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Environmental and surface texture-induced temperature variation across the bridge deck is a major source of errors in delamination detection through thermography. This type of external noise poses a significant challenge for conventional quantitative methods such as global thresholding and k-means clustering. An iterative top-down approach is proposed for delamination segmentation based on grayscale morphological reconstruction. A weight-decay function was used to regularize the reconstruction for regional maxima extraction. The mean and coefficient of variation of temperature gradient estimated from delamination boundaries were used for discrimination. The proposed approach was tested on a lab experiment and an in-service bridge deck. The result showed the ability of the framework to handle the non-uniform background situation which often occurred in practice and thus eliminates the need for inferencing the background required by existing methods. The gradient statistics of the delamination boundary in the thermal image were indicated as the valid criterion refine the segmentation under the proposed framework. Thus, improved performance was achieved compared to conventional methods. The parameter selection and the limitation of this approach were also discussed., Comment: arXiv admin note: text overlap with arXiv:1904.05723

Despite decades of efforts using thermography to detect delamination in concrete decks, challenges still exist in removing environmental noise from thermal images. The performance of conventional temperature-contrast approaches can be significantly limited by environment-induced non-uniform temperature distribution across imaging spaces. Time-series based methodologies were found robust to spatial temperature non-uniformity but require the extended period to collect data. A new empirical image filtering method is introduced in this paper to enhance the delamination detection using blob detection method that originated from computer vision. The proposed method employs a Laplacian of Gaussian filter to achieve multi-scale detection of abnormal thermal patterns by delaminated areas. Results were compared with the state-of-the-art methods and benchmarked with time-series methods in the case of handling the non-uniform heat distribution issue. To further evaluate the performance of the method numerical simulations using transient heat transfer models were used to generate the 'theoretical' noise-free thermal images for comparison. Significant performance improvement was found compared to the conventional methods in both indoor and outdoor tests. This methodology proved to be capable to detect multi-size delamination using a single thermal image. It is robust to the non-uniform temperature distribution. The limitations were discussed to refine the applicability of the proposed procedure.

We propose a multiple pulses phase-matching quantum key distribution protocol (MPPM-QKD) to exceed the linear key rate bound and to achieve higher error tolerance. In our protocol, Alice and Bob generate at first their own train pulses (each train should contain L pulses) as well as random bit sequences, and also encode each pulse of their trains with a randomized phase and a modulation phase. As the next step, both encoded trains are simultaneously sent to Charlie, who performs an interference detection and may be also an eavesdropper. After a successful detection is announced by Charlie, Alice and Bob open the randomized phase of each pulse and keep only communications when the summation of the difference randomized phases at two success detection's time-stamps for Alice and Bob are equal to 0 or pi. Thereafter, Alice and Bob compute the sifted key with the time-stamps. The above procedure is repeated until both Alice and Bob achieve sufficiently long sifted keys. We can also show that the secret key rate of the proposed QKD protocol can beat the rate-loss limit of so far known QKD protocols when the transmission distance is greater than 250 km. Moreover, the proposed protocol has a higher error tolerance, approximately 24%, when the transmission distance is 50 km and L = 128. The secret key rate and the transmission distance of our protocol are superior to that of the round-robin differential-phase-shift quantum key distribution protocol [6], and also of the measurement-device-independent quantum key distribution protocol [4], and the secret key rate performance is better in both cases than that of phase-matching quantum key distribution when bit train length is greater than 32.

Environmental-induced temperature variations across the bridge deck were one of the major factors that degraded the performance of delamination detection through thermography. The non-uniformly distributed thermal background yields the assumption of most conventional quantitative methods used in practice such as global thresholding and k-means clustering. This study proposed a pre-processing method to estimate the thermal background through iterative grayscale morphologic reconstruction based on a pre-selected temperature contrast. After the estimation of the background, the thermal feature of delamination was kept in the residual image. A UAV-based nondestructive survey was carried out on an in-service bridge for a case study and two delamination quantization methods (threshold-based and clustering-based) were applied on both raw and residual thermal image. Results were compared and evaluated based on the hammer sounding test on the same bridge. The performance of detectability was noticeably improved while direct implementation of post-processing on raw image exhibited over- and under-estimation of delamination. The selection of pre-defined temperature contrast and stopping criterion of iteration were discussed. The study concluded the usefulness of the proposed method for the case study and further evaluation and parameter tuning are expected to generalize the method and procedure., Comment: Accepted as the presentation for 98th Annual Meeting of the Transportation Research Board (TRB)

Delamination assessment of the bridge deck plays a vital role for bridge health monitoring. Thermography as one of the nondestructive technologies for delamination detection has the advantage of efficient data acquisition. But there are challenges on the interpretation of data for accurate delamination shape profiling. Due to the environmental variation and the irregular presence of delamination size and depth, conventional processing methods based on temperature contrast fall short in accurate segmentation of delamination. Inspired by the recent development of deep learning architecture for image segmentation, the Convolutional Neural Network (CNN) based framework was investigated for the applicability of delamination segmentation under variations in temperature contrast and shape diffusion. The models were developed based on Dense Convolutional Network (DenseNet) and trained on thermal images collected for mimicked delamination in concrete slabs with different depths under experimental setup. The results suggested satisfactory performance of accurate profiling the delamination shapes., Comment: Accepted for the 2019 ASCE International Conference on Computing in Civil Engineering

In recent years, using drone, also known as unmanned aerial vehicle (UAV), in close-distance visual inspection has became an active area in many disciplines. However, many challenges still remain before we can achieve autonomous inspection, especially when inspecting complex structures. The complex civil structures, such as bridges, dams and wind turbines, are large-scale and geometrical complicated. It requires sophisticated path planning algorithms to achieve close-distance inspection and, at the same time, avoid collisions. In practice, directly deploying the path planning result on such structures is error prone, costly, and full of hazards. In this paper, rely on micro quadrotor UAV, the authors present an affordable experimental platform for testing drone-based path planning result. The platform allows the users to conduct many path planning experiments at any time without worrying expensive and time consuming outdoor test flying. This platform is developed based on the bundle of Crazyflie, which includes Crazyflie 2.0 quadrotor, Crazyradio and loco positioning system (LPS). Equipped with an onboard micro