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Key information for the maintenance and diagnosis of structures including bridges can be obtained from the processing of digital images acquired by unmanned aerial vehicle (UAV). However, low-quality images caused by various problems such as UAV movement, inspection environment, and camera parameters can lead to inappropriate structural evaluation due to the difficulty of digital image processing. Therefore, an appropriate assessment method for image quality considering the deterioration of the inspection image in the structural inspection procedure is required. In this study, a new image quality assessment (IQA) using a convolutional neural network (CNN) is proposed in consideration of various degradation factors that may occur in the structure inspection image. The first stage presents a method to obtain consistent quality against various interference factors of deterioration that may occur in inspection images. Adjusting the camera parameters minimizes the degradation of the inspection image. Subsequently, low- and high-quality images are distinguished according to the proposed image acquisition method. The second stage is the classification of the inspection dataset using the CNN-based image quality classifier model through training of data classified according to their quality. Experimental validation of the proposed method shows that the results are similar to the Human Visual System (HVS), which means subjective quality classification, and that the inspection image can be classified with more accurate and shorter processing time.

Unmanned aerial vehicles (UAVs) have been increasingly utilized for facility safety inspections due to their superior safety, cost effectiveness, and inspection accuracy compared to traditional manpower-based methods. High-resolution images captured by UAVs directly contribute to identifying and quantifying structural defects on facility exteriors, making image quality a critical factor in achieving accurate results. However, motion blur induced by external factors such as vibration, low light conditions, and wind during UAV operation significantly degrades image quality, leading to inaccurate defect detection and quantification. To address this issue, this research proposes a deblurring network using a Generative Adversarial Network (GAN) to eliminate the motion blur effect in UAV images. The GAN-based motion deblur network represents an image inpainting method that leverages generative models to correct blurry artifacts, thereby generating clear images. Unlike previous studies, this proposed approach incorporates deblur and blur learning modules to realistically generate blur images required for training the generative models. The UAV images processed using the motion deblur network are evaluated using a quality assessment method based on local blur map and other well-known image quality assessment (IQA) metrics. Moreover, in the experiment of crack detection utilizing the object detection system, improved detection results are observed when using enhanced images. Overall, this research contributes to improving the quality and accuracy of facility safety inspections conducted with UAV-based inspections by effectively addressing the challenges associated with motion blur effects in UAV-captured images.

As technology advances, the use of unmanned aerial vehicles (UAVs) and image sensors for structural monitoring and diagnostics is becoming increasingly critical. This approach enables the efficient inspection and assessment of structural conditions. Furthermore, the integration of deep learning techniques has been proven to be highly effective in detecting damage from structural images, as demonstrated in our study. To enable effective learning by deep learning models, a substantial volume of data is crucial, but collecting appropriate instances of structural damage from real-world scenarios poses challenges and demands specialized knowledge, as well as significant time and resources for labeling. In this study, we propose a methodology that utilizes a generative adversarial network (GAN) for image-to-image translation, with the objective of generating synthetic structural damage data to augment the dataset. Initially, a GAN-based image generation model was trained using paired datasets. When provided with a mask image, this model generated an RGB image based on the annotations. The subsequent step generated domain-specific mask images, a critical task that improved the data augmentation process. These mask images were designed based on prior knowledge to suit the specific characteristics and requirements of the structural damage dataset. These generated masks were then used by the GAN model to produce new RGB image data incorporating various types of damage. In the experimental validation conducted across the three datasets to assess the image generation for data augmentation, our results demonstrated that the generated images closely resembled actual images while effectively conveying information about the newly introduced damage. Furthermore, the experimental validation of damage detection with augmented data entailed a comparative analysis between the performance achieved solely with the original dataset and that attained with the incorporation of additional augmented data. The results for damage detection consistently demonstrated that the utilization of augmented data enhanced performance when compared to relying solely on the original images.

