Junhee Kim

Abstract: Since the discovery of carbon nanotubes, researchers have been fascinated by their mechanical and electrical properties, as well as their versatility for a wide array of applications. In this study, a carbon nanotube–polyelectrolyte composite multilayer thin film fabricated by a layer-by-layer (LbL) method is proposed to develop a multifunctional material for measuring strain and corrosion processes. LbL fabrication of carbon nanotube composites yields mechanically strong thin films in which multiple sensing transduction mechanisms can be encoded. For example, judicious selection of carbon nanotube concentrations and polyelectrolyte matrices can yield thin films that exhibit changes in their electrical properties to strain and pH. In this study, experimental results suggest a consistent trend between carbon nanotube concentrations and strain sensor sensitivity. Furthermore, by simply altering the type of polyelectrolyte used, pH sensors of high sensitivity can be developed to potentially monitor environmental factors suggesting corrosion of metallic structural materials (e.g. steel, aluminum). (Some figures in this article are in colour only in the electronic version)

Abstract: Dense networks of low-cost wireless sensors have the potential to facilitate prolific data collection in large and complex infrastructure at costs lower than those historically associated w...

Abstract: Deterioration of bridges under repeated traffic loading has called attention to the need for improvements in the understanding of vehicle–bridge interaction. While analytical and numerical models have been previously explored to describe the interaction that exists between a sprung mass ( i.e. , a moving vehicle) and an elastic beam ( i.e. , bridge), comparatively less research has been focused on the experimental observation of vehicle–bridge interaction. A wireless monitoring system with wireless sensors installed on both the bridge and moving vehicle is proposed to record the dynamic interaction between the bridge and vehicle. Time-synchronized vehicle–bridge response data is used within a two-stage system identification methodology. In the first stage, the free-vibration response of the bridge is used to identify the dynamic characteristics of the bridge. In the second stage, the vehicle–bridge response data is used to identify the time varying load imposed on the bridge from the vehicle. To test the proposed monitoring and system identification strategy, the 180 m long Yeondae Bridge (Icheon, Korea) was selected. A dense network of wireless sensors was installed on the bridge while wireless sensors were installed on a multi-axle truck. The truck was driven across the bridge at constant velocity with bridge and vehicle responses measured. Excellent agreement between the measured Yeondae Bridge response and that predicted by an estimated vehicle–bridge interaction model validates the proposed strategy.

Abstract: Wireless structural monitoring systems consist of networks of wireless sensors installed to record the loading environment and corresponding response of large-scale civil structures. Wireless monitoring systems are desirable because they eliminate the need for costly and labor intensive installation of coaxial wiring in a structure. However, another advantageous characteristic of wireless sensors is their installation modularity. For example, wireless sensors can be easily and rapidly removed and reinstalled in new locations on a structure if the need arises. In this study, the reconfiguration of a rapid-to-deploy wireless structural monitoring system is proposed for monitoring short- and medium-span highway bridges. Narada wireless sensor nodes using power amplified radios are adopted to achieve long communication ranges. A network of twenty Narada wireless sensors is installed on the Yeondae Bridge (Korea) to measure the global response of the bridge to controlled truck loadings. To attain acceleration measurements in a large number of locations on the bridge, the wireless monitoring system is installed three times, with each installation concentrating sensors in one localized area of the bridge. Analysis of measurement data after installation of the three monitoring system configurations leads to reliable estimation of the bridge modal properties, including mode shapes.

Abstract: SUMMARY This paper reviews the theoretical principles of subspace system identification as applied to the problem of estimating black-box state-space models of support-excited structures (e.g., structures exposed to earthquakes). The work distinguishes itself from past studies by providing readers with a powerful geometric interpretation of subspace operations that relates directly to theoretical structural dynamics. To validate the performance of subspace system identification, a series of experiments are conducted on a multistory steel frame structure exposed to moderate seismic ground motions; structural response data is used off-line to estimate black-box state-space models. Ground motions and structural response measurements are used by the subspace system identification method to derive a complete input–output state-space model of the steel frame system. The modal parameters of the structure are extracted from the estimated input–output state-space model. With the use of only structural response data, output-only state-space models of the system are also estimated by subspace system identification. The paper concludes with a comparison study of the modal parameters extracted from the input–output and output-only state-space models in order to quantify the uncertainties present in modal parameters extracted from output-only models. Copyright © 2012 John Wiley & Sons, Ltd.

