Skip to main content Skip to navigation
Washington State University National Center for Transportation Infrastructure Durability & Life-Extension (TriDurLE)

Tazarv & Hart Awarded Research Fellowship

Dr. Mostafa Tazarv and student Kallan Hart (of South Dakota State University have been awarded the Dennis R. Mertz Bridge Research Fellowship for 2020-21 by PCI for their research fellowship proposal “Repairable Precast Bridge Bents for Extreme Events.” Congratulations! The researchers will be working on this project in collaboration with Gage Brothers Concrete Products and TriDurLE.

Mostafa Tazarv

Mostafa Tazarv

Kallan Hart

Kallan Hart

Developing Enhanced Performance Curves of ITD Asphalt Pavements by Mining the Historical Data


Project

Developing Enhanced Performance Curves of ITD Asphalt Pavements by Mining the Historical Data

Team

Dr. Xianming Shi, Washington State University

Description

The overarching goal of this project is to develop reliable and realistic and enhanced performance curves for ITD asphalt pavements by mining the historical data. To this end, the project has the following objectives: 1) identify the appropriate parameters and additional criteria to use in the enhanced asphalt performance curves; 2) develop and calibrate distress-specific models for forecasting future pavement conditions, for both new and rehabilitated asphalt pavements; 3) validate existing and enhanced curves using historical performance data.  This project fits into the National University Transportation Center (UTC) National Center for Transportation Infrastructure Durability & Life-Extension (TriDurLE) research thrust of “Asset management and performance management for enhanced durability and life-extension of transportation infrastructure”.

 

More Detail

Visuals

Dr. Xianming Shi.
Xianming Shi

Dr. Xianming Shi

Development of Multi-scale Self-healing High-volume Fly Ash UHPC


Project

Development of Multi-scale Self-healing High-volume Fly Ash UHPC

Team

Dr. Xianming Shi, Washington State University

Description

Despite its higher initial cost than conventional concrete, ultra-high performance concrete (UHPC) is gaining popularity in applications such as prefabricated connections, bridge decks, beams, girders, pile foundations, thin-wall shell structures, encasement of corroded steel girders, and encasement of substandard or corroded columns. The durability of UHPC is challenged once cracks are generated in the UHPC structural members. The high materials cost of UHPC also limits its wide application in pavement and other civil infrastructures. In this context, the overarching goal of this project is to design a cost-effective UHPC featuring the use of high volume fly ash (HVFA) binder and ability to heal the cracks when and where needed. The multiscale self-healing ability of the HVFA UHPC will be achieved through the combined use of microcapsules and light-weight aggregate (LWA), so as to maintain the superior durability of the UHPC. To this end, this exploratory laboratory investigation aims to:

1) design healing agents and multi-dimensional microcapsules and LWAs to heal cracks with different widths

2) identify cost-effective mix designs for UHPC with the use of HVFA binder

3) evaluate the self-healing effectiveness of dog-bone shape HVFA UHPC specimens, using the direct tensile test and digital image correlation technique.

The specific design of this novel UHPC is as follows. Different sizes of polymeric microcapsules (with inorganic healant) are obtained through a water-in-oil suspension polymerization technique and LWAs are encapsulated with a modified polymer coating. The healing agents incorporating a novel nano-material, graphene oxide (GO), are synthesized to improve the healing efficiency of cracked UHPC. More crystals are expected to form inside the cracks and provide comparable strength as the intact part, so that the tensile properties of cracked UHPC can be greatly recovered after self-healing.

More Detail

Visuals

Dr. Xianming Shi

Bayesian Network


Project

Multi-Level Resilience-Based Transportation Asset Management (TAM) Framework using Bayesian Network

Team

Ji Yun Lee, PI, Washington State University
Yue Li, PI, Case Western Reserve University
Xiong Yu, Co-PI, Case Western Reserve University

Description

This project proposes a multi-level resilience-based transportation asset management framework using Bayesian network. The framework is aimed at (a) measuring transportation network resilience at multiple management levels (e.g., project, network and enterprise levels), (b) tracking and quantifying uncertainties existing at every level so as to effectively manage uncertainties in assessing the overall network resilience, (c) determining the optimal combination of inspection/monitoring techniques based on Value of Information, and (d) providing the optimal allocation of budgets to multiple pre- and post-disaster resilience-enhancing strategies.

