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

Close-Range Sensing and Data Fusion for Built Heritage Inspection and Monitoring - A Review

Built cultural heritage is under constant threat due to environmental pressures, anthropogenic damages, and interventions. Understanding the preservation state of monuments and historical structures, and the factors that alter their architectural and structural characteristics through time, is crucial for ensuring their protection. Therefore, inspection and monitoring techniques are essential for heritage preservation, as they enable knowledge about the altering factors that put built cultural heritage at risk, by recording their immediate effects on monuments and historic structures. Nondestructive evaluations with close-range sensing techniques play a crucial role in monitoring. However, data recorded by different sensors are frequently processed separately, which hinders integrated use, visualization, and interpretation. This article’s aim is twofold: i) to present an overview of close-range sensing techniques frequently applied to evaluate built heritage conditions, and ii) to review the progress made regarding the fusion of multi-sensor data recorded by them. Particular emphasis is given to the integration of data from metric surveying and from recording techniques that are traditionally non-metric. The article attempts to shed light on the problems of the individual and integrated use of image-based modeling, laser scanning, thermography, multispectral imaging, ground penetrating radar, and ultrasonic testing, giving heritage practitioners a point of reference for the successful implementation of multidisciplinary approaches for built cultural heritage scientific investigations

THE USE OF POINT CLOUD DATA TO SUPPORT CONCRETE BRIDGE DECK CONDITION ASSESSMENT

Bridge deck condition assessments are typically conducted through visual physical inspections, utilizing traditional contact sensors for Non-Destructive Evaluation techniques such as hammer Sounding and chain dragging which require the expertise of trained inspectors. However, the accuracy of these inspections is limited by the level of deterioration of the bridge deck, as the ability of the inspectors is proportional to the apparent level of damage. This study aims to improve the accuracy of bridge deck inspection processes by utilizing non-destructive evaluation techniques, including the analysis of point cloud data gathered via Light Detection and Ranging (LiDAR) as a geometry-capturing tool. The overall goal of this research is to evaluate and quantify the effectiveness and efficiency of LiDAR sensors in contributing to the suite of technologies available to perform bridge deck condition assessment. To achieve this, the research proposes to understand the deterioration pattern of New Jersey bridges, evaluate the results gathered from point cloud data collected on a full-scale bridge deck, quantify the information gained from deploying LiDAR on operating bridges in New Jersey, and investigate the costs related to current bridge condition assessment practices and the impact of incorporating the use of LiDAR sensors. Two data processing approaches were chosen to measure gross and fine dimensions of the evaluated bridge decks, resulting in an accuracy of 96% with respect to results gathered from inspection reports

Integrated Ground Penetrating Radar (GPR) imaging and characterization of glacial and periglacial environments

This PhD research is based on Ground Penetrating Radar (GPR) imaging and characterization of glacial and periglacial environments. Its main focus is the assessment of the physical meaning of electromagnetic (EM) facies of frozen materials, proving that a detailed analysis of the geophysical data and often the integration with other prospecting techniques is essential because inferences on some kind of facies could not be clearly unambiguous. Chapters 2 and 3 of this dissertation present the characterization of a high scattered facies within an ice body, which was proved to be not straightforwardly related to warm ice and the presence of liquid water, as usually occurs in GPR data. An investigation approach based on differential diagnosis of the information obtained by different techniques (as GPR, photogrammetry, geomorphology) was proposed, representing something completely new for geophysical applications. Once such a facies was related to englacial debris within the ice, GPR modelling and inversion were fundamental to provide a first quantification of the debris causing the high scattered zone through a scattering amplitude inversion approach based on the combined analysis of synthetic and field data. It resulted that just a percentage below 10% in volume can produce the high scattered facies imaged on GPR data. A second focus of the research, arising from the main one, gets the issue of the ambiguity of the interpretation, the integration of techniques and the role of debris in a glacial body for improving both the characterization of an Alpine glacier and the geometrical imaging of Antarctic environments. The outcome of these researches, which are still ongoing, points out the relation between some surficial structures and the subsurface, revealing much more complex settings than expected just from geomorphological analysis and local drilling. As a matter of fact, this research deepened the knowledge in the identification of peculiar EM facies, including dead ice patches, and morphologies which affect the occurrence of periglacial elements and mixed glacial and fluvio-glacial features. Such research allowed to develop dedicated and new methodology of data analysis, considering GPR attribute analysis, differential diagnosis and the scattering amplitude approach for GPR inversion. The outcomes reached through this research are innovative, as they open new research possibilities and define the road ahead not only for future GPR glaciological researches, but also for different practical applications.This PhD research is based on Ground Penetrating Radar (GPR) imaging and characterization of glacial and periglacial environments. Its main focus is the assessment of the physical meaning of electromagnetic (EM) facies of frozen materials, proving that a detailed analysis of the geophysical data and often the integration with other prospecting techniques is essential because inferences on some kind of facies could not be clearly unambiguous. Chapters 2 and 3 of this dissertation present the characterization of a high scattered facies within an ice body, which was proved to be not straightforwardly related to warm ice and the presence of liquid water, as usually occurs in GPR data. An investigation approach based on differential diagnosis of the information obtained by different techniques (as GPR, photogrammetry, geomorphology) was proposed, representing something completely new for geophysical applications. Once such a facies was related to englacial debris within the ice, GPR modelling and inversion were fundamental to provide a first quantification of the debris causing the high scattered zone through a scattering amplitude inversion approach based on the combined analysis of synthetic and field data. It resulted that just a percentage below 10% in volume can produce the high scattered facies imaged on GPR data. A second focus of the research, arising from the main one, gets the issue of the ambiguity of the interpretation, the integration of techniques and the role of debris in a glacial body for improving both the characterization of an Alpine glacier and the geometrical imaging of Antarctic environments. The outcome of these researches, which are still ongoing, points out the relation between some surficial structures and the subsurface, revealing much more complex settings than expected just from geomorphological analysis and local drilling. As a matter of fact, this research deepened the knowledge in the identification of peculiar EM facies, including dead ice patches, and morphologies which affect the occurrence of periglacial elements and mixed glacial and fluvio-glacial features. Such research allowed to develop dedicated and new methodology of data analysis, considering GPR attribute analysis, differential diagnosis and the scattering amplitude approach for GPR inversion. The outcomes reached through this research are innovative, as they open new research possibilities and define the road ahead not only for future GPR glaciological researches, but also for different practical applications