Abstract: A number of image processing techniques IPTs have been implemented for detecting civil infrastructure defects to partially replace human-conducted onsite inspections. These IPTs are primarily used to manipulate images to extract defect features, such as cracks in concrete and steel surfaces. However, the extensively varying real-world situations e.g., lighting and shadow changes can lead to challenges to the wide adoption of IPTs. To overcome these challenges, this article proposes a vision-based method using a deep architecture of convolutional neural networks CNNs for detecting concrete cracks without calculating the defect features. As CNNs are capable of learning image features automatically, the proposed method works without the conjugation of IPTs for extracting features. The designed CNN is trained on 40 K images of 256 × 256 pixel resolutions and, consequently, records with about 98% accuracy. The trained CNN is combined with a sliding window technique to scan any image size larger than 256 × 256 pixel resolutions. The robustness and adaptability of the proposed approach are tested on 55 images of 5,888 × 3,584 pixel resolutions taken from a different structure which is not used for training and validation processes under various conditions e.g., strong light spot, shadows, and very thin cracks. Comparative studies are conducted to examine the performance of the proposed CNN using traditional Canny and Sobel edge detection methods. The results show that the proposed method shows quite better performances and can indeed find concrete cracks in realistic situations.

Abstract: In response to the marked increase in research activity and publications in multifunctional materials and structures in the last few years, this article is an attempt to identify the topics that are most relevant to multifunctional composite materials and structures and review representative journal publications that are related to those topics. Articles covering developments in both multiple structural functions and integrated structural and non-structural functions since 2000 are emphasized. Structural functions include mechanical properties like strength, stiffness, fracture toughness, and damping, while non-structural functions include electrical and/or thermal conductivity, sensing and actuation, energy harvesting/storage, self-healing capability, electromagnetic interference (EMI) shielding, recyclability and biodegradability. Many of these recent developments are associated with polymeric composite materials and corresponding advances in nanomaterials and nanostructures, as are many of the articles reviewed. The article concludes with a discussion of recent applications of multifunctional materials and structures, such as morphing aircraft wings, structurally integrated electronic components, biomedical nanoparticles for dispensing drugs and diagnostics, and optically transparent impact absorbing structures. Several suggestions regarding future research needs are also presented.

Abstract: The increasing demand for wearable electronic devices has made the development of highly elastic strain sensors that can monitor various physical parameters an essential factor for realizing next generation electronics. Here, we report an ultrahigh stretchable and wearable device fabricated from dry-spun carbon nanotube (CNT) fibers. Stretching the highly oriented CNT fibers grown on a flexible substrate (Ecoflex) induces a constant decrease in the conductive pathways and contact areas between nanotubes depending on the stretching distance; this enables CNT fibers to behave as highly sensitive strain sensors. Owing to its unique structure and mechanism, this device can be stretched by over 900% while retaining high sensitivity, responsiveness, and durability. Furthermore, the device with biaxially oriented CNT fiber arrays shows independent cross-sensitivity, which facilitates simultaneous measurement of strains along multiple axes. We demonstrated potential applications of the proposed device, such as st...

Abstract: The use of nanomaterials for strain sensors has attracted attention due to their unique electromechanical properties. However, nanomaterials have yet to overcome many technological obstacles and thus are not yet the preferred material for strain sensors. In this work, we investigated graphene woven fabrics (GWFs) for strain sensing. Different than graphene films, GWFs undergo significant changes in their polycrystalline structures along with high-density crack formation and propagation mechanically deformed. The electrical resistance of GWFs increases exponentially with tensile strain with gauge factors of ~103 under 2~6% strains and ~106 under higher strains that are the highest thus far reported, due to its woven mesh configuration and fracture behavior, making it an ideal structure for sensing tensile deformation by changes in strain. The main mechanism is investigated, resulting in a theoretical model that predicts very well the observed behavior.

Abstract: A type of strain sensor with high tolerable strain based on a ZnO nanowires/polystyrene nanofibers hybrid structure on a polydimethylsiloxane film is reported. The novel strain sensor can measure and withstand high strain and demonstrates good performance on rapid human-motion measurements. In addition, the device could be driven by solar cells. The results indicate that the device has potential applications as an outdoor sensor system.