The project will provide decision-makers (e.g., state DOT risk managers, executives, and program and project managers) with several analytical models, including (a) component-level time-dependent reliability analysis and its updating procedure based on different types of inspection and monitoring techniques; (b) network analysis which can incorporate both the robustness of components and the adaptive capacity of a network; (c) Bayesian-network-based resilience assessment model that evaluates each resilience capacity at multiple transportation management system levels and quantifies uncertainty at every assessment stage; and (d) asset management strategies by disaggregating resilience into the three resilience capacities using backward simulation. The framework itself and such analytical models can be implemented in risk-/resilience-based transportation asset management. Moreover, this project can be extended to develop a Python interactive tool, which is designed to enable transportation agencies to understand how the research findings can be easily and successfully implemented in improving the resilience of their transportation system.

Impacts/Benefits

More Details

Visuals

Ji Yun Li, PI

Dr. Ji Yun Lee is an Assistant Professor in the Department of Civil and Environmental Engineering at Washington State University (WSU). Prior to joining WSU in 2017, Dr. Lee served as a Postdoctoral Scholar in the Department of Civil and Environmental Engineering at the University of California, Los Angeles (2016-2017) and a Visiting Faculty in the Department of Civil, Environmental and Construction Engineering at the of Central Florida (2015-2016). She received her Ph.D. (2015) in Civil Engineering from the Georgia Institute of Technology, her M.S. (2011) in Civil Engineering from Stanford University, and her B.S. (2009) in Architectural Engineering from Korea University. Her research experience and interests lie in the general field of structural reliability and risk assessment of civil infrastructure systems and supply chain network exposed to extreme events, risk-informed decision-making, and infrastructure/community resilience.

Design of Long-lasting Discrete Sacrificial Anode


Project

Design of Long-lasting Discrete Sacrificial Anode for Corrosion Mitigation of Reinforcement in Chloride Contaminated Concrete

Team

Dr. Xianming Shi, Washington State University

Description

Reinforcement corrosion induced by chloride contamination is a leading cause of structural damage and premature degradation in reinforced concrete (RC) structures, with significant implications for safety, reliability, economics, and environmental performance. Discrete sacrificial anode (DSA) is one tool used for corrosion mitigation of steel reinforcement in chloride contaminated concrete, particularly through embedment in repair mortar to reduce the detrimental “ring effect”. Our recent study revealed that the commercial DSA products actually have much shorter service life than expected, because zinc corrosion products accumulate at the interface between zinc core and the packaged mortar, reduce the current supply to steel reinforcement, crack the encased mortar, and finally lead to the complete failure of the DSA. In this context, the overarching goal of this project is to design long-lasting DSA to prolong its service life and reduce the costs associated with the need for frequent replacements. To achieve the goal, this study aims to:

1) design conductive and porous foamed cement paste as the encasing material for DSA, and
2) characterize the effects of different components of the paste on the life-cycle performance of newly-designed DSA and assess its effectiveness on the rehabilitation of salt-contaminated RC.

Specifically, carbon fibers will be incorporated into the foamed cement paste to increase its electrical conductivity. Light weight aggregates with water or saturated calcium hydroxide (Ca(OH)2) encapsulated inside will be used in the paste to maintain a sufficient level of moisture. Electrochemical tests will be conducted to study the corrosion performance of steel bars and zinc anodes as well as evaluate the effectiveness of DSAs. Scanning electron microscopy (SEM), Energy dispersive X-ray spectroscopy (EDX), and X-ray diffraction analysis (XRD) will be employed to investigate the mechanisms related to how the foamed microstructure and different components of the paste enhance the longevity and performance of DSAs.

More Detail

Visuals

Dr. Xianming Shi

Durability of Transverse Sawcut Joints in Mid-Western Jointed Concrete Pavements


Project

Durability of Transverse Sawcut Joints in Mid-Western Jointed Concrete Pavements

Team

Dr. Dan Zollinger, PI, Texas A&M University
Dr. Jenny Liu, Co-PI, Missouri University of Science & Technology

Description

This proposed project is comprised of an investigation into the role and extent that joint sealant effectiveness plays on the durability of sealed transverse sawcut joints in jointed concrete pavement that are subjected to deicing salts and freeze-thaw conditions. Specifically, this research will address the circumstances associated with the deterioration that occurs under the effect of oxychloride formation. This type of deterioration has been most prevalent in concrete pavements placed in the Midwestern parts of the US. This distress (Figure 1) is so extensive throughout Midwestern concrete pavements that it threatens the marketability of concrete pavements in the region. This proposed research will focus on aspects of the lesser-known distress in concrete (calcium oxychloride formation) that is caused by a chemical reaction between the chloride-based deicers and the calcium hydroxide (Ca(OH)2, (also denoted as CH). This reaction leads to the formation of calcium oxychloride, a deleterious reaction product that causes expansive pressures that damages a concrete pavement. As a consequence, the industry has a high level of interest in economic and effective solutions to prevent or minimize this distress type and has committed to in-kind contributions towards this research effort. Research has shown that some of the key factors in the incidence of calcium oxychloride formation are salt concentration and temperature which govern the threshold that must be exceeded in order to initiate the reaction. This research will seek to formulate a modeling approach to ascertain if the conditions in-situ warrant measures beyond the routine sealing of the joint to prevent damage from the formation of calcium oxychloride in the vicinity of a joint. The conditions will be characterized in terms of salt condition and temperature of the pore water in the concrete relative to the threshold or the activation energy for the reaction to occur.

