Bridges are key elements in the US transportation system. There are more than six hundred thousand bridges on the highway system in the United States. Approximately one third of these bridges are in need of maintenance and will cost more than $120 billion to rehabilitate or repair. Several factors affect the performance of bridges over their life spans. Identifying these factors and accurately assessing the condition of bridges are critical in the development of an effective maintenance program. While there are several methods available for condition assessment, selecting the best technique remains a challenge. Therefore, developing an accurate and reliable model for concrete bridge deck deterioration is a key step towards improving the overall bridge condition assessment process. Consequently, the main goal of this dissertation is to develop an improved bridge deck deterioration prediction model that is based on the National Bridge Inventory (NBI) database. To achieve the goal, deterministic and stochastic approaches have been investigated to model the condition of bridge decks. While the literatures have typically proposed the Markov chain method as the best technique for the condition assessment of bridges, this dissertation reveals that some probability distribution functions, such as Lognormal and Weibull, could be better prediction models for concrete bridge decks under certain condition ratings. A new universal framework for optimizing the performance of prediction of concrete bridge deck condition was developed for this study. The framework is based on a nonlinear regression model that combines the Markov chain method with a state-specific probability distribution function. In this dissertation, it was observed that on average, bridge decks could stay much longer in their condition ratings than the typical 2-year inspection interval, suggesting that inspection schedules might be extended beyond 2 years for bridges in certain condition rating ranges. The results also showed that the best statistical model varied from one state to another and there was no universal statistical prediction model that can be developed for all states. The new framework was implemented on Michigan data and demonstrated that the prediction error in the combined model was less than each of the two models (i.e. Markov and Lognormal). The results also showed that average daily traffic, age, deck area, structure type, skew angle, and environmental factors have significant impact on the deterioration of concrete bridge decks. The contributions of the work presented in this dissertation include: 1) the identification of the significant factors that impact concrete bridge deck deterioration; 2) the development of a universal deterioration prediction framework that can be uniquely tailored for each state’s data; and 3) supporting the possibility of extending inspection schedules beyond the typical 2-year cycles. Future work may involve: 1) evaluating each of the factors that impact the deterioration rates in more depth by refining the investigation ranges; 2) investigating the possibility of revising the regular bridge deck inspection intervals beyond the 2-year cycles; and 3) developing deterioration prediction models for other bridge elements (i.e. superstructure and substructure) using the framework developed in this dissertation.

Bridges play a significant role in the U.S. economy. The number of the bridges in the U.S. exceeds six hundred thousand. Almost one third of them are considered structurally deficient and will require more than $164 billion to repair or replace. Identifying the factors that affect the performance of concrete bridge decks during its service life is critical to the development of an accurate condition assessment and deterioration prediction model. Accurate bridge deck deterioration models can provide vital information for predicting short- and long-term behavior of concrete bridge decks and minimizing costly routine inspection and maintenance activities. Therefore, the main goal of this dissertation is to develop a deterioration prediction model for concrete bridge decks that is based on the National Bridge Inventory (NBI) database. To achieve the goal, five deterioration prediction models for concrete bridge decks were developed using Multinomial Logistic Regression, Decision Tree, Artificial Neural Network, k-Nearest Neighbors and Naive Bayesian machine learning techniques. Michigan bridge deck data from NBI between the years 1992 to 2015 were used for training the various prediction models. The results show that the performance of all five developed models were acceptable. However, the artificial neural network achieved the highest accuracy in the validation process. Additionally, bridge decks age, area, average daily traffic, and skew angle are found to be significant factors in the deterioration of concrete bridge decks. Furthermore, it was observed that bridge decks could stay in their condition rating more than the typical 2-year inspection interval, suggesting that inspection schedules could be extended for certain bridges that had slower deterioration rates. The contributions of this work include 1) the development of an optimized deterioration prediction model that can be used in the condition assessment process for concrete bridge decks, 2)the identification of the factors that have the most impact on concrete bridge deck deterioration,and 3) demonstrating that the inspection schedule can be longer than 2 years for bridges that do not deteriorate fast which can lead to cost and time savings. Future work can include the following: (1)developing deterioration prediction models for concrete bridge decks using deep learning techniques; (2) developing deterioration prediction models for other bridge specific elements (i.e., superstructure and substructure) using multivariant analysis; (3) developing deterioration prediction models for other (or all) U.S. states using the framework developed in this research; and (4) investigating the prospect of revising the mandated inspection interval beyond the 2-year period.

