Earthquake-Induced Damage Classification of Bridges Using Artificial Neural Networks

Fragility analysis is currently used to develop a probabilistic seismic demand model with an assumed lognormal distribution and then determine the probability of exceeding certain seismic demand thresholds for various states of damage. Due to the complexity in probability calculations, the seismic demand is often defined by one intensity measure of the earthquake ground motion such as peak ground acceleration, and its lognormal distribution has been repeatedly demonstrated inaccurate as the level of damage increases. This study aims to develop artificial neural networks (ANNs) for a near real-time evaluation of the regional structural damage of a highway bridge network after a catastrophic earthquake. Bridge responses to the earthquake are treated as the earthquake-induced ground motion classifiers for structural damage states. The input and output layers of an ANN represent intensity measures of a ground motion and their corresponding damage state, respectively. To achieve this objective, the scope of work includes: (1) Select representative bridges along a major highway, (2) Collect and organize a big data set of ground motions, (3) Model the representative bridges and evaluate their damage states based on a damage index under the ground motions through time history analysis, (4) Label the ground motions with corresponding damage states and develop a balanced set of training and test data, (5) Train the ANN with the training dataset and evaluate the overall accuracy of damage prediction using unseen test dataset, (6) Optimize the ANN architecture for robust and accurate performance by ranking the importance of various intensity measures, comparing two structural damage indices, and considering varying numbers of hidden layers and neurons, and (7) Evaluate the performance of the ANNs for existing bridges along an emergency designated route with practical considerations of three intensity measures availed in the American Association of State Highway and Transportation Officials (AASHTO) Guide Specifications for Load and Resistance Factor Design (LRFD) Seismic Bridge Design.

Language

• English

Project

• Status: Active
• Funding: $351403
• Contract Numbers:

  69A3551747107

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Mid-America Transportation Center

  University of Nebraska-Lincoln
  2200 Vine Street, PO Box 830851
  Lincoln, NE  United States  68583-0851
• Project Managers:

  Stearns, Amy

• Performing Organizations:

  Missouri University of Science & Technology, Rolla

  Department of Engineering
  202 University Center
  Rolla, MO    65409
• Principal Investigators:

  Chen, Genda

• Start Date: 20210101
• Expected Completion Date: 20220630
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers
• Source Data: RiP Project 91994-85

Subject/Index Terms

• TRT Terms: Bridges; Classification; Earthquake resistant design; Earthquakes; Evaluation and assessment; Forecasting; Neural networks; Seismicity; Structural deterioration and defects
• Subject Areas: Bridges and other structures; Design; Highways; Maintenance and Preservation; Planning and Forecasting;

Filing Info

• Accession Number: 01762021
• Record Type: Research project
• Source Agency: Mid-America Transportation Center
• Contract Numbers: 69A3551747107
• Files: UTC, RIP
• Created Date: Jan 7 2021 2:07PM