According to data from the Ministry of Employment and Labor in Korea, a significant portion of fatal accidents on construction sites occur due to collisions between construction workers and equipment, with many of these collisions being attributed to worker negligence. This study introduces a method for accurately localizing construction equipment and workers on-site, delineating areas prone to collisions as ‘a danger area of a collision’, and defining collision risk states. Utilizing advanced deep learning models which specialize in object detection, video footage obtained from strategically placed closed-circuit television (CCTV) cameras across the construction site is analyzed. The positions of each detected object are determined using transformation or homography matrices representing the conversion relationship between a sufficiently flat reference plane and image coordinates. Additionally, ‘a danger area of a collision’ is proposed for evaluating equipment collision risk based on the moving equipment’s speed, and the validity of this area is verified. Through this, the paper presents a system designed to preemptively identify potential collision risks, particularly when workers are located within the ‘danger area of a collision’, thereby mitigating accident risks on construction sites.

Since all structures vibrate due to external loads, measuring and analyzing vibration data is a representative method of structural health monitoring. In this paper, we propose a non-contact cable estimation method using a vision sensor mounted on an unmanned aerial vehicle. A target cable among many cables can be identified through marker detection. In addition, the motion of the structure can be quickly captured using the extracted feature points. Although computer vision can be used to transform displacements of multiple axis, in this study, only the vertical displacement is considered to estimate tension. Finally, the cable tension can be estimated via the vibration method using the modal frequencies derived from the cable displacement. To verify the performance of the proposed method, lab-scale experiments were carried out and the results were compared with the conventional method based on the accelerometer. The proposed method showed a 3.54% error compared with the existing method and confirmed that the cable tension force can be estimated quickly at low cost.

Crack width estimation through remote imaging and digital image processing are the leading diagnostic technologies used for infrastructure. However, owing to limitations in the accuracy of these techniques, alternative methods to detect target crack widths larger than the diagnostic standard are being developed. In this study, the coordinates of fiducial markers were extracted using the Harris corner detector, and fine crack widths of approximately 0.3 mm or less were estimated using the ground sample distance, varied numbers of adjusted pixels, and varied focal lengths. The results of these examinations were compared with the measured values. As a result, it was confirmed that the number of pixels rather than the ground sample distance was the dominant factor affecting the accuracy of the estimated value, and the accuracy could be improved by adjusting the number of pixels, increasing the focal length, and ensuring an effective object distance. The results showed the feasibility of estimating fine crack widths with a high accuracy through the fiducial-marker-based method under the aforementioned conditions. In the future, if the number of crack samples is increased and more detailed focal length and object distance data are supported, this method may provide estimations with a higher accuracy.

As the number of old bridges increases, the number of bridges with structural defects is also increasing. Timely inspection and maintenance of bridges are required because structural degradation is accelerated after bridge damage. Recently, in the field of structural health monitoring, a bridge inspection using an unmanned aerial vehicle system (UAS) is receiving a lot of attention. In this paper, UAS-based automatic damage detection and bridge condition evaluation were performed on existing bridges. From the process of preparing for inspection to the management of inspection data, the entire bridge inspection process was performed through field tests. The necessary element techniques for each stage were explained and the results were confirmed. Finally, UAS-based results were compared with conventional human-based visual inspection results. As a result, it was confirmed that the UAS-based bridge inspection is faster and more objective than the existing technology. Therefore, it was confirmed that the automatic bridge inspection method based on unmanned aerial vehicles can be applied to the field as a promising technology.

Electromagnetic dampers are emerging as alternatives to conventional dampers applied to stay cables of bridges because they can reduce maintenance costs and allow vibration monitoring owing to their permanent driving characteristics and self-generation function. In this study, the main equations (including those for the induced electromotive force of the active coils and the total damping force of the damper) were derived through magnetic circuit analysis using the main parameters of the electromagnetic damper model. Characteristic tests were performed on electromagnetic damper prototypes to analyze the hysteretic dynamics and derive characteristics according to their structure and excitation conditions. On the basis of the results, we proposed a regenerative hybrid electrodynamic damper with an oxygen-free copper tube and teeth structure. Its physical and electromagnetic behaviors were examined through an electromagnetic analysis of the finite element model of the proposed damper. The results confirmed that attenuation occurred via strengthened magnetic flows, and the estimated power production is suitable for energy harvesting applications. Therefore, we confirmed the feasibility of constructing a system that can simultaneously perform cable attenuation and vibration monitoring using the proposed damper.

