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X-WR-CALNAME:National Center for Transportation Infrastructure Durability & Life-Extension (TriDurLE)
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DTSTART;TZID=America/Los_Angeles:20230926T130000
DTEND;TZID=America/Los_Angeles:20230926T140000
DTSTAMP:20260403T175047
CREATED:20230824T191744Z
LAST-MODIFIED:20230824T195652Z
UID:3505-1695733200-1695736800@tridurle.wsu.edu
SUMMARY:Quantifying and Reducing Uncertainty in Resilience Assessment of Transportation Networks Using Dynamic Bayesian Network
DESCRIPTION:Presentation\nQuantifying and Reducing Uncertainty in Resilience Assessment of Transportation Networks Using Dynamic Bayesian Network \nThe Nation’s transportation systems are complex and are some of the highest-valued and largest public assets in the United States. As a result of repeated natural hazards and their significant impact on transportation functionality and socio-economic health of communities\, transportation resilience has gained increasing attention in recent years. Previous studies on transportation resilience have heavily emphasized network functionality during and/or following a scenario hazard event by implicitly assuming that sufficient knowledge of structural capacity and environmental/service conditions is available at the time of an extreme event. However\, such assumptions often fail to consider uncertainties that arise when an extreme hazard event occurs in the future. Thus\, it is essential to quantify and reduce uncertainties to better prepare for extreme events and accurately assess transportation resilience. To this end\, this study proposes a dynamic Bayesian network-based resilience assessment model for a large-scale roadway network that can explicitly quantify uncertainties in all phases of the assessment and investigate the role of inspection and monitoring programs in uncertainty reduction. Specifically\, the significance of data reliability is investigated through a sensitivity analysis\, where various sets of data having different reliability are used in updating system resilience. To evaluate the effectiveness of the model\, a benchmark problem involving a highway network in South Carolina\, USA is utilized\, showcasing the systematic quantification and reduction of uncertainties in the proposed model. The benchmark problem result shows that incorporating monitoring and inspection data on important variables could improve the accuracy of predicting the seismic resilience of the network. It also suggests the need to consider equipment reliability when designing monitoring and inspection programs. With the recent development of a wide range of monitoring and inspection techniques\, including non-destructive testing\, health monitoring equipment\, satellite imagery\, LiDAR\, etc.\, these findings can be useful in assisting transportation managers in identifying necessary equipment reliability levels and prioritizing inspection and monitoring efforts. \nSpeaker\n\nJi Yun Lee\, PhD\nWashington State University \nDr. 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 University 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. \nRegister Here
URL:https://tridurle.wsu.edu/event/quantifying-and-reducing-uncertainty-in-resilience-assessment-of-transportation-networks-using-dynamic-bayesian-network/
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240126T140000
DTEND;TZID=America/Los_Angeles:20240126T150000
DTSTAMP:20260403T175047
CREATED:20240125T181312Z
LAST-MODIFIED:20240916T185839Z
UID:3603-1706277600-1706281200@tridurle.wsu.edu
SUMMARY:Seismic Performance of Corroded Precast Concrete Columns
DESCRIPTION:Presentation\nSeismic Performance of Corroded Precast Concrete Columns \nAccelerated Bridge Construction (ABC) results in bridges that are assembled using precast concrete elements built with better quality in significantly shorter time than cast-in-place (CIP) bridges. Despite these advantages\, ABC bridges are vulnerable to corrosion\, a phenomenon observed in CIP bridges as well. Corrosion adversely affects the lateral force and axial compression capacity of reinforced concrete columns\, posing a concern for the structural integrity of both bridge types. The columns tested in this study include: (i) a control specimen with no corrosion; (ii) a specimen exhibiting moderate corrosion (with a target 10% loss in mass of longitudinal steel reinforcement); and (iii) a specimen displaying severe corrosion (with a target 25% loss). The impressed current technique was used for accelerating corrosion of the specimens in the laboratory. A numerical model was developed to calibrate the physical experiments of the control and corroded specimens. The numerical model could predict the experimental drift ratio with satisfactory accuracy up to bar fracture. The objective of this study is to quantify the decrease in lateral force and drift ratio capacity observed in corroded reinforced concrete columns constructed through ABC methods under seismic loads. In this context the deterioration due to corrosion and subsequent earthquake damage can be considered as multiple hazards. The research findings emphasize a significant decline in the lateral displacement capacity of reinforced concrete columns subjected to moderate and severe corrosion. The numerical model was subsequently used to perform parametric studies for similar columns with higher levels of corrosion deterioration. \nSpeaker\n\nChris P. Pantelides\, PhD\, SE\, F. ASCE\, F. ACI\nUniversity of Utah \nDr. Chris P. Pantelides is a Professor in the Department of Civil Engineering at the University of Utah and Site Director of the National Center for Transportation Infrastructure Durability & Life-Extension (TriDurLE). He is a member of TRB AFF50 Committee (Seismic Design and Performance of Bridges)\, ACI Committee 374 (Performance-Based Seismic Design of Concrete Buildings)\, and ACI-ASCE joint Committee 352 (Joints and Connections in Monolithic Concrete Structures). He is the Fellow of ACI and ASCE. Dr. Pantelides received his PhD and MS in Civil Engineering from University of Missouri\, Rolla. His research interests include seismic design\, evaluation\, and rehabilitation of reinforced concrete building and bridge construction.
