Concrete Crack Identification Using a UAV Incorporating Hybrid Image Processing

Crack assessment is an essential process in the maintenance of concrete structures. In general, concrete cracks are inspected by manual visual observation of the surface, which is intrinsically subjective as it depends on the experience of inspectors. Further, it is time-consuming, expensive, and often unsafe when inaccessible structural members are to be assessed. Unmanned aerial vehicle (UAV) technologies combined with digital image processing have recently been applied to crack assessment to overcome the drawbacks of manual visual inspection. However, identification of crack information in terms of width and length has not been fully explored in the UAV-based applications, because of the absence of distance measurement and tailored image processing. This paper presents a crack identification strategy that combines hybrid image processing with UAV technology. Equipped with a camera, an ultrasonic displacement sensor, and a WiFi module, the system provides the image of cracks and the associated working distance from a target structure on demand. The obtained information is subsequently processed by hybrid image binarization to estimate the crack width accurately while minimizing the loss of the crack length information. The proposed system has shown to successfully measure cracks thicker than 0.1 mm with the maximum length estimation error of 7.3%

Transfer Learning-Based Crack Detection by Autonomous UAVs

Unmanned Aerial Vehicles (UAVs) have recently shown great performance collecting visual data through autonomous exploration and mapping in building inspection. Yet, the number of studies is limited considering the post processing of the data and its integration with autonomous UAVs. These will enable huge steps onward into full automation of building inspection. In this regard, this work presents a decision making tool for revisiting tasks in visual building inspection by autonomous UAVs. The tool is an implementation of fine-tuning a pretrained Convolutional Neural Network (CNN) for surface crack detection. It offers an optional mechanism for task planning of revisiting pinpoint locations during inspection. It is integrated to a quadrotor UAV system that can autonomously navigate in GPS-denied environments. The UAV is equipped with onboard sensors and computers for autonomous localization, mapping and motion planning. The integrated system is tested through simulations and real-world experiments. The results show that the system achieves crack detection and autonomous navigation in GPS-denied environments for building inspection

Evaluation of Crack-Repairing Performance in Concrete Using Surface Waves

The purpose of this study is to investigate the applicability of surface-wave techniques for the evaluation of the crack-repairing performance of an epoxy injection method in concrete. In this study, box-shaped concrete specimens with four different crack depths were made with identical mix proportions. The specimens with different crack depths were completely repaired using the same epoxy injection method. The spectral energy transmission ratio of surface waves is used as an index to differentiate the effects of crack depth and crack-repairing performance. The decrease of spectral energy transmission ratio in accordance with the increase of crack depth was identified before repairing. Furthermore, the spectral energy transmission ratio increased after the crack-repairing process in all specimens. The spectral energy transmission ratio is considered as a great indicator for estimating the crack-repairing performance of the epoxy injection method; the ratio was recovered up to almost 95% of the uncracked condition

Building crack monitoring based on digital image processing

Building crack monitoring is of great value to the judgment of building safety. In this study, the digital image processing technology was studied and applied to the monitoring of building cracks. Crack images were collected by CCD camera, and then operations such as graying, correction, denoising and segmentation were carried out to obtain clear crack images. The obtained images are processed morphologically to further improve the quality. Finally, the width and length of cracks are calculated. In the case analysis, the results of 15 cracks measured by microscope were taken as the standards and compared with the calculated results. The results showed that the results calculated in this study and the manual measurement results differed little, and the average error of the width and length is 0.021 mm and 0.024 mm respectively, which suggested that the method proposed had high reliability. The findings of this study provides a new idea for the further development of building crack monitoring field, which is conducive to the accurate assessment of building safety

Detection of exposed steel rebars based on deep-learning techniques and unmanned aerial vehicles

In recent years deep-learning techniques have been developed and applied to inspect cracks in RC structures. The accuracy of these techniques leads to believe that they may also be applied to the identification of other pathologies. This article proposes a technique for automated detection of exposed steel rebars. The tools developed rely on convolutional neural networks (CNNs) based on transfer-learning using AlexNet. Experiments were conducted in large-scale structures to assess the efficiency of the method. To circumvent limitations on the proximity access to structures as large as the ones used in the experiments, as well as increase cost efficiency, the image capture was performed using an unmanned aerial system (UAS). The final goal of the proposed methodology is to generate orthomosaic maps of the pathologies or structure 3D models with superimposed pathologies. The results obtained are promising, confirming the high adaptability of CNN based methodologies for structural inspection.This work was financially supported by: Base Funding - UIDB/04708/2020 and Programmatic Funding - UIDP/04708/2020 of the CONSTRUCT - Instituto de I&D em Estruturas e Construções funded by national funds through the FCT/MCTES (PIDDAC). Additionally, the author Rafael Cabral acknowledges the support provided by the doctoral grant UI/BD/150970/2021 - Portuguese Science Foundation, FCT/MCTES.info:eu-repo/semantics/publishedVersio

