Bridge inspection and infrastructure inspection are critical steps in the lifecycle of the built environment. Emerging technologies and data are driving factors which are disrupting the traditional processes for conducting these inspections. Because inspections are mainly conducted visually by human inspectors, this paper focuses on improving the visual inspection process with data-driven approaches. Data driven approaches, however, require significant data, which was sparse in the existing literature. Therefore, this research first examined the present state of the existing data in the research domain. We reviewed hundreds of image-based visual inspection papers which used machine learning to augment the inspection process and from this, we compiled a comprehensive catalog of over forty available datasets in the literature and identified promising, emerging techniques and trends in the field. Based on our findings in our review we contributed six significant datasets to target gaps in data in the field. The six datasets comprised of structural material segmentation, corrosion condition state segmentation, crack detection, structural detail detection, and bearing condition state classification. The contributed datasets used novel annotation guidelines and benefitted from a novel semi-automated annotation process for both object detection and pixel-level detection models. Using the data obtained from our collected sources, task-appropriate deep learning models were trained. From these datasets and models, we developed a change detection algorithm to monitor damage evolution between two inspection videos and trained a GAN-Inversion model which generated hyper-realistic synthetic bridge inspection image data and could forecast a future deterioration state of an existing bridge element. While the application of machine learning techniques in civil engineering is not wide-spread yet, this research provides impactful contribution which demonstrates the advantages that data driven sciences can provide to more economically and efficiently inspect structures, catalog deterioration, and forecast potential outcomes. / Doctor of Philosophy / Bridge inspection and infrastructure inspection are critical steps in the lifecycle of the built environment. Emerging technologies and data are driving factors which are disrupting the traditional processes for conducting these inspections. Because inspections are mainly conducted visually by human inspectors, this paper focuses on improving the visual inspection process with data-driven approaches. Data driven approaches, however, require significant data, which was sparse in the existing literature. Therefore, this research first examined the present state of the existing data in the research domain. We reviewed hundreds of image-based visual inspection papers which used machine learning to augment the inspection process and from this, we compiled a comprehensive catalog of over forty available datasets in the literature and identified promising, emerging techniques and trends in the field. Based on our findings in our review we contributed six significant datasets to target gaps in data in the field. The six datasets comprised of structural material detection, corrosion condition state identification, crack detection, structural detail detection, and bearing condition state classification. The contributed datasets used novel labeling guidelines and benefitted from a novel semi-automated labeling process for the artificial intelligence models. Using the data obtained from our collected sources, task-appropriate artificial intelligence models were trained. From these datasets and models, we developed a change detection algorithm to monitor damage evolution between two inspection videos and trained a generative model which generated hyper-realistic synthetic bridge inspection image data and could forecast a future deterioration state of an existing bridge element. While the application of machine learning techniques in civil engineering is not widespread yet, this research provides impactful contribution which demonstrates the advantages that data driven sciences can provide to more economically and efficiently inspect structures, catalog deterioration, and forecast potential outcomes.

<p dir="ltr">Unmanned Aerial Systems (UAS) have the potential to drastically change how civil infrastructure is inspected, monitored, and managed. This innovative technology can ensure the inspector’s safety, provide additional inspection information, and reduce costs. However, a challenge arose as this industry expanded: a lack of standardized guidelines or minimum performance requirements to perform these operations. With no standard tests to verify UAS’ ability to conduct inspections and unknown detection capabilities, agencies are left to rely upon consultants’ or vendors’ promotional material and claims when considering UAS deployment. The following work proposes a series of performance-based assessments and procedural documentation to establish minimum standards for using UAS in bridge inspection applications. Through this work, the following performance-based tests have been developed: (1) a controlled environment simulating bridge geometries to assess the overall capability of a UAS used for bridge inspection [evaluation chamber], (2) an assessment of UAS performance under multiple environmental temperatures [environmental temperature chamber], (3) a UAS performance assessment under varying wind speeds [wind chamber], (4) a consolidated checklist compiling Federal Aviation Administration guidelines and best practices [flight checklist], (5) a field assessment of UAS under conditions analogous to on-site bridge inspection [practical test]. For infrastructure owners, embracing these performance-based assessments will help ensure that UAS meets a minimum level of performance and allow owners to verify and distinguish between various UAS used for bridge inspection. This work also discusses positive feedback from beta testing provided by industry and infrastructure owner representatives, showcasing the effectiveness of providing an authentic assessment of UAS bridge inspection capabilities. Future work encourages the wide implementation of this assessment program and encourages owners to refrain from using untested technology in the inspection of their infrastructure.</p>

<p dir="ltr">Bridge decks in Indiana face the brunt of the deterioration mechanisms associated with structural deficiencies. These deficiencies do not always present themselves in noticeable ways, however, their detection is imperative to the performance of the deck and the bridges’ overall health. The inspection of these bridge decks presents engineers with not only a timely, but dangerous process as maintenance of traffic (MOT) from the states’ department of transportation (DOT) is not a viable option for most inspections. This results in engineers taking an unnecessary risk to inspect these decks for deteriorations. The most detrimental of these structural deficiencies, delaminations, do not always result in visual confirmation. Leading to more time spent in the roadway trying to sound for these defects. This thesis introduces a state-of-the-art review of previous NDT studies in relation to bridge structures along with the validation of their results. Background information on all testing methods being evaluated will also be provided in this study. This thesis also presents an in depth investigation using multiple consultants and a variety of NDT methods to assess the viability of delamination detection in relation to these methods. These methods were verified through coring at select locations on the deck. This thesis then discusses the practical implications of these NDT methods that provide an accurate level of delamination detection on project and network level inspections.</p>

The aim of this thesis is to evaluate the real condition of reinforced concrete beam bridge located in Brno Jundrov. In order to evaluate this condition, there was made the main bridge inspection. Subsequently, a number of diagnostic methods were used, such as hardness tests, concrete stress strenght, ultrasonic pulse method, including their evaluation. In terms of the main inspection, the condition of construction of the bridge was evaluated as V - bad. We found out that the concrete from which the bridge is built of is in very good condition. The problem, however, is the condition of the reinforcement located on the bridge. Based on its condition there must be expected lifespan within the next 5-10 years.