To withstand harsh conditions and have a moderate strength, it is desirable to use natural rubber for base isolators. In addition, previous studies have measured the magnetorheological (MR) effect under low-strain range, mostly within 10%. In the reality, it is necessary to evaluate the performance under large-strain range for base isolators. In this study, material properties of natural rubber-based MREs with various mixing ratios were evaluated under large-strain range (~100%). In the first step, MREs with various iron ratios were fabricated and evaluated to observe the MR effect according to the ratio and arrangement of iron powder. As a result, the highest MR effect (22.0% at 100% strain) and damping ratio (10.29%) were observed in the sample with 35% iron ratio, and the MR effect of the isotropic and the anisotropic MRE did not show significant difference under large-strain (50~100%). In the second step, MRE samples containing the optimum iron ratio (investigated in the first step) and various mixing ratios of carbon black and naphthenic oil were prepared. As a result, the MRE containing 60phr of carbon black and 40phr of naphthenic oil had the highest MR effect (33.8% at 100% strain). Compared to the case without additives, it showed an obvious improvement.

This paper proposes an innovative building-integrated wind turbine (BIWT) system by directly utilizing the building skin, which is an unused and unavailable area in all conventional BIWT systems. The proposed system has been developed by combining a guide vane that is able to effectively collect the incoming wind and increase its speed and a rotor with an appropriate shape for specific conditions. To this end, several important design issues for the guide vane as well as the rotor were thoroughly investigated and accordingly addressed in this paper. A series of computational fluid dynamics (CFD) analyses was performed to determine the optimal configuration of the proposed system. Finally, it is demonstrated from performance evaluation tests that the prototype with the specially designed guide vane and rotor for the proposed BIWT system accelerates the wind speed to a sufficient level and consequently increases the power coefficient significantly. Thus, it was confirmed that the proposed system is a promising environment-friendly energy production system for urban areas.

In this study, we experimentally validated the normalized uniform load surface (NULS) curvature method, which has been developed recently to assess damage localization in beam-type structures. The normalization technique allows for the accurate assessment of damage localization with greater sensitivity irrespective of the damage location. In this study, damage to a simply supported beam was numerically and experimentally investigated on the basis of the changes in the NULS curvatures, which were estimated from the modal flexibility matrices obtained from the acceleration responses under an ambient excitation. Two damage scenarios were considered for the single damage case as well as the multiple damages case by reducing the bending stiffness (EI) of the affected element(s). Numerical simulations were performed using MATLAB as a preliminary step. During the validation experiments, a series of tests were performed. It was found that the damage locations could be identified successfully without any false-positive or false-negative detections using the proposed method. For comparison, the damage detection performances were compared with those of two other well-known methods based on the modal flexibility matrix, namely, the uniform load surface (ULS) method and the ULS curvature method. It was confirmed that the proposed method is more effective for investigating the damage locations of simply supported beams than the two conventional methods in terms of sensitivity to damage under measurement noise.

Piezoelectric energy harvesters of cantilevered beam type are studied in various fields due to simplicity. In general, these systems obtain electrical energy from mechanical strain by bending of cantilevered beam. However, conventional systems have disadvantages that they have low efficiency in frequency regions other than resonance frequency. To overcome the limitations, various energy harvesters to apply performance enhancement strategies are proposed and investigated. In this paper, a frequency-changeable L shape energy harvester which is form connected cantilever beam and rigid arm is proposed and investigated. The conventional piezoelectric energy harvester exhibits the principal frequency in the simple bending mode whereas the proposed system features the twisting mode resulting in a higher output voltage than the conventional system. The proposed energy harvester is simplified to a two-degree-of-freedom model and its dynamics are described. How the length of a rigid bar affects its natural frequencies is also studied. To evaluate the performance of the system, experiments by using a vertical shaker and numerical simulation are carried out. As a result, it is shown that the natural frequency for a twisting mode decreases as the arm length increased, and the higher output voltage is generated comparing with those of the conventional energy harvester.

