Trend Deviation Analysis for Automated Detection of Defects in GPR Data for Road Condition Surveys

This paper presents a novel approach for automated detection of defects and structural changes in GPR data acquired in HMA (Hot Mix Asphalt) road surveys. Unlike the majority of existing approaches for road GPR data processing that are mainly used for extraction of layer profile information, the proposed method focuses on automated identification of significant deviations in subsurface structure and material properties. It is based on the detection of variations in intensity trends of longitudinal lines of interpolated B-scans that are characterized by deviations above a defined threshold. The outputs include mapped defects and deterioration areas together with the locations of detected changes in road structure design

Improvement of railway track diagnosis using Ground Penetrating Radar

The scope of this research is to provide an improved procedure for track railways monitoring, which should be an alternative to the one commonly used in Portugal and in others European countries. The traditionally approach for track maintenance corrects the track geometry withouth finding out the causes of geometric defects, namely the ones related with the foundation condition. The methodology proposed in this research aims to determining the real causes of track degradation through the application of a non-destructive equipment, the Ground Penetrating Radar (GPR). The GPR is widely used for road applications while for railways applications it is still under development. Among its advantages, it enables performing measurements in a continuous way, reducing traffic interruptions, and it allows for subsurface layers characterisation in terms of layers thicknesses. On the other hand, as its application is still quite recent in this field, special care is still needed before, during and after tests performance. In particular, for GPR data intepretation, a calibration study for track materials characterisation is always needed. In situ test pits and laboratory tests are performed and presented in this thesis. The proposed methodology consists in correlating track geometric parameters with GPR data. It intends to provide an efficient tool for supporting maintenance decisions at network level. In this way, it aims to contribute for the identification of crtical areas and for prediction of track defects, in a more efficient way comparing to the methodology currently performed

Improvement of railway track diagnosis using Ground Penetrating Radar

The scope of this research is to provide an improved procedure for track railways monitoring, which should be an alternative to the one commonly used in Portugal and in others European countries. The traditionally approach for track maintenance corrects the track geometry withouth finding out the causes of geometric defects, namely the ones related with the foundation condition. The methodology proposed in this research aims to determining the real causes of track degradation through the application of a non-destructive equipment, the Ground Penetrating Radar (GPR). The GPR is widely used for road applications while for railways applications it is still under development. Among its advantages, it enables performing measurements in a continuous way, reducing traffic interruptions, and it allows for subsurface layers characterisation in terms of layers thicknesses. On the other hand, as its application is still quite recent in this field, special care is still needed before, during and after tests performance. In particular, for GPR data intepretation, a calibration study for track materials characterisation is always needed. In situ test pits and laboratory tests are performed and presented in this thesis. The proposed methodology consists in correlating track geometric parameters with GPR data. It intends to provide an efficient tool for supporting maintenance decisions at network level. In this way, it aims to contribute for the identification of crtical areas and for prediction of track defects, in a more efficient way comparing to the methodology currently performed

Condition Assessment of Concrete Bridge Decks Using Ground and Airborne Infrared Thermography

