Details:

Creators:
Winn, Emily K. ; Burgueño, Rigoberto
Winn, Emily K. ; Burgueño, Rigoberto Less -
Corporate Creators:
Michigan State University. Dept. of Civil and Environmental Engineering
Corporate Contributors:
United States. Federal Highway Administration
Subject/TRT Terms:
[+]
Artificial Intelligence Bridge Decks Bridge Management Systems Databases Deterioration Highway Bridges Inspection Neural Networks Structural Health Monitoring
Publication/ Report Number:
RC-1587a
Resource Type:
Tech Report
Geographical Coverage:
Michigan
Corporate Publisher:
Michigan. Dept. of Transportation
NTL Classification:
NTL-HIGHWAY/ROAD TRANSPORTATION-Bridges and Structures;NTL-HIGHWAY/ROAD TRANSPORTATION-Construction and Maintenance;NTL-PLANNING AND POLICY-Management Systems;
Abstract:
This research documents the development and evaluation of artificial neural network (ANN) models to predict the condition ratings of concrete highway bridge decks in Michigan. Historical condition assessments chronicled in the national bridge inventory (NBI) database were used to develop the ANN models. Two types of artificial neural networks, multi-layer perceptrons and ensembles of neural networks (ENNs), were developed and their performance was evaluated by comparing them against recorded field inspections and using statistical methods. The MLP and ENN models had an average predictive capability across all ratings of 83% and 85%, respectively, when allowed a variance equal to bridge inspectors. A method to extract the influence of parameters from the ANN models was implemented and the results are consistent with the expectations from engineering judgment. An approach for generalizing the neural networks for a population of bridges was developed and compared with Markov chain methods. Thus, the developed ANN models allow modeling of bridge deck deterioration at the project (i.e., a specific existing or new bridge) and system/network levels. Further, the generalized ANN degradation curves provided a more detailed degradation profile than what can be generated using Markov models. A bridge management system (BMS) that optimizes the allocation of repair and maintenance funds for a network of bridges is proposed. The BMS uses a genetic algorithm and the trained ENN models to predict bridge deck degradation. Employing the proposed BMS leads to the selection of optimal bridge repair strategies to protect valuable infrastructure assets while satisfying budgetary constraints. A program for deck degradation modeling based on trained ENN models was developed as part of this project.
Format:
PDF
Funding:
2009-0746/Z2
Collection(s):
US Transportation Collection
Main Document Checksum:
[+]
urn:sha256:a6e878841c67e41b4ed9336bc94d73223e17a7878907ed377e602a5a6dbb2e20
Download URL:
https://rosap.ntl.bts.gov/view/dot/26531/dot_26531_DS1.pdf
File Type:

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