Reinforcement concrete (RC) bridge decks are surveyed regularly to ensure that they are safe to use and to determine if they require rehabilitation or replacement. The bridge surveys include evaluating subsurface bridge condition. RC bridges have steel reinforcement bars, also called rebars, embedded in their surface, which are prone to corrosion due to factors like moisture, carbonation, use of deicing salts and aging. By the time the effect of corroded rebars is visible on deck surface in form of cracks, the damage is tremendous. If left unchecked, corroded rebars can deteriorate at a faster and significantly affect bridge integrity. So, it is very important to timely identify and repair deteriorated rebars. Ground Penetrating Radar (GPR) is a widely used non-destructive technology (NDT) for detecting subsurface anomalies in variety of structures including RC bridges. The raw GPR data is represented as images that can be processed for obtaining a deterioration map of a bridge, which indicates the level of corrosion in rebars for the entire bridge. The existing methods to generate the deterioration map using GPR data are semi-automated, time consuming and depends on expertise of the engineer analyzing the data. In this thesis, we work towards automating the process of obtaining deterioration map of RC bridge decks based on measuring signal attenuation at the upper rebar mat using GPR. Intensity and gradient-based feature vectors were explored to construct a classifier, which can detect the regions of interest (ROI) corresponding to each rebar in images. Each classifier was tested on datasets constructed from two different bridges. Further, the exact location of rebar was found in each ROI. Once all the rebars were detected throughout the bridge, depth-correction of the measured attenuation is applied so that the component of that measured attenuation caused solely by variation in rebar depth does not skew the results. Finally, a deterioration map was generated which indicates the level of corrosion in the bridge. The proposed algorithm was tested on two RC bridges and the deteriorated regions obtained are compared with the results obtained using existing tools.

Concrete bridge decks require periodic condition assessment and preventive maintenance to extend their useful lifespan. Nondestructive evaluation methods such as Ground Penetrating Radar (GPR) are slowly beginning to replace or complement the manual (visual) assessment of bridge conditions for detecting defects at their early stages. However, GPR scans of bridge decks are frequently cluttered with high amplitude reflections from known parts of the bridge deck, which make the detection of defects low amplitude reflections difficult. One such known part is the embedded steel reinforcement bars known as rebar. This dissertation presents a novel approach to the automated detection of defects in concrete bridge decks by removing known reflections such as rebar from GPR scans of reinforced concrete bridge decks. The algorithm detects reflections from rebar with a frequency-domain pulse detection method, groups detected pulses into clusters, interpolates synthetic rebar reflections based on each cluster, and subtracts the synthetic rebar reflection from the original GPR scan data. This algorithm will facilitate the automated, non-destructive condition assessment of bridge decks.

Bridge decks deteriorate over time as a result of deicing salts, freezing-and-thawing, and heavy use, resulting in internal defects. According to a 2006 study by the American Society of Civil Engineers, 29% of bridges in the United States are considered structurally deficient or functionally obsolete. Ground penetrating radar (GPR) is a promising non-destructive evaluation technique for assessing subsurface conditions of bridge decks. However, the analysis of GPR scans is typically done manually, where the accuracy of the detection process depends on the technician's trained eye. In this work, a framework is developed to automate the detection, locailzation, and characterization of subsurface defects inside bridge decks. This framework is composed of a fractal-based feature extraction algorithm to detect defective regions, a deconvolution algorithm using banded-ICA to reduce overlapping between reflections and to estimate the depth of defects, and a classification algorithm using principal component analysis to identify main features in defective regions. This framework is implemented and simulated using MATLAB and GPR real scans of simulated concrete bridge decks. This framework, as demonstrated by the experimental results, has the following contributions to the current body of knowledge in ground penetrating radar detection and analysis techniques, and in concrete bridge deck condition assessment: 1) developed a framework that integrated detection, localization, and classificationof subsurface defects inside concrete bridge decks, 2) presented a comparison between the most common fractal methods to determine the most suitable one for bridge deck condition assessment, 3) introduced a fractal-based feature extraction algorithm that is capable of detecting and horizontally labeling defective regions using only the underlying GPR B-scan without the need for a training dataset, 4) developed a deconvolution algorithm using EFICA to detect embedded defects in bridge decks, 5) introduced an automated identification methodology of defective regions which can be integrated into a CAD system that allows for better visual assessment by the maintenance engineer and has the potential to eliminate human interpretation errors and reduce condition assessment time and cost, and 6) presented an investigation and a successful attempt to classify some of the common defects in bridge decks.

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.

As the nation's infrastructure continues to age, there is a need to effectively and economically monitor and inspect bridges. With the introduction of non-destructive testing technologies such as Ground Penetrating Radar (GPR) for condition assessment of bridge decks, states will be better equipped to inspect, assess, and prioritize transportation funding to maintain, preserve, and improve infrastructure. The objective of the research is to improve the condition assessment of bridge decks through the use of GPR which can increase the speed, effectiveness, and accuracy of inspections. The non-destructive evaluation technique provides information that can be used to identify the potential amount of internal deterioration of a concrete bridge deck that cannot be identified with a visual inspection. As in many other states, New Mexico currently uses the chain drag method in which the inspection of the deck condition is solely based on inspector's subjective interpretation of the sound produced by dragging a chain over the bridge deck. The use of GPR has the potential to greatly improve the quality of the inspections by collecting more reliable and less subjective information on the condition of bridge decks. Through the collection and analysis of data acquired from the GPR on a set of reinforced concrete decks, this research seeks to provide a better understanding of GPR technology, data acquisition, and training needs for adoption of GPR in bridge deck inspections in the state of New Mexico. With a better understanding of the technology, GPR can become and indispensable tool for more informed decisions for the allocation of funds for maintenance and improved asset management. This research improves implementation and provides effective economic methods to employ this technology to improve the inspection and maintenance of bridge infrastructure.