Workshop Findings
BBD 2020 Workshop Proceedings
BBD 2019 Workshop Proceedings
BBD 2017 Working Session Report
Summary of the working group discussions
BBD 2016 Working Session Reports
- What are the challenges with sharing Bridge health data? Results
- How can standardization be accomplished for sharing Bridge health data? Results
- What benefits could result from sharing Bridge health data? Results
Big Data Research pipeline
DEEDS: a platform for shared data and computing that supports the entire research process
- Members only: Bridge Analytics Platform
Publications
Dissertation
Ph.D. Student Emmanuel Akintunde successfully defended his dissertation Fatigue Prognosis in Bridges Using Long Term Monitoring Data on July 6th, 2022. The University of Nebraska – Lincoln.
Presentation
Ph.D. Student Akshay Kale gave a presentation at the April 2022 MWBPP monthly teleconference. His talk was titled Building Interpretable Methods For Identifying Bridge Maintenance Patterns The University of Nebraska at Omaha. The Midwest Bridge Preservation Partnership (MWBPP) is comprised of representatives from regional state and local highway agencies, provincial transport agencies, industry, suppliers, consultants, and academia.
-
Ji Young Lee, Chungwook Sim, Carrick Detweiler, and Brendan Barnes (2019), “Computer-Vision Based UAV Inspection for Steel Bridge Connections,” IWSHM 2019 Conference Proceedings, Stanford, CA. Sep. 10-12.
-
Eftekhar Azam, S., Linzell, D.G. & Rageh, A. “Damage Detection in Structural Systems Utilizing Artificial Neural Networks and Proper Orthogonal Decomposition.” Structural Control and Health Monitoring, 2018
-
Rageh, A., Linzell, D.G. & Eftekhar Azam, S. “Automated, Strain-Based, Output-Only Bridge Damage Detection.” Journal of Civil Structural Health Monitoring, 2018.
-
Won., K., and Sim, C. (2020), “Automated Transverse Crack Mapping System with Optical Sensors and Big Data Analytics,” Sensors, Vol. 20, No. 7, 1838;
-
Juan Castiglione, Rodrigo Astroza, Saeed Eftekhar Azam, Daniel Linzell, Auto-regressive model based input and parameter estimation for nonlinear finite element models, Mechanical Systems and Signal Processing, Volume 143,2020,106779, ISSN 0888-3270.
-
Rageh, A. Eftekhar Azam, S., & Linzell, D.G. “Steel railway bridge fatigue damage detection using numerical models and machine learning: Mitigating influence of modeling uncertainty.” International Journal of Fatigue, 2019.
-
Akshay Kale, Masters Thesis completed Spring 2019, “Identifying predictors of bridge deterioration in the US from a Data Science Perspective”. Committee: Robin Gandhi, Brian Ricks, Jong-Hoon Youn.
-
Eftekhar Azam, S., Linzell, D. and Rageh, A. “Experimental Validation and Numerical Investigation of Virtual Strain Sensing Methods for Railway Bridges.” Mechanical Systems and Signal Processing, 2020, Revised and under re-review.
-
Kale, A., Gandhi, R., Ricks, B., Identifying predictors of bridge deterioration in The United States from a data science perspective. Under review at ASCE journals.
-
Gandhi, R., Khazanchi, D., Linzell, D., Ricks, B. and Sim, C. (May 2018). “The Hidden Crisis : Developing Smart Big Data pipelines to address Grand Challenges of Bridge Infrastructure health in the United States.” Proceedings of the 15th ISCRAM Conference – Rochester, NY, USA (WiPe Paper – Open Track), pp. 1016-1021. Proceedings
-
O’Brien, C., Lacy, B., Ricks, B., Gandhi, R., Haas, C., Sim, C., Khazanchi, D., Linzell, D., An examination of bridge maintenance patterns across US states using actuarial methods, Manuscript under preparation.
Web Applications
Datasets
Union Pacific Bridge Defect Image Datasets
- 18,000 images of bridge defects. Members only access.
Nebraska DoT Bridge Inspection Records Dataset cross-linked with National Bridge Inventory
- Platform: datacenterhub.org
- Chungwook Sim; Robin Gandhi; David M Gee; Ajay Khampariya; Akshay Kale; Dreizan Moore (2017), “Nebraska Bridge Data,” https://datacenterhub.org/resources/14392. To request access please contact Chungwook Sim.
Creating common schemas for Bridge Deficiencies
- JSON-Schemas made available on Github
Converting NBI Data into Structured JSON
Analyzing Nebraska Bridge Inspection Records
- Jupyter Notebook with MongoDB for analyzing all NBI records from 1992 to 2016 with over 17 million records
- Jupyter Notebook with MongoDB to link Nebraska DOT and NBI datasets
- R markdown for Nebraska Bridge analysis based on NBI data
NBI compute infrastructure at Labs Workbench, a service of the National Data Service (NDS), a MBDH Infrastructure Partner
This repository contains preliminary information related to the NDS Labs National Bridge Infrastructure (NBI) data pilot. This pilot consists of a MongoDB containing the NBI dataset and a Jupyter environment with sample notebook for use in the Labs Workbench system.