Civil and Environmental Engineering


First Advisor

Joshua S Steelman

Date of this Version

Spring 4-27-2020


Garcia, F., 2020. Reliability-Calibrated ANN-Based Load And Resistance Factor Load Rating For Steel Girder Bridges. M.S. thesis. University of Nebraska-Lincoln.


A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Civil Engineering, Under the Supervision of Professor Joshua S. Steelman. Lincoln, Nebraska: April 2020

Copyright 2020 Francisco Garcia


This research aimed to develop a supplemental ANN-based tool to support the Nebraska Department of Transportation (NDOT) in optimizing bridge management investments when choosing between refined modeling, field testing, retrofitting, or bridge replacement. ANNs require an initial investment to collect data and train a network, but offer future benefits of speed and accessibility to engineers utilizing the trained ANN in the future. As the population of rural bridges in the Midwest approaching the end of their design service lives increases, Departments of Transportation are under mounting pressure to balance safety of the traveling public with fiscal constraints. While it is well-documented that standard code-based evaluation methods tend to conservatively overestimate live load distributions, alternate methods of capturing more accurate live load distributions, such as finite element modeling and diagnostic field testing, are not fiscally justified for broad implementation across bridge inventories. Meanwhile, ANNs trained using comprehensive, representative data are broadly applicable across the bridge population represented by the training data. The ANN tool developed in this research will allow NDOT engineers to predict critical girder distribution factors (GDFs), removing unnecessary conservativism from approximate AASHTO GDFs, potentially justifying load posting removal for existing bridges, and enabling more optimized design for new construction, using ten readily available parameters, such as bridge span, girder spacing, and deck thickness. A key drawback obstructing implementation of ANNs in bridge rating and design is the potential for unconservative ANN predictions. This research provides a framework to account for increased live load effect uncertainty incurred from neural network prediction errors by performing a reliability calibration philosophically consistent with AASHTO Load and Resistance Factor Rating. The study included detailed FEA for 174 simple span, steel girder bridges with concrete decks. Subsets of 163 and 161 bridges within these available cases comprised the ANN design and training datasets for critical moment and shear live load effects, respectively. The reliability calibration found that the ANN live load effect prediction error with mean absolute independent testing error of 3.65% could be safely accommodated by increasing the live load factor by less than 0.05. The study also demonstrates application of the neural network model validated with a diagnostic field test, including discussion of potential adjustments to account for noncomposite bridge capacity and Load Factor Rating instead of Load and Resistance Factor Rating.

Advisor: Joshua S. Steelman