FE-ANN Based Modeling of 3D Simple Reinforced Concrete Girders for Objective Structural Health Evaluation

The structural deterioration of aging infrastructure systems and the costs of repairing these systems is an increasingly important issue worldwide. Structural health monitoring (SHM), most commonly visual inspection and condition rating, has proven to be a cost-effective method for detecting and evaluating damage. However, the effectiveness varies depending on the availability and experience of personnel performing the largely qualitative damage evaluations. The artificial neural network (ANN) approach presented in this study attempts to augment visual inspection through a crack-induced damage quantification model for reinforced concrete bridge girders that requires only the results of limited field measurements to operate. Using Abaqus finite element (FE) analysis software, the researchers modeled simply supported three-dimensional concrete T-beams with varying geometric, material, and cracking properties. The ANNs achieved excellent prediction accuracies, with coefficients of determination exceeding 0.99 for both networks. Additionally, the ANNs displayed good predictions accuracies when predicting damage levels in beams not included in the database. Results indicate promise for this application of ANNs. Utilizing the two top-performing network architectures, the researchers developed a touch-enabled software application for use as an on-site bridge member damage evaluation tool in the field. The application was given the acronym BRIDGES, for Bridge Rating for Induced Damage in Girders: Evaluation Software. The application’s outputs were validated as matching the ANN predictions. The researchers developed a similar software application for the reverse problem/damage detection and use as an on-site damage prediction tool. This application tries to predict the crack configurations using ANN, based on the geometrical and material parameters, as well as the nine nodal stiffness ratios. This touch-enabled application was given the acronym DRY BEAM, for Damage Recognition Yielding Bridge Evaluation After Monitoring.

Language

English

Project

Status: Completed
Funding: $99937
Contract Numbers:
DTRT13-G-UTC37
Sponsor Organizations:
Midwest Transportation Center

Iowa State University
2711 S Loop Drive, Suite 4700
Ames, IA United States 50010-8664

Kansas Department of Transportation

Eisenhower State Office Building
700 SW Harrison Street
Topeka, KS United States 66603-3754

Office of the Assistant Secretary for Research and Technology

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

Iowa State University
2711 S Loop Drive, Suite 4700
Ames, IA United States 50010-8664
Performing Organizations:
Kansas State University

148 Rathbone Hall
Manhattan, KS United States 66506
Principal Investigators:
Rasheed, Hayder
Start Date: 20150301
Expected Completion Date: 20170531
Actual Completion Date: 0

Subject/Index Terms

TRT Terms: Concrete bridges; Condition surveys; Databases; Decision making; Girder bridges; Instrumentation; Maintenance; Mobile applications; Neural networks; Sensors; Stiffness; Structural health monitoring
Subject Areas: Bridges and other structures; Data and Information Technology; Highways; Maintenance and Preservation;

Filing Info

Accession Number: 01579730
Record Type: Research project
Source Agency: Midwest Transportation Center
Contract Numbers: DTRT13-G-UTC37
Files: UTC, RiP
Created Date: Oct 27 2015 12:22PM