" TRB's second Strategic Highway Research Program (SHRP 2) Report S2-R06A-RR-1: Nondestructive Testing to Identify Concrete Bridge Deck Deterioration identifies nondestructive testing technologies for detecting and characterizing common forms of deterioration in concrete bridge decks.The report also documents the validation of promising technologies, and grades and ranks the technologies based on results of the validations.The main product of this project will be an electronic repository for practitioners, known as the NDToolbox, which will provide information regarding recommended technologies for the detection of a particular deterioration. " -- publisher's description.

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.

Bridges are at the heart of transportation systems connecting the roads to and between the mainlands. Thus, bridges are an integral part of the economic growth of any country. They are subjected to dynamic loads of the vehicles and the environmental effects. These loads cause stress and strain cycles causing its deterioration by initiating microcracking. The deterioration is then accelerated due to the chloride attack which causes the corrosion of the steel reinforcement resulting in cracking and delamination of concrete and ultimately leads to failure. It is essential to analyze the bridge with its actual condition which is difficult with a visual inspection. This analysis can help in determining the degree of repairs needed and an approximate idea about its service life. The development of the Non-Destructive Test (NDT) methods helps assess the condition of the bridge without any kind of damage to the original structure. In the past few decades, the Non-Destructive Evaluation (NDE) with the help of Ground Penetration Radar (GPR) has gained popularity due to its ease in the evaluation of the larger areas such as bridge deck and parking lot in a shorter amount of time with sufficient training. The NDE using GPR for Structural Health Monitoring (SHM) has been still evolving with new improvements in its technology as well as the development of new methods for the analysis of its data. A positive step towards detecting the subsurface materials present in the cracks has been undertaken in this study. A methodology to detect the subsurface cracks/gaps in concrete using GPR has been developed here by preparing three concrete samples of dimensions 50 x 25 x 5 cm3, 50 x 25 x 10 cm3, and 50 x 25 x 20 cm3 in the laboratory. The detection of reinforcement of 6 mm, 10 mm, 18 mm, 20 mm diameter, as well as a 21.8 mm Fiber Reinforcement Polymer (FRP) bar, are studied along with the detection of the air gap, water gap, and gap with the salt solutions of thickness 3 mm, 4 mm, 4.8 mm, 5.8 mm and 8.8 mm under the depth of 5 cm, 10 cm, and 15 cm. The amplitude values of these parameters are studied, and a comparison is made to check the ability of GPR to detect this material in cracks and/or delamination with changes in depths. This will be helpful in analyzing the GPR data with more reliability. Along with this, a non-linear finite element model (FEM) of a bridge superstructure using a fiber element is developed. The FE model of the bridge deck is updated and analyzed using a GPR defect map. This procedure of model updating is less tedious than the previous method available in the literature and proves to be time-saving. This model updating procedure will prove to be helpful in estimating the capacity of the bridge and make a prediction for future deterioration with the help of NDE methods (here GPR).

"This study investigates two bridge decks in the state of Missouri using both nondestructive and destructive testing methods. The Missouri Department of Transportation (MoDOT) is responsible for the monitoring and maintenance of over 10,000 bridges. Currently monitoring of these bridges includes a comprehensive visual inspection. In this study, ground-coupled ground penetrating radar (GPR) is used to estimate deterioration, along with other traditional methods, including visual inspection, and core evaluation. Extracted core samples were carefully examined, and the volume of permeable pore space was determined for each core. After the initial investigation, the two bridges underwent rehabilitation using hydrodemolition as a method to remove loose or deteriorated concrete. Depths and locations of material removal were determined using light detection and ranging (lidar). Data sets were compared to determine the accuracy of GPR to predict deterioration for condition monitoring and rehabilitation planning of bridge decks. As shown by the lidar survey of the material removed during rehabilitation, the GPR top reinforcement reflection amplitude accurately predicted regions of deterioration within the bridge decks. In general, regions with lower reflection amplitudes, indicating more evidence of deterioration, corresponded to regions with greater depths of material removal during the rehabilitation. Also, the GPR top reinforcement reflection amplitude indicated deterioration in areas where visual deterioration was noticed from the top surface of the deck. The majority of cores with delaminations were extracted from sections where the GPR top reinforcement reflection amplitude indicated greater evidence of deterioration based on lower amplitude values."--Abstract, page iii.