Integrated NDE Methods Using Data Fusion-For Bridge Condition Assessment

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

Robotic Platform Rabit for Condition Assessment of Concrete Bridge Decks Using Multiple NDE Technologies

Current assessment of concrete bridge decks relies on visual inspection and use of simple nondestructive and destructive evaluations. More advanced, but still manual nondestructive evaluation (NDE) technologies provide more comprehensive assessment. Still, due to a lower speed of data collection and still not automated data analysis and interpretation, they are not used on a regular basis. The development and implementation of a fully autonomous robotic system for condition assessment of concrete bridge decks using multiple nondestructive evaluation (NDE) technologies is described. The system named RABIT (Robotics Assisted Bridge Inspection Tool) resolves issues related to the speed of data collection and analysis. The system concentrates on the characterization of internal deterioration and damage, in particular three most common deterioration types in concrete bridge decks: rebar corrosion, delamination, and concrete degradation. For those purposes, RABIT implements four NDE technologies: electrical resistivity (ER), impact echo (IE), ultrasonic surface waves (USW) and ground-penetrating radar (GPR). Because the system utilizes multiple probes or large sensor arrays for the four NDE technologies, the spatial resolution of the results is significantly improved. The technologies are used in a complementary way to enhance the overall condition assessment and certainty regarding the detected deterioration. In addition, the system utilizes three high resolution cameras to image the surface of the deck for crack mapping and documentation of previous repairs, and to image larger areas of the bridge for inventory purposes. Finally, the robot’s data visualization platform facilitates an intuitive 3-dimensional presentation of the main three deterioration types and deck surface features

Comprehensive Bridge Deck Deterioration Mapping of Nine Bridges by Nondestructive Evaluation Technologies Final Report, January 2011

The primary objective of this research was to demonstrate the benefits of NDT technologies for effectively detecting and characterizing deterioration in bridge decks. In particular, the objectives were to demonstrate the capabilities of ground-penetrating radar (GPR) and impact echo (IE), and to evaluate and describe the condition of nine bridge decks proposed by Iowa DOT. The first part of the report provides a detailed review of the most important deterioration processes in concrete decks, followed by a discussion of the five NDT technologies utilized in this project. In addition to GPR and IE methods, three other technologies were utilized, namely: half-cell (HC) potential, electrical resistivity (ER), and ultrasonic surface waves (USW) method. The review includes a description of the principles of operation, field implementation, data analysis, and interpretation; information regarding their advantages and limitations in bridge deck evaluations and condition monitoring are also implicitly provided.. The second part of the report provides descriptions and bridge deck evaluation results from the nine bridges. The results of the NDT surveys are described in terms of condition assessment maps and are compared with the observations obtained from the recovered cores or conducted bridge deck rehabilitation. Results from this study confirm that the used technologies can provide detailed and accurate information about a certain type of deterioration, electrochemical environment, or defect. However, they also show that a comprehensive condition assessment of bridge decks can be achieved only through a complementary use of multiple technologies at this stage,. Recommendations are provided for the optimum implementation of NDT technologies for the condition assessment and monitoring of bridge decks

Integrated Condition Assessment of Subway Networks Using Computer Vision and Nondestructive Evaluation Techniques

Subway networks play a key role in the smart mobility of millions of commuters in major metropolises. The facilities of these networks constantly deteriorate, which may compromise the integrity and durability of concrete structures. The ASCE 2017 Report Card revealed that the condition of public transit infrastructure in the U.S. is rated D-; hence a rehabilitation backlog of $90 billion is estimated to improve transit status to good conditions. Moreover, the Canadian Urban Transit Association (CUTA) reported 56.6 billion CAD in infrastructure needs for the period 2014-2018. The inspection and assessment of metro structures are predominantly conducted on the basis of Visual Inspection (VI) techniques, which are known to be time-consuming, costly, and qualitative in nature. The ultimate goal of this research is to develop an integrated condition assessment model for subway networks based on image processing, Artificial Intelligence (AI), and Non-Destructive Evaluation (NDE) techniques. Multiple image processing algorithms are created to enhance the crucial clues associated with RGB images and detect surface distresses. A complementary scheme is structured to channel the resulted information to Artificial Neural Networks (ANNs) and Regression Analysis (RA) techniques. The ANN model comprises sequential processors that automatically detect and quantify moisture marks (MM) defects. The RA model predicts spalling/scaling depth and simulates the de-facto scene by developing a hybrid algorithm and interactive 3D presentation. In addition, a comparative analysis is performed to select the most appropriate NDE technique for subway inspection. This technique is applied to probe the structure and measure the subsurface defects. Also, a novel model for the detection of air voids and water voids is proposed. The Fuzzy Inference System (FIS), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Monte Carlo Simulation (MCS) are streamlined through successive operations to create the integrated condition assessment model. To exemplify and validate the proposed methodology, a myriad of images and profiles are collected from Montréal Metro systems. The results ascertain the efficacy of the developed detection algorithms. The attained recall, precision, and accuracy for MM detection algorithm are 93.2%, 96.1%, and 91.5% respectively. Whereas for spalling detection algorithm, are 91.7%, 94.8%, and 89.3% respectively. The mean and standard deviation of error percentage in MM region extraction are 12.2% and 7.9% respectively. While for spalling region extraction, they account for 11% and 7.1% respectively. Subsequent to selecting the Ground Penetrating Radar (GPR) for subway inspection, attenuation maps are generated by both the amplitude analysis and image-based analysis. Thus, the deteriorated zones and corrosiveness indices for subway elements are automatically computed. The ANN and RA models are validated versus statistical tests and key performance metrics that indicated the average validity of 96% and 93% respectively. The air/water voids model is validated through coring samples, camera images, infrared thermography and 3D laser scanning techniques. The validation outcomes reflected a strong correlation between the different results. A sensitivity analysis is conducted showing the influence of the studied subway elements on the overall subway condition. The element condition index using neuro-fuzzy technique indicated different conditions in Montréal subway systems, ranging from sound concrete to very poor, represented by 74.8 and 35.1 respectively. The fuzzy consolidator extrapolated the subway condition index of 61.6, which reveals a fair condition for Montréal Metro network. This research developed an automated tool, expected to improve the quality of decision making, as it can assist transportation agencies in identifying critical deficiencies, and by focusing constrained funding on most deserving assets

Surface and Sub-Surface Analyses for Bridge Inspection

The development of bridge inspection solutions has been discussed in the recent past. In this dissertation, significant development and improvement on the state-of-the-art in the field of bridge inspection using multiple sensors (e.g. ground penetrating radar (GPR) and visual sensor) has been proposed. In the first part of this research (discussed in chapter 3), the focus is towards developing effective and novel methods for rebar detection and localization for sub-surface bridge inspection of steel rebars. The data has been collected using Ground Penetrating Radar (GPR) sensor on real bridge decks. In this regard, a number of different approaches have been successively developed that continue to improve the state-of-the-art in this particular research area. The second part (discussed in chapter 4) of this research deals with the development of an automated system for steel bridge defect detection system using a Multi-Directional Bicycle Robot. The training data has been acquired from actual bridges in Vietnam and validation is performed on data collected using Bicycle Robot from actual bridge located in Highway-80, Lovelock, Nevada, USA. A number of different proposed methods have been discussed in chapter 4. The final chapter of the dissertation will conclude the findings from the different parts and discuss ways of improving on the existing works in the near future

Ground Penetrating Radar-based Deterioration Assessment of Bridge Decks

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$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