows the railway to prioritize bridges for maintenance and/or remedial action as well as monitor the health of bridge transitions. In addition, the resulting framework has the ability to identify the most cost-effective approach to managing the bridge transitions, status quo maintenance, implementation of a transition zone (and best approach), or matching stiffness through the bridge. Since the data utilized were very well known to the railway, and the resulting outcome is an easy-to-use and practical framework, it is expected that acceptance by industry partners will be timely and well received