More Detail

Visual

Dan Zollinger.

Dr. Dan Zollinger

Jenny Liu.

Dr. Jenny Liu

Automated Detection of Characterization of Cracks


Project

Automated Detection of Characterization of Cracks Using Structure-From-Motion Based Photogrammetry: A Feasibililty Study

Team

Dr. Xiong Zhang
Missouri University of Science & Technology

Description

In infrastructure such as pavement, bridges and tunnels, crack widths and patterns on surfaces are two of the most important signs used to estimate durability. Conventional techniques suffer from challenges such as tediousness, subjectivity, and high cost. A new measurement technique that overcomes these challenges while measuring crack displacement with high accuracy and low cost in aging structures is needed. The research will develop a Structure-from-Motion Based photogrammetry technique for measuring crack widths and patterns using videos taken by commercially available low cost digital cameras. Software will be developed to analyze the videos by combining deep-learning techniques and modern close-range photogrammetry. 3D models of the pavement and bridge structures with high accuracy will be constructed using the videos and will be compared and validated using the results generated from high accuracy LiDAR system. Post-processing algorithms will be developed to automatically calculate the real lengths as well as the real width and depth of a crack at any arbitrary locations. This method for 3D crack mapping will provide us a high accuracy, low cost, and easy-to-operate tool for pavement and bridge management.

More Detail

Visual

Condition Monitoring and Performance Management


Project

Analyzing the Impact of Autonomous Maintenance Technology to Transportation Infrastructure Capacity for Condition Monitoring and Performance Management

Team

Dr. Xianbiao (XB) Hu
Missouri University of Science & Technology

Description

The Autonomous Maintenance Technology (AMT) is a quickly emerging autonomous-vehicle-based technology for improving transportation infrastructure maintenance by removing drivers from risk. This project will develop models and algorithms to reveal its fundamental operating mechanism, and analyze its impact to transportation capacity for infrastructure condition monitoring and performance management. Newell car following model and moving-bottleneck-based traffic flow theory will be utilized to mathematically derive the roadway capacity under different scenarios. Multiple sensors, including high resolution Global Positioning System (GPS), Light Detection and Ranging (LiDAR), Radar, high definition camera, accelerometer and gyroscope installed on the AMT vehicles will collect real data from the field for model validations.

More Detail

Visual

XianBiao Hu

Dr. XB Hu is currently an Assistant Professor at Missouri University of Science and Technology. He received his Ph.D. from the University of Arizona in 2013 and was a founding team member and the Director of R&D at Metropia Inc. in Tucson AZ. His research focuses on smart transportation systems, transportation big data analytics, and traffic flow and system modeling.

Development of Holistic Methodologies for Improving Asphalt Mix Durability


Project

Development of Holistic Methodologies for Improving Asphalt Mix Durability

Team

Jenny Liu, PI, Missouri University of Science and Technology
Fujie Zhou, PI, Texas A&M University
Pedro Romero, PI, University of Utah

Description

Asphalt mix durability have always been major concerns of all State DOTs, and they cost taxpayers billions of dollars each year to repair cracking and rutting problems. To have a durable mix, one needs to address three aspects: durable mix design, production, and placement. The objective of this project is to develop holistic methodologies for addressing all three aspects with an ultimate goal to improve asphalt mix durability. A detailed literature review has been completed during the first stage of the Yr 1 research. By the end of Yr 2, as a minimum, this project will develop (1) a systematic methodology for designing durable mixes in the laboratory, (2) a performance-related methodology for production quality control and quality assurance (QC/QA) at asphalt plants, and (3) an innovative methodology for placement acceptance in the field.

All the methodologies and findings from this project will be summarized and documented in the final report. To facilitate implementation and transfer the technology coming out this project, the research team will reach out DOTs, contractors, and other stakeholders through publications, presentations at different conferences (such as TRB) and webinars.

Year One

More Detail
Visual

Year Two

More Detail
Visual

Jenny Liu.

Dr. Jenny Liu

Dr. Pedro Romero

 

Dr. Fujie Zhou