As the importance of sustainable energy increases, wind power generation systems utilizing wind energy around high-rise buildings are being developed. However, in existing wind turbine systems, it is necessary to solve noise, vibration problems, and structural issues for the installation of large-sized systems. In addition, small wind turbine systems can be installed only in limited areas such as roofs and corners, because their efficiency is limited to high and stable wind speed. For this reason, the distribution of fluctuating wind pressure around high-rise buildings was analyzed, and its feasibility as an energy source was evaluated, reflecting that fluctuating wind pressure can be used in vibration-based energy-harvesters. To achieve this, firstly, experimental conditions and theories were established to check the characteristic of wind pressure around high-rise buildings. The experiment was divided into the environment without surrounding buildings and the urban environment. Next, the pressure distribution around high-rise buildings and the quantitative results obtained from the experiment were determined. Finally, based on the results obtained from the experiments, the feasibility of fluctuating wind pressure as an energy-harvesting source was analyzed. From this study, it was found that fluctuating wind pressure can be used as a new energy source at new locations of high-rise buildings that were not utilized previously.

The vibration of cables in a cable-supported bridge affects its serviceability and safety. Therefore, cable dampers are essential for vibration control, monitoring, and the further maintenance of such bridges. In this study, the vibration control performance of an electrodynamic damper applied to a cable used in a footbridge was experimentally verified considering the major design variables of the damper. The damping performance was analyzed by varying the damping ratio according to the excitation condition and external circuit resistance. The acceleration and displacement at each measurement point and the frequency-domain response decreased. Considering the dominant response conditions of the cables in the bridge, an effective additional attenuation was observed. In addition, the harvesting power considering the measurement frequency and power loss was sufficient to operate a wireless measuring sensor by examining the energy harvesting performance from the cable measurement data of an actual bridge in service. Finally, a stepwise operation strategy for a cable vibration monitoring system was suggested and examined by analyzing the meteorological observation data and the power output according to the wind environment. The results demonstrate the feasibility of using an electrodynamic damper to build an integrated monitoring system capable of simultaneous cable vibration reduction and energy harvesting.

Displacement measurements are useful information for various engineering applications such as structural health monitoring (SHM), earthquake engineering and system identification. Most existing displacement measurement methods are costly, labor-intensive, and have difficulties particularly when applying to full-scale civil structures because the methods require stationary reference points. Indirect estimation methods converting acceleration to displacement can be a good alternative as acceleration transducers are generally cost-effective, easy to install, and have low noise. However, the application of acceleration-based methods to full-scale civil structures such as long span bridges is challenging due to the need to install cables to connect the sensors to a base station. This article proposes a low-cost wireless displacement measurement system using acceleration. Developed with smart sensors that are low-cost, wireless, and capable of on-board computation, the wireless displacement measurement system has significant potential to impact many applications that need displacement information at multiple locations of a structure. The system implements an FIR-filter type displacement estimation algorithm that can remove low frequency drifts typically caused by numerical integration of discrete acceleration signals. To verify the accuracy and feasibility of the proposed system, laboratory tests are carried out using a shaking table and on a three storey shear building model, experimentally confirming the effectiveness of the proposed system.

This study is aimed at enhancing the efficiency of a thermoelectric (TE) energy harvesting system by using a thick graphene layer. This method is a simple yet effective way to increase the temperature gradient across a conventional TE module by accelerating heat dissipation on the cold side of the system. Aqueous dispersions of graphene were used to prepare a 112-μm thick graphene layer on the cold side of the TE system with aluminum as the substrate material. The maximum efficiency of the proposed system was improved by 25.45 %, as compared to the conventional TE system, which does not have a graphene layer. Additionally, the proposed system shows very little performance deterioration (2.87 %) in the absence of enough air flow on the cold side of the system, compared to the case of the conventional system (10.59 %). Hence, the proposed system, when coupled with the latest research on high performance TE materials, presents a groundbreaking improvement in the practical application of the TE energy harvesting systems.

