Hyunjun Kim

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%.

Principles and Applications of Ultrasonic-Based Nondestructive Methods for Self-Healing in Cementitious Materials

Recently, self-healing technologies have emerged as a promising approach to extend the service life of social infrastructure in the field of concrete construction. However, current evaluations of the self-healing technologies developed for cementitious materials are mostly limited to lab-scale experiments to inspect changes in surface crack width (by optical microscopy) and permeability. Furthermore, there is a universal lack of unified test methods to assess the effectiveness of self-healing technologies. Particularly, with respect to the self-healing of concrete applied in actual construction, nondestructive test methods are required to avoid interrupting the use of the structures under evaluation. This paper presents a review of all existing research on the principles of ultrasonic test methods and case studies pertaining to self-healing concrete. The main objective of the study is to examine the applicability and limitation of various ultrasonic test methods in assessing the self-healing performance. Finally, future directions on the development of reliable assessment methods for self-healing cementitious materials are suggested.

Flood fragility analysis for bridges with multiple failure modes

Bridges are one of the most important infrastructure systems that provide public and economic bases for humankind. It is also widely known that bridges are exposed to a variety of flood-related risk factors such as bridge scour, structural deterioration, and debris accumulation, which can cause structural damage and even failure of bridges through a variety of failure modes. However, flood fragility has not received as much attention as seismic fragility despite the significant amount of damage and costs resulting from flood hazards. There have been few research efforts to estimate the flood fragility of bridges considering various flood-related factors and the corresponding failure modes. Therefore, this study proposes a new approach for bridge flood fragility analysis. To obtain accurate flood fragility estimates, reliability analysis is performed in conjunction with finite element analysis, which can sophisticatedly simulate the structural response of a bridge under a flood by accounting for flood-related risk factors. The proposed approach is applied to a numerical example of an actual bridge in Korea. Flood fragility curves accounting for multiple failure modes, including lack of pier ductility or pile ductility, pier rebar rupture, pile rupture, and deck loss, are derived and presented in this study.

Flood fragility analysis of bridge piers in consideration of debris impacts

Abstract This research developed a flood fragility curve of bridges considering the debris impacts. Damage and failures of civil infrastructure due to natural disasters can cause casualties as well as social and economic losses. Fragility analysis is an effective tool to help better understand the vulnerability of a structure to possible extreme events, such as earthquakes and floods. In particular, flood-induced failures of bridges are relatively common in Korea, because of the mountainous regions and summer concentrated rainfall. The main failure reasons during floods are reported to be debris impact and scour; however, research regarding debris impacts is considered challenging dueto various uncertainties that affect the failure probability. This study introduces a fragility analysis methodology forevaluating the structural vulnerability due to debris impacts during floods. The proposed method describes how theessential components in fragility analysis are considered, including limit-state function, intensity measure of the debrisimpact, and finite element model. A numerical example of the proposed fragility analysis is presented using a bridgepier system under a debris impact.

Crack and Noncrack Classification from Concrete Surface Images Using Machine Learning

In concrete structures, surface cracks are important indicators of structural durability and serviceability. Generally, concrete cracks are visually monitored by inspectors who record crack information such as the existence, location, and width. Manual visual inspection is often considered ineffective in terms of cost, safety, assessment accuracy, and reliability. Digital image processing has been introduced to more accurately obtain crack information from images. A critical challenge is to automatically identify cracks from an image containing actual cracks and crack-like noise patterns (e.g. dark shadows, stains, lumps, and holes), which are often seen in concrete structures. This article presents a methodology for identifying concrete cracks using machine learning. The method helps in determining the existence and location of cracks from surface images. The proposed approach is particularly designed for classifying cracks and noncrack noise patterns that are otherwise difficult to distinguish using existing image processing algorithms. In the training stage of the proposed approach, image binarization is used to extract crack candidate regions; subsequently, classification models are constructed based on speeded-up robust features and convolutional neural network. The obtained crack identification methods are quantitatively and qualitatively compared using new concrete surface images containing cracks and noncracks.

Multisensor fusion for system identification

System identification is a fundamental process for developing a numerical model of a physical structure. The system identification process typically involves in data acquisition; particularly in civil engineering applications accelerometers are preferred due to its cost-effectiveness, low noise, and installation convenience. Because the measured acceleration responses result in translational degrees of freedom (DOF) in the numerical model, moment-resisting structures such as beam and plate are not appropriately represented by the models. This study suggests a system identification process that considers both translational and rotational DOFs by using accelerometers and gyroscopes. The proposed approach suggests a systematic way of obtaining dynamic characteristics as well as flexibility matrix from two different measurements of acceleration and angular velocity. Numerical simulation and laboratory experiment are conducted to validate the efficacy of the proposed system identification process.