Advanced Techniques for Ground Penetrating Radar Imaging

Ground penetrating radar (GPR) has become one of the key technologies in subsurface sensing and, in general, in non-destructive testing (NDT), since it is able to detect both metallic and nonmetallic targets. GPR for NDT has been successfully introduced in a wide range of sectors, such as mining and geology, glaciology, civil engineering and civil works, archaeology, and security and defense. In recent decades, improvements in georeferencing and positioning systems have enabled the introduction of synthetic aperture radar (SAR) techniques in GPR systems, yielding GPR–SAR systems capable of providing high-resolution microwave images. In parallel, the radiofrequency front-end of GPR systems has been optimized in terms of compactness (e.g., smaller Tx/Rx antennas) and cost. These advances, combined with improvements in autonomous platforms, such as unmanned terrestrial and aerial vehicles, have fostered new fields of application for GPR, where fast and reliable detection capabilities are demanded. In addition, processing techniques have been improved, taking advantage of the research conducted in related fields like inverse scattering and imaging. As a result, novel and robust algorithms have been developed for clutter reduction, automatic target recognition, and efficient processing of large sets of measurements to enable real-time imaging, among others. This Special Issue provides an overview of the state of the art in GPR imaging, focusing on the latest advances from both hardware and software perspectives

State-of-research on performance indicators for bridge quality control and management

The present study provides a review of the most diffused technical and non-technical performance indicators adopted worldwide by infrastructure owners. This work, developed within the European COST Action TU 1406—“Quality specifications for roadway bridges, standardization at a European level,” aims to summarize the state-of-art maintenance scheduling practices adopted by bridge owners, mainly focusing on the identification and classification of the most diffused performance indicators (PIs). PIs are subdivided in technical and non-technical ones: for this latter subclass, PIs are classified in environmental, social and economic-targeted. The study aims to be a reference for researchers dealing with performance-based assessments and bridge maintenance and management practices.Peer ReviewedPostprint (published version

Evaluating Storm Sewer Pipe Condition Using Autonomous Drone Technology

The United States Air Force (USAF) owns a total of 30.9 million linear feet (LF) of storm sewer pipes valued at approximately $2.3B in its vast portfolio of built infrastructure. Current inventory records reveal that 78% of the inventory (24.1 million LF) is over 50 years old and will soon exceed its estimated service life. Additionally, the USAF depends on contract support while its business processes undervalue in-service evaluations from long-term funding plans. Ultimately, this disconnect negatively impacts infrastructure performance and overall strategic success, and the USAF risks making uninformed decisions in a fiscally constrained environment. This research presents a proof of concept effort to automate storm sewer evaluations for the USAF using unmanned ground vehicles and computer vision technology for autonomous defect detection. The results conceptually show that a low-cost autonomous system can be developed using commercial off the shelf (COTS) hardware and open-source software to quantify the condition of underground storm sewer pipes with an efficiency of 36%. While the results show that the prototype developed for this research is not sufficient for operational use, it does demonstrate that the USAF can leverage COTS systems in future AM strategies to improve asset visibility at a significantly lower cost.

Innovative Methods and Materials in Structural Health Monitoring of Civil Infrastructures

In the past, when elements in sructures were composed of perishable materials, such as wood, the maintenance of houses, bridges, etc., was considered of vital importance for their safe use and to preserve their efficiency. With the advent of materials such as reinforced concrete and steel, given their relatively long useful life, periodic and constant maintenance has often been considered a secondary concern. When it was realized that even for structures fabricated with these materials that the useful life has an end and that it was being approached, planning maintenance became an important and non-negligible aspect. Thus, the concept of structural health monitoring (SHM) was introduced, designed, and implemented as a multidisciplinary method. Computational mechanics, static and dynamic analysis of structures, electronics, sensors, and, recently, the Internet of Things (IoT) and artificial intelligence (AI) are required, but it is also important to consider new materials, especially those with intrinsic self-diagnosis characteristics, and to use measurement and survey methods typical of modern geomatics, such as satellite surveys and highly sophisticated laser tools

Early Crack Detection of Reinforced Concrete Structure Using Embedded Sensors

International audienceThe damage in reinforced concrete (RC) structures can be induced either by the dynamic or static load. The inspection technologies available today have difficulty in detecting slowly progressive, locally limited damage, especially in hard-to-reach areas in the superstructure. The four-point bending test on the benchmark RC structure was used as a test of the quality and sensitivity of the embedded sensors. It allowed assessment of whether any cracking and propagation that occurs with the embedded sensors can be detected. Various methods are used for the analysis of the ultrasonic signals. By determining the feature from the ultrasonic signals, the changes in the whole structure are evaluated. The structural degradation of the RC benchmark structure was tested using various non-destructive testing methods to obtain a comprehensive decision about structural condition. It is shown that the ultrasonic sensors can detect a crack with a probability of detection of 100%, also before it is visible by the naked eye and other techniques, even if the damage is not in the direct path of the ultrasonic wave. The obtained results confirmed that early crack detection is possible using the developed methodology based on embedded and external sensors and advanced signal processing