In this study, the feasibility of using micro-wind turbines to power wireless sensors on a cable-stayed bridge is comprehensively investigated. To this end, the wind environment around a bridge onto which a turbine is installed is examined, as is the power consumption of a wireless sensor. Feasible alternators and rotors are then carefully selected to make an effective small wind generator (known as a micro-wind turbine). Using the three specially selected micro-wind turbines, a series of experiments was conducted to find the turbine best able to generate the largest amount of power. Finally, a horizontal-axis micro-wind turbine with a six-blade rotor was combined with a wireless sensor to validate experimentally its actual power-charging capability. It is demonstrated that the micro-wind turbine can generate sufficient electricity to power a wireless sensor under moderate wind conditions.

Bridge inspection using unmanned aerial vehicles (UAV) with high performance vision sensors has received considerable attention due to its safety and reliability. As bridges become obsolete, the number of bridges that need to be inspected increases, and they require much maintenance cost. Therefore, a bridge inspection method based on UAV with vision sensors is proposed as one of the promising strategies to maintain bridges. In this paper, a crack identification method by using a commercial UAV with a high resolution vision sensor is investigated in an aging concrete bridge. First, a point cloud-based background model is generated in the preliminary flight. Then, cracks on the structural surface are detected with the deep learning algorithm, and their thickness and length are calculated. In the deep learning method, region with convolutional neural networks (R-CNN)-based transfer learning is applied. As a result, a new network for the 384 collected crack images of 256 × 256 pixel resolution is generated from the pre-trained network. A field test is conducted to verify the proposed approach, and the experimental results proved that the UAV-based bridge inspection is effective at identifying and quantifying the cracks on the structures.

Globally, maintaining equilibrium between energy supply and demand is critical in urban areas facing increasing energy consumption and high-speed economic development. As an alternative, the large-scale application of renewable energy, such as solar and wind power, might be a long-term solution in an urban context. This study assessed the overall utilization potential of a building-integrated photovoltaic and wind turbine (BIPvWt) system, which can be applied to a building skin in global urban areas. The first step of this study was to reorganize the large volume of global annual climate data. The data were analyzed by computational fluid dynamic analysis and an energy simulation applicable to the BIPvWt system, which can generate a Pmax 300 Wp/module with a 15% conversion efficiency from a photovoltaic (PV) system and a 0.149 power coefficient/module from wind turbines in categorized urban contexts and office buildings in specific cities; it was constructed to evaluate and optimize the ratio that can cover the current energy consumption. A diagram of the distribution of the solar and wind energy potential and design guidelines for a building skin were developed. The perspective of balancing the increasing energy consumption using renewable energy in urban areas can be visualized positively in the near future.

Cable structure is a major component of long-span bridges, such as cable-stayed and suspension bridges, and it transfers the main loads of bridges to the pylons. As these cable structures are exposed to continuous external loads, such as vehicle and wind loads, vibration control and continuous monitoring of the cable are required. In this study, an electromagnetic (EM) damper was designed and fabricated for vibration control and monitoring of the cable structure. EM dampers, also called regenerative dampers, consist of permanent magnets and coils. The electromagnetic force due to the relative motion between the coil and the permanent magnet can be used to control the vibration of the structure. The electrical energy can be used as a power source for the monitoring system. The effects of the design parameters of the damper were numerically analyzed and the damper was fabricated. The characteristics of the damper were analyzed with various external load changes. Finally, the vibration-control and energy-harvesting performances of the cable structure were evaluated through a hybrid simulation. The vibration-control and energy-harvesting performances for various loads were analyzed and the applicability to the cable structure of the EM damper was evaluated.

An optimal control algorithm using neural networks is proposed. The controller neural network is trained by a training rule developed to minimize cost function. Both the linear structure and the nonlinear structure can be controlled by the proposed neurocontroller. A bilinear hysteretic model is used to simulate nonlinear structural behavior. Three main advantages of the neurocontroller can be summarized as follows. First, it can control a structure with unknown dynamics. Second, it can easily be applied to nonlinear structural control. Third, external disturbances can be considered in the optimal control. Examples show that structural vibration can be controlled successfully.