The diploma thesis deals with the issue of bridge strustures, especially their diagnostics and evaluation. The work consists of two parts, theoretical and practical. The theoretical part deals with the basic description and division of bridge structures, brigde inspections and test methods, especially those that were used in the thesis during bridge inspections. The practical part includes a visual inspection of two bridges, their description, detection of faults and defects and their documentation. In the practical part, the basic maretial characteristics of concrete bridges were determined, the condition of structures was assessed and a proposal for the elimination of failures and defects was developed. In the end, the work deals with the evaluation of both bridges.

Adjacent precast, prestressed box beam bridges have a history of poor performance and have been observed to exhibit common types of deterioration including longitudinal cracking, concrete spalling, and deterioration of the concrete top flange. The nature of these types of deterioration leads to uncertainty of the extent and effect of deterioration on structural behavior. Due to limitations in previous research and understanding of the strength of deteriorated box beam bridges, conservative assumptions are being made for the assessment and load rating of these bridges. Furthermore, the design of new box beam bridges, which can offer an efficient and economical solution, is often discouraged due to poor past performance. Therefore, the objective of this research is to develop improved recommendations for the inspection, load rating, and design of adjacent box beam bridges. Through a series of bridge inspections, deteriorated box beams were identified and acquired for experimental testing. The extent of corrosion was determined through visual inspection, non-destructive evaluation, and destructive evaluation. Non-destructive tests (NDT) included the use of connectionless electrical pulse response analysis (CEPRA), ground penetrating radar (GPR), and half-cell potentials. The deteriorated capacity was determined through structural testing, and an analysis procedure was developed to estimate deteriorated behavior. A rehabilitation procedure was also developed to restore load transfer of adjacent beams in cases where shear key failures are suspected. Based on the understanding of deterioration developed through study of deteriorated adjacent box beam bridges, improved inspection and load rating procedure are provided along with design recommendations for the next generation of box beam bridges.

<p>Civil engineering structures must provide an adequate and safe performance during their time of service, and the owners of these structures must have a reliable inspection strategy to ensure time-dependent damage does not become excessive. Visual inspection is the first step in every structural inspection; however, many elements in the majority of structures are difficult to access and require specialized personal and equipment. In an attempt to reduce the risk of the inspector and the cost of additional equipment, the use of Unmanned Aircraft Systems (UAS) has been increasing in the last years. The absence of standards and regulations regarding the use of UAS in inspection of structures has allowed the market to widely advertise Unmanned Aerial Vehicles (UAV) without protocols or qualifications that prove their effectiveness, leaving the owners of the structures to solely rely on claims of the vendors before deciding which technology suits their particular inspection needs. Focusing primarily on bridge inspection, this research aimed to address the lack of performance-based evaluation and standards for UAS, developing a validation criterion to evaluate a given UAS based on a repeatable test that resembles typical conditions in a structure. </p><p><br></p><p>Current applications of UAS in inspection of structures along with its advantages and limitations were studied to determine the current status of UAS technologies. A maximum typical rotor-tip-to-rotor-tip distance of an UAV was determined based on typical UAVs used in bridge inspection, and two main parameters were found to be relevant when flying close to structures: proximity effects in the UAV and availability of visual line of sight. Distances where proximity effects are relevant were determined based on several field inspections and flights close to structures. In addition, the use of supplementary technologies such as Global Positioning System (GPS) and Inertial Measurement Units (IMU) was studied to understand their effect during inspection. </p><p><br></p><p>Following the analysis, the author introduces the idea of a series of obstacles and elements inside an enclosed space that resemble components of bridge structures to be inspected using UAVs, allowing repeatability of the test by controlling outside parameters such as lighting condition, wind, precipitation, temperature, and GPS signal. Using distances based on proximity effects, maximum typical rotor-tip-to-rotor-tip distance, and a gallery of bridges and situations when flying close to bridge structures, a final arrangement of elements is presented as the evaluation chamber. Components inside the evaluation chamber include both “real” steel and concrete specimens as well as those intended to simulate various geometric configurations on which other features are mounted. Pictures of damages of steel and concrete elements have been placed in the internal faces of the obstacles that can be assessed either in real-time flight or in post-processing work. A detailed comparison between the objectives of this research project and the results obtained by the evaluation chamber was performed using visual evaluation and resolution charts for the images obtained, the availability of visual line of sight during the test, and the absence of GPS signal.</p><p><br></p><p>From the comparison and analysis conducted and based on satisfactory flight results as images obtained during flights, the evaluation chamber is concluded to be a repeatable and reliable tool to apply to any UAS prior to inspect bridges and other structures, and the author recommends to refrain from conducting an inspection if the UAS does not comply with the minimum requirements presented in this research work. Additionally, this research provided a clearer understanding of the general phenomenon presented when UAVs approach structures and attempts to fill the gap of knowledge regarding minimum requirements and criterion for the use of UAS technologies in inspection of structures.</p>