URL:https://tridurle.wsu.edu/event/seismic-performance-of-corroded-precast-concrete-columns/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240927T100000
DTEND;TZID=America/Los_Angeles:20240927T110000
DTSTAMP:20260403T175047
CREATED:20240916T190727Z
LAST-MODIFIED:20240916T190727Z
UID:3660-1727431200-1727434800@tridurle.wsu.edu
SUMMARY:Meta-learning-based surrogate modeling and system digital twin for life-cycle management of infrastructure systems
DESCRIPTION:Presentation\nMeta-learning-based surrogate modeling and system digital twin for life-cycle management of infrastructure systems \nLife-cycle management (LCM) is an effective tool to balance structural safety and cost. With recent advances in computational power and artificial intelligence\, applications of LCM can be extended from single structures and assets to complex networks and systems. This presentation will introduce two computational methods to enable this transformation. First\, meta-learning-based surrogate modeling is a framework to transfer knowledge from existing modeling tasks to new and similar tasks. This framework is suitable for efficient reliability analysis and optimization at a community level. It has been applied to climate change adaptation and fleet management. Second\, performance-oriented system digital twin (SDT) is a user-centric method to establish custom SDTs based on user’s modeling needs and available data sources. Based on Bayesian network\, the SDT can fuse the data and the complex model of the system. It has been applied to data-driven life-cycle risk assessment of bridge networks in Miami-Dade County. In the future\, with further development of these two computational methods\, LCM can help us move towards a smart\, sustainable\, and resilient community. \nSpeaker\n\nMinghui Cheng\, PhD\nUniversity of Miami \nDr. Minghui Cheng is an assistant professor jointly appointed by the Department of Civil & Architectural Engineering and School of Architecture at the University of Miami. Previously\, he was a postdoctoral associate at Systems Engineering\, Cornell University. He obtained his Ph.D. in Structural Engineering from Lehigh University in 2021 and his B.E. in Civil Engineering at Hunan University\, China\, in 2016. His research focuses on the system digital twin of infrastructure systems and sustainability-informed life-cycle management with an emphasis on surrogate modeling\, optimization\, and machine learning. \nRegister
URL:https://tridurle.wsu.edu/event/meta-learning-based-surrogate-modeling-and-system-digital-twin-for-life-cycle-management-of-infrastructure-systems/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20241203T113000
DTEND;TZID=America/Los_Angeles:20241203T123000
DTSTAMP:20260403T175047
CREATED:20241121T183829Z
LAST-MODIFIED:20241202T001314Z
UID:3717-1733225400-1733229000@tridurle.wsu.edu
SUMMARY:Forensic analysis of outdoor exposed legacy samples undergoing corrosion propagation
DESCRIPTION:Presentation\nForensic analysis of outdoor exposed legacy samples undergoing corrosion propagation \nForensic analysis was performed on outdoor samples that showed cracks. One or more rebars were removed from the selected samples. The outdoor samples have been exposed to seawater wet/dry cycles since 1994. In some cases\, the three top rebars were removed\, in other cases only one rebar or two rebars were removed. The surface condition upon rebar exposure was recorded by photographing the sample\, then the rebars were cleaned by sandblasting the rebars with walnut sand. The cross-section loss as a function of length was measured by using a caliper with conical tip. The pitting factor was calculated using only the sections that showed corrosion\, i.e.\, the non-corroding sections were not included to calculate the average section loss. The percentage average mass loss (η) values were estimated by using the mass of the rebar section rebar embedded with respect to the mass a non-corroding rebar of the same length. The location of the corroding section was not always at the center\, in some cases multiple sections were found to be corroding. In some instances\, a large corrosion spot (likely by coalescing several smaller ones) was observed. Electrochemical measurements were performed\, and typically a set of measurements was performed prior to terminating a rebar. Correlations of rebar potential vs. η\, corrosion current density vs. η and resistivity vs. η were prepared and included in here. There is quite a bit of scatter on these three plots suggesting that some rebars had been corroding for a longer time. \nSpeaker\n\nFrancisco J. Presuel-Moreno\, PhD\nFlorida Atlantic University \nDr. Francisco J. Presuel-Moreno is a professor in the Ocean and Mechanical Engineering department at Florida Atlantic University. Dr. Presuel received his PhD from University of South Florida. Dr. Presuel has been conducting research related to concrete durability and topics related to corrosion of reinforcing steel embedded in concrete for 30 years. \nRegister
URL:https://tridurle.wsu.edu/event/forensic-analysis-of-outdoor-exposed-legacy-samples-undergoing-corrosion-propagation/
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