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Visual inspections are an important component of bridge monitoring efforts. There are a variety of emerging technologies and novel methods that can be used in the inspection process to improve data collection, enhance inspector safety, and reduce the number of potential road closures. Numerous research efforts have been undertaken with the goal of automating significant portions (or the entire) of the inspection process. However, from an industry standpoint, shifting to completely automated inspection procedures from the current manual approach is unlikely to occur in a single step. Furthermore, when these technologies are adopted, a disconnect typically exists between the expected and actual value generated, impeding consistent innovation and widespread adoption in the industry. There is a continual call for more real-world structural health monitoring (SHM) use cases to be documented which can demonstrate its effectiveness in bridge management; and frameworks that can evaluate the value of SHM in a systematic manner have not been widely studied. As a result, this thesis first presents a SHM case study which successfully compares in-service temperature data from a deployment on Waterloo Bridge, UK to international design models. In general, the monitoring programme provided useful information about the structure's thermal behaviour during operation. Secondly, a value rating methodology for SHM deployments was applied to three bridge structures in the UK via a series of semi-structured interviews. The investigated methodology was capable of providing a reasonable representation of the real value generated for the asset owner, but it may not fully capture the benefit of 'model validation' deployments. The viability of a remote inspection method in which inspectors solely use digital images to rate defects has received little attention. Therefore, in the second half of the thesis, an experimental trial is presented which investigates the feasibility of such a method. Statistical analysis shows that aggregated defects rated by off-site inspectors are perceived to be more severe and of a higher priority than those rated by on-site inspectors. The findings of this study also indicate that remote inspections may be no less subjective than traditional on-site inspections. Lastly, a schema that may facilitate a photo-based remote inspection process is developed. Key stakeholders are identified and the overall system architecture of the schema is described. While focus was placed on developing a framework that would be more easily deployed in day-to-day operations, it is viewed by the author that the resulting framework could easily be adapted to implement newer technologies.

Manual surface inspection methods performed by quality inspectors do not satisfy the continuously increasing quality standards of industrial manufacturing processes. Machine vision provides a solution by using an automated visual inspection (AVI) system to perform quality inspection and remove defective products. Numerous studies and works have been conducted on surface inspection algorithms. With the advent of deep learning, a number of new algorithms have been developed for better inspection. In this paper, the state-of-the-art in surface defect inspection using deep learning is presented. In particular, we focus on the inspection of industrial products in semiconductor, steel, and fabric manufacturing processes. This work makes three contributions. First, we present the prior literature reviews on vision-based surface defect inspection and analyze the recent AVI-related hardware and software. Second, we review traditional surface defect inspection algorithms including statistical methods, spectral methods, model-based methods, and learning-based methods. Third, we investigate recent advances in deep learning-based inspection algorithms and present their applications in the steel, fabric, and semiconductor industries. Furthermore, we provide information on publicly available datasets containing surface image samples to facilitate the research on deep learning-based surface inspection.

Image-based inspection technologies involving various sensors and unmanned aerial vehicles are widely used for facility inspections. The level of data analysis technology required to process the acquired data algorithmically (e.g., image processing and machine learning) is also increasing. However, compared with their development rate, the applicability of new inspection technologies to actual bridges is low. In addition, only individual technologies (for inspecting specific deteriorations) are being developed; integrated inspection systems have been neglected. In this study, the bottom-up method (which systematizes the applications of a specific technology) is avoided; instead, several technologies are summarized and a system of preliminary frameworks is established using a top-down method, and the applicability of each technology is verified in a testbed. To this end, the utility of the initially constructed technical system was assessed for two bridges; then, a strong utility technology was selected and applied to an offshore bridge under extreme conditions. The data obtained from the inspection were accumulated in a database, and a 3D-type external inspection map was produced and applied in the subsequent inspection via virtual and augmented reality equipment. Through the system, it was possible to obtain cost-effective and objective bridge inspection images in extreme environments, and the applicability of various technologies was verified.

In June, Mitie announced the launch of a new drone service. The technology enables improved property surveying, efficient thermal mapping and the inspection of high-rise buildings -- previously unreachable. The drone inspection service offers unrivalled benefits in terms of quality of the inspection, cost reduction and instant reporting. The drone's hi-resolution 4K camera boosts the accuracy of each survey giving facilities and property managers a more detailed inspection service. With the ability to reach 400ft, previously inaccessible places are reachable from the ground and without the need for specialist equipment. Footage and imagery of any areas of concern are immediately transmitted to the operative's smartphone or tablet for inspection.

Manual visual inspection of civil infrastructure is high-risk, subjective, and time-consuming. The success of deep learning and the proliferation of low-cost consumer robots has spurred rapid growth in research and application of autonomous inspections. The major components of autonomous inspection include data acquisition, data processing, and decision making, which are usually studied independently. However, for robust real-world applicability, these three aspects of the overall process need to be addressed concurrently with end-to-end testing, incorporating scenarios such as variations in structure type, color, damage level, camera distance, view angle, lighting, etc. Developing real-world datasets that span all these scenarios is nearly impossible. In this paper, we propose a framework to create a virtual visual inspection testbed using 3D synthetic environments that can enable end-to-end testing of autonomous inspection strategies. To populate the 3D synthetic environment with virtual damaged buildings, we propose the use of a non-linear finite element model to inform the realistic and automated visual rendering of different damage types, the damage state, and the material textures of what are termed herein physics-based graphics models (PBGMs). To demonstrate the benefits of the autonomous inspection testbed, three experiments are conducted with models of earthquake damaged reinforced concrete buildings. First, we implement the proposed framework to generate a new large-scale annotated benchmark dataset for post-earthquake inspections of buildings termed QuakeCity. Second, we demonstrate the improved performance of deep learning models trained using the QuakeCity dataset for inference on real data. Finally, a comparison of deep learning-based damage state estimation for different data acquisition strategies is carried out. The results demonstrate the use of PBGMs as an effective testbed for the development and validation of strategies for autonomous vision-based inspections of civil infrastructure.

This paper presents the design, modeling and control of a fully actuated aerial robot for infrastructure contact inspection as well as its localization system. Health assessment of transport infrastructure involves measurements with sensors in contact with the bridge and tunnel surfaces and the installation of monitoring sensing devices at specific points. The design of the aerial robot presented in the paper includes a 3DoF lightweight arm with a sensorized passive joint which can measure the contact force to regulate the force applied with the sensor on the structure. The aerial platform has been designed with tilted propellers to be fully actuated, achieving independent attitude and position control. It also mounts a “docking gear” to establish full contact with the infrastructure during the inspection, minimizing the measurement errors derived from the motion of the aerial platform and allowing full contact with the surface regardless of its condition (smooth, rough, ...). The localization system of the aerial robot uses multi-sensor fusion of the measurements of a topographic laser sensor on the ground and a tracking camera and inertial sensors on-board the aerial robot, to be able to fly under the bridge deck or close to the bridge pillars where GNSS satellite signals are not available. The paper also presents the modeling and control of the aerial robot. Validation experiments of the localization system and the control system, and with the aerial robot inspecting a real bridge are also included.

Using aerial platforms for Non-Destructive Inspection (NDI) of large and complex structures is a growing field of interest in various industries. Infrastructures such as: buildings, bridges, oil and gas, etc. refineries require regular and extensive inspections. The inspection reports are used to plan and perform required maintenance, ensuring their structural health and the safety of the workers. However, performing these inspections can be challenging due to the size of the facility, the lack of easy access, the health risks for the inspectors, or several other reasons, which has convinced companies to invest more in drones as an alternative solution to overcome these challenges. The autonomous nature of drones can assist companies in reducing inspection time and cost. Moreover, the employment of drones can lower the number of required personnel for inspection and can increase personnel safety. Finally, drones can provide a safe and reliable solution for inspecting hard-to-reach or hazardous areas. Despite the recent developments in drone-based NDI to reliably detect defects, several limitations and challenges still need to be addressed. In this paper, a brief review of the history of unmanned aerial vehicles, along with a comprehensive review of studies focused on UAV-based NDI of industrial and commercial facilities, are provided. Moreover, the benefits of using drones in inspections as an alternative to conventional methods are discussed, along with the challenges and open problems of employing drones in industrial inspections, are explored. Finally, some of our case studies conducted in different industrial fields in the field of Non-Destructive Inspection are presented.