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).

The ASCE report card 2013 rated bridges at a grade of C+, implying their condition is moderate and require immediate attention. Moreover, the Federal Highway Administration reported that it is required to invest more than $20.5 billion each year to eliminate the bridge deficient backlog by 2028. In Canada 2012, more than 50% of bridges fall under fair, poor, and very poor categories, where more than $90 billion are required to replace these bridges. Therefore, government agencies should have an accurate way to inspect and assess the corrosiveness of the bridges under their management. Numerical Amplitude method is one of the most common used methods to interpret Ground Penetrating Radar (GPR) outputs, yet it does not have a fixed and informative numerical scale that is capable of accurately interpreting the condition of bridge decks. To overcome such problem, the present research aims at developing a numerical GPR-based scale with three thresholds and build deterioration models to assess the corrosiveness of bridge decks. Data, for more than 60 different bridge decks, were collected from previous research works and from surveys of bridge decks using a ground-coupled antenna with the frequency of 1.5 GHz. The amplitude values of top reinforcing rebars of each bridge deck were classified into four categories using k-means clustering technique. Statistical analysis was performed on the collected data to check the best-fit probability distribution and to choose the most appropriate parameters that affect thresholds of different categories of corrosion and deterioration. Monte-Carlo simulation technique was used to validate the value of these thresholds. Moreover, a sensitivity analysis was performed to realize the effect of changing the thresholds on the areas of corrosion. The final result of this research is a four-category GPR scale with numerical thresholds that can assess the corrosiveness of bridge decks. The developed scale has been validated using a case study on a newly constructed bridge deck and also by comparing maps created using the developed scale and other methods. The comparison shows sound and promising results that advance the state of the art of GPR output interpretation and analysis. In addition, deterioration models and curves have been developed using Weibull Distribution based on GPR outputs and corrosion areas. The developed new GPR scale and deterioration models will help the decision makers to assess accurately and objectively the corrosiveness of bridge decks. Hence, they will be able to take the right intervention decision for managing these decks.

" 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.

Bridge management system (BMS) is an effective mean for managing bridges throughout their design life. BMS requires accurate collection of data pertinent to bridge conditions. Non Destructive Evaluation methods (NDE) are automated accurate tools used in BMS to supplement visual inspection. This research provides overview of current practices in bridge inspection and in-depth study of thirteen NDE methods for condition assessment of concrete bridges and eleven for structural steel bridges. The unique characteristics, advantages and limitations of each method are identified along with feedback on their use in practice. Comparative study of current practices in bridge condition rating, with emphasis on the United States and Canada is also performed. The study includes 4 main criteria: inspection levels, inspection principles, inspection frequencies and numerical ratings for 4 provinces and states in North America and 5 countries outside North America. Considerable work has been carried out using a number of sensing technologies for condition assessment of civil infrastructure. Fewer efforts, however, have been directed for integrating the use of these technologies. This research presents a newly developed method for automated condition assessment and rating of concrete bridge decks. The method integrates the use of ground penetrating radar (GPR) and infrared thermography (IR) technologies. It utilizes data fusion at pixel and feature levels to improve the accuracy of detecting defects and, accordingly, that of condition assessment. Dynamic Bayesian Network (DBN) is utilized at the decision level of data fusion to overcome cited limitations of Markov chain type models in predicting bridge conditions based on prior inspection results. Pixel level image fusion is applied to assess the condition of a bridge deck in Montreal, Canada using GPR and IR inspection results. GPR data are displayed as 3D from 24 scans equally spaced by 0.33m to interpret a section of the bridge deck surface. The GPR data is fused with IR images using wavelet transform technique. Four scenarios based on image processing are studied and their application before and after data fusion is assessed in relation to accuracy of the employed fusion process. Analysis of the results showed that bridge condition assessment can be improved with image fusion and, accordingly, support inspectors in interpretation of the results obtained. The results also indicate that predicted bridge deck condition using the developed method is very close to the actual condition assessment and rating reported by independent inspection. The developed method was also applied and validated using three case studies of reinforced concrete bridge decks. Data and measurements of multiple NDE methods are extracted from Iowa, Highway research board project, 2011. The method utilizes data collected from ground penetrating radar (GPR), impact echo (IE), Half-cell potential (HCP) and electrical resistivity (ER). The analysis results of the three cases indicate that each level of data fusion has its unique advantage. The power of pixel level fusion lies in combining the location of bridge deck deterioration in one map as it appears in the fused image. While, feature fusion works in identification of specific types of defects, such as corrosion, delamination and deterioration. The main findings of this research recommend utilization of data fusion within two levels as a new method to facilitate and enhance the capabilities of inspectors in interpretation of the results obtained. To demonstrate the use of the developed method and its model at the decision level of data fusion an additional case study of a bridge deck in New Jersey, USA is selected. Measurements of NDE methods for years 2008 and 2013 for that bridge deck are used as input to the developed method. The developed method is expected to improve current practice in forecasting bridge deck deterioration and in estimating the frequency of inspection. The results generated from the developed method demonstrate its comprehensive and relatively more accurate diagnostics of defects.

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.