Applications of nondestructive testing (NDT) technologies have shown promise in assessing the condition of existing concrete bridges. Infrared thermography (IRT) has gradually gained wider acceptance as a NDT and evaluation tool in the civil engineering field. The high capability of IRT in detecting subsurface delamination, commercial availability of infrared cameras, lower cost compared with other technologies, speed of data collection, and remote sensing are some of the expected benefits of applying this technique in bridge deck inspection practices. The research conducted in this thesis aims at developing a rational condition assessment system for concrete bridge decks based on IRT technology, and automating its analysis process in order to add this invaluable technique to the bridge inspector’s tool box. Ground penetrating radar (GPR) has also been vastly recognized as a NDT technique capable of evaluating the potential of active corrosion. Therefore, integrating IRT and GPR results in this research provides more precise assessments of bridge deck conditions. In addition, the research aims to establish a unique link between NDT technologies and inspector findings by developing a novel bridge deck condition rating index (BDCI). The proposed procedure captures the integrated results of IRT and GPR techniques, along with visual inspection judgements, thus overcoming the inherent scientific uncertainties of this process. Finally, the research aims to explore the potential application of unmanned aerial vehicle (UAV) infrared thermography for detecting hidden defects in concrete bridge decks. The NDT work in this thesis was conducted on full-scale deteriorated reinforced concrete bridge decks located in Montreal, Quebec and London, Ontario. The proposed models have been validated through various case studies. IRT, either from the ground or by utilizing a UAV with high-resolution thermal infrared imagery, was found to be an appropriate technology for inspecting and precisely detecting subsurface anomalies in concrete bridge decks. The proposed analysis produced thermal mosaic maps from the individual IR images. The k-means clustering classification technique was utilized to segment the mosaics and identify objective thresholds and, hence, to delineate different categories of delamination severity in the entire bridge decks. The proposed integration methodology of NDT technologies and visual inspection results provided more reliable BDCI. The information that was sought to identify the parameters affecting the integration process was gathered from bridge engineers with extensive experience and intuition. The analysis process utilized the fuzzy set theory to account for uncertainties and imprecision in the measurements of bridge deck defects detected by IRT and GPR testing along with bridge inspector observations. The developed system and models should stimulate wider acceptance of IRT as a rapid, systematic and cost-effective evaluation technique for detecting bridge deck delaminations. The proposed combination of IRT and GPR results should expand their correlative use in bridge deck inspection. Integrating the proposed BDCI procedure with existing bridge management systems can provide a detailed and timely picture of bridge health, thus helping transportation agencies in identifying critical deficiencies at various service life stages. Consequently, this can yield sizeable reductions in bridge inspection costs, effective allocation of limited maintenance and repair funds, and promote the safety, mobility, longevity, and reliability of our highway transportation assets

Nondestructive Evaluation of Structural Defects in Concrete Slabs

Nondestructive testing (NDT) is a reliable method for determining the structural integrity of new and old construction and understanding the condition of structural defects. It is widely acknowledged that the interpretation of nondestructive evaluation (NDE) has a significant impact on the reliability and consistency of this technology. However, NDT data acquisition remains subjective and heavily dependent on the operator’s knowledge and expertise. As a result, there are still issues to be resolved regarding the imaging and diagnostic procedures for NDT-based concrete inspection. NDT methods such as ground penetrating radar (GPR), and impact-echo (IE), have been extensively used to inspect concrete bridges for damage and deterioration. In this study, concrete slabs were investigated in a laboratory setting with simulated structural defects such as corrosion, delamination, honeycomb, and voids. To validate the accuracy and robustness of the two NDT methods, defects were embedded closely (ranging from 1 to 5 inches) and superimposed in two different layers for the same specimen. GPR B-scan (line scan) radargrams demonstrated hyperbolic shapes for voids and corroded rebars, whereas delamination was identified by a hollow strip pattern. GPR C-scan (area scan) data was used to calculate the size (length, width, and thickness) of delamination simulated by polystyrene sheets. The results showed an accuracy of 80% in predicting the thickness of delamination. GPR, however, did not detect delamination for objects smaller than 8 inches in size due to the dense meshing. The amplitude data from GPR A-scans (amplitude vs depth) were used to calculate the depth of a defect. Statistical measures exhibit a high degree of agreement between the depth measured by GPR and the actual depth of a defect. The IE method uses elastic waves to propagate through concrete for identifying the embedded defects. The interpretation of IE data involved post-processing of reflected signals in the time-domain and frequency-domain modes. The simulated structural defects did not show any discernible trend in time-domain data. However, defects were detected using IE thickness data from the peak frequency spectrum. The frequency-domain IE data was validated by comparing it to GPR data for similar concrete specimens

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

Non-destructive Testing in Civil Engineering

This Special Issue, entitled “Non-Destructive Testing in Civil Engineering”, aims to present to interested researchers and engineers the latest achievements in the field of new research methods, as well as the original results of scientific research carried out with their use—not only in laboratory conditions but also in selected case studies. The articles published in this Special Issue are theoretical–experimental and experimental, and also show the practical nature of the research. They are grouped by topic, and the main content of each article is briefly discussed for your convenience. These articles extend the knowledge in the field of non-destructive testing in civil engineering with regard to new and improved non-destructive testing (NDT) methods, their complementary application, and also the analysis of their results—including the use of sophisticated mathematical algorithms and artificial intelligence, as well as the diagnostics of materials, components, structures, entire buildings, and interesting case studies

Context-Enabled Visualization Strategies for Automation Enabled Human-in-the-loop Inspection Systems to Enhance the Situation Awareness of Windstorm Risk Engineers

Insurance loss prevention survey, specifically windstorm risk inspection survey is the process of investigating potential damages associated with a building or structure in the event of an extreme weather condition such as a hurricane or tornado. Traditionally, the risk inspection process is highly subjective and depends on the skills of the engineer performing it. This dissertation investigates the sensemaking process of risk engineers while performing risk inspection with special focus on various factors influencing it. This research then investigates how context-based visualizations strategies enhance the situation awareness and performance of windstorm risk engineers. An initial study investigated the sensemaking process and situation awareness requirements of the windstorm risk engineers. The data frame theory of sensemaking was used as the framework to carry out this study. Ten windstorm risk engineers were interviewed, and the data collected were analyzed following an inductive thematic approach. The themes emerged from the data explained the sensemaking process of risk engineers, the process of making sense of contradicting information, importance of their experience level, internal and external biases influencing the inspection process, difficulty developing mental models, and potential technology interventions. More recently human in the loop systems such as drones have been used to improve the efficiency of windstorm risk inspection. This study provides recommendations to guide the design of such systems to support the sensemaking process and situation awareness of windstorm visual risk inspection. The second study investigated the effect of context-based visualization strategies to enhance the situation awareness of the windstorm risk engineers. More specifically, the study investigated how different types of information contribute towards the three levels of situation awareness. Following a between subjects study design 65 civil/construction engineering students completed this study. A checklist based and predictive display based decision aids were tested and found to be effective in supporting the situation awareness requirements as well as performance of windstorm risk engineers. However, the predictive display only helped with certain tasks like understanding the interaction among different components on the rooftop. For remaining tasks, checklist alone was sufficient. Moreover, the decision aids did not place any additional cognitive demand on the participants. This study helped us understand the advantages and disadvantages of the decision aids tested. The final study evaluated the transfer of training effect of the checklist and predictive display based decision aids. After one week of the previous study, participants completed a follow-up study without any decision aids. The performance and situation awareness of participants in the checklist and predictive display group did not change significantly from first trial to second trial. However, the performance and situation awareness of participants in the control condition improved significantly in the second trial. They attributed this to their exposure to SAGAT questionnaire in the first study. They knew what issues to look for and what tasks need to be completed in the simulation. The confounding effect of SAGAT questionnaires needs to be studied in future research efforts

Evaluation of surface defect detection in reinforced concrete bridge decks using terrestrial LiDAR

Routine bridge inspections require labor intensive and highly subjective visual interpretation to determine bridge deck surface condition. Light Detection and Ranging (LiDAR) a relatively new class of survey instrument has become a popular and increasingly used technology for providing as-built and inventory data in civil applications. While an increasing number of private and governmental agencies possess terrestrial and mobile LiDAR systems, an understanding of the technology’s capabilities and potential applications continues to evolve. LiDAR is a line-of-sight instrument and as such, care must be taken when establishing scan locations and resolution to allow the capture of data at an adequate resolution for defining features that contribute to the analysis of bridge deck surface condition. Information such as the location, area, and volume of spalling on deck surfaces, undersides, and support columns can be derived from properly collected LiDAR point clouds. The LiDAR point clouds contain information that can provide quantitative surface condition information, resulting in more accurate structural health monitoring. LiDAR scans were collected at three study bridges, each of which displayed a varying degree of degradation. A variety of commercially available analysis tools and an independently developed algorithm written in ArcGIS Python (ArcPy) were used to locate and quantify surface defects such as location, volume, and area of spalls. The results were visual and numerically displayed in a user-friendly web-based decision support tool integrating prior bridge condition metrics for comparison. LiDAR data processing procedures along with strengths and limitations of point clouds for defining features useful for assessing bridge deck condition are discussed. Point cloud density and incidence angle are two attributes that must be managed carefully to ensure data collected are of high quality and useful for bridge condition evaluation. When collected properly to ensure effective evaluation of bridge surface condition, LiDAR data can be analyzed to provide a useful data set from which to derive bridge deck condition information