EXPEDITIONARY LOGISTICS: A LOW-COST, DEPLOYABLE, UNMANNED AERIAL SYSTEM FOR AIRFIELD DAMAGE ASSESSMENT

Airfield Damage Repair (ADR) is among the most important expeditionary activities for our military. The goal of ADR is to restore a damaged airfield to operational status as quickly as possible. Before the process of ADR can begin, however, the damage to the airfield needs to be assessed. As a result, Airfield Damage Assessment (ADA) has received considerable attention. Often in a damaged airfield, there is an expectation of unexploded ordnance, which makes ADA a slow, difficult, and dangerous process. For this reason, it is best to make ADA completely unmanned and automated. Additionally, ADA needs to be executed as quickly as possible so that ADR can begin and the airfield restored to a usable condition. Among other modalities, tower-based monitoring and remote sensor systems are often used for ADA. There is now an opportunity to investigate the use of commercial-off-the-shelf, low-cost, automated sensor systems for automatic damage detection. By developing a combination of ground-based and Unmanned Aerial Vehicle sensor systems, we demonstrate the completion of ADA in a safe, efficient, and cost-effective manner.http://archive.org/details/expeditionarylog1094561346Outstanding ThesisLieutenant, United States NavyApproved for public release; distribution is unlimited

An efficient and resilient digital-twin communication framework for smart bridge structural survey and maintenance

A bridge digital twin (DT) is expected to be updated in near real time during inspection and monitoring but is usually subject to massive heterogeneous data and communication constraints. This work proposes an efficient framework for a bridge DT with decreased communication complexity to achieve updates synchronously and provide feedback to the physical bridge in time. The integrated edge computing and non-cellular long-distance wireless communication enable DT resilience when cloud servers become unresponsive due to the loss of internet connection. This framework is validated by different scenarios for DTs in support of bridge inspection and monitoring. It is demonstrated that the framework can enable dynamic interaction between on-site inspection and online bridge DT during the survey as well as knowledge transfer among different sectors in time. It can also support local decision-making on a single bridge as well as regional dynamic coordination for multiple bridges without cloud-server involvement

Advancements in Building Deconstruction: Examining the Role of Drone Technology and Building Information Modelling

Deconstructing a building with the help of drones and BIM (building information modelling) is becoming increasingly common as a more efficient, eco-friendly, and affordable alternative to the traditional techniques of building disassembly. This paper presents a systematic review following the methodology of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to investigate the role of drone technology and BIM in building deconstruction. A total of 10 studies were identified based on the integration of drone technology with BIM, all of which proved promising in enhancing the process of building deconstruction. The analysis of the 35 and 3 non-academic selected data reveals several key findings. Firstly, BIM is not commonly used in deconstruction or demolition processes, particularly in managing fixtures and fittings of buildings. Secondly, the adoption of deconstruction-oriented design methods and the use of drone technology can significantly reduce the negative environmental impacts of building demolition waste. Lastly, the limited implementation of design for deconstruction practices in the construction industry hinders the realisation of environmental, social, and economic benefits associated with this approach. Overall, this systematic review highlights the potential of drone technology and BIM in improving building deconstruction practices, while also identifying knowledge gaps and areas for further research and development on this topic

UAV-Based Bridge Inspection and Computational Simulations

The use of Unmanned Aerial Vehicles (UAV), commonly known as drones, has significantly increased in the field of civil engineering due to the poor condition of the United States’ infrastructure. The American Society of Civil Engineers (ASCE) recently reported that more than 9.1% of the United States’ bridges were structurally deficient and required attention and maintenance to ensure appropriate structural performance. Meanwhile, current practices are expensive and unsafe for bridge inspectors, requiring innovative and safer methods for the study of bridges. The goal of this paper was to identify better techniques to not only inspect, quantify, and determine the effect of damage on bridges to minimize the risk for inspectors, but also to determine their live-load performance using UAV-based computational simulation updating techniques. To accomplish the objective, an extensive literature review and survey to state departments of transportation (DOTs) was conducted to gain technical knowledge on current UAV-based inspection practices. To evaluate the efficiency of the UAV, the Keystone Interchange Bridges (i.e., Keystone Wye timber arch bridge and timber girder bridge) in the Black Hills National Forest near the city of Keystone, South Dakota (SD), were studied. To provide a more systematical and efficient UAV-enabled bride inspection method, a five-stage recommended bridge inspection protocol was developed. A UAV-image-based bridge damage quantification protocol involving image quality assessment and image-based damage measurement was recommended. Finally, using the damage information form the inspection and quantification of the bridges, a Finite Element (FE) model to determine the live-load performance of the Keystone Wye timber arch bridge in terms of Distribution Factors (DF) and Load Rating Factors (RF) was developed. It was concluded that the UAV served as an effective tool to supplement current inspection practices and provide damage information that can be used to update FE models to rationally estimate bridge performance