Improving Bridge Assessment through the Integration of Conventional Visual Inspection, Non-Destructive Evaluation, and Structural Health Monitoring Data
University of Pittsburg – IRISE (Dr. A.H. Alavi) Rutgers University – CAIT (Dr. F. Moon) Wiss, Janney, Elstner Associates Inc. – (Dr. S.K. Babanajad)
University of Pittsburgh | Swanson School of Engineering
The Research Problem
Large costs and relatively long intervals between inspections for large structures Current assessment approaches are generally subjective in nature and provide only qualitative data reflective of surface or near-surface condition
Huge gap exists in the establishment of effective approaches to fuse the collected massive NDE and SHM data
University of Pittsburgh | Swanson School of Engineering
Project Objectives Developing an advanced multi-modal data-driven NDE and SHM framework that can reliably operate on raw data with various levels of spatial and temporal resolution Addressing the principal challenges associated with studying the service life of bridge structures: 1. 2.
3.
Long term monitoring of bridges (which requires accelerated aging) How to deal with the diverse outputs related to bridge condition (in terms of data collected through SHM, NDE, and visual inspection) An information fusion system to identify the synergies among bridge degradation, remaining service life, and the results taken from the multimodal sensing technologies (SHM, NDE, and UAV-collected data)
University of Pittsburgh | Swanson School of Engineering
Comprehensive Bridge Deck Inspection System Development
BEAST Data Collection
Project Objectives Periodical Data Collection
Accelerated Bridge Aging
UAV
NDE
SHM
Vision-based Data Future Field Implementation
Bridge Condition Evaluation System Development
NDE Data
Project Approach/Deliverables
Tasks: Collection of high-resolution and high-temporal data from the BEAST Facility Advanced statistical data analytics to discover synergies between different methods Development of recommendations and guidelines for PennDOT implementation
Deliverables: Final Report Technical Articles Technical Events (TRB, NEBPP)
University of Pittsburgh | Swanson School of Engineering
Schedule/Status
Start date was December 1, 2019 Due to COVID-19 pandemic, the Rutgers BEAST operation paused multiple times More than 1.1 million traffic cycles so far , 12 rounds of data collection 3 rounds of drone data collection (HD Image and IR Thermography) Vision-based crack, spall and delamination detection software program1,2,3 Statistical analysis for fusing multi-resource NDE data Visual Analytics: 3D reconstruction model of the bridge systems
Zhang, K. Barri, S. K. Babanajad, A. H. Alavi, “Real-Time Detection of Cracks on Concrete Bridge Decks Using Deep Learning in Frequency Domain”, Engineering, Elsevier, 2020. Zhang, A. H. Alavi, “Automated two-stage approach for detection and quantification of surface defects in concrete bridge decks”, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XV, 2021 3 Q. Zhang,K. Barri, Z, Wan, “A Deep Learning-based Autonomous System for Detection and Quantification of Delamination on Concrete Bridge Decks”, submitted to International Bridge Conference 2021. 1 Q. 2 Q.
Application of Research Results Pitt Bridge Condition Assessment System (PittBCAS)
UAV collected IR Image
University of Pittsburgh | Swanson School of Engineering
Stationary IR Image
Thank you Amir H. Alavi, PhD Assistant Professor Department of Civil and Environmental Engineering University of Pittsburgh E-mail: alavi@pitt.edu
University of Pittsburgh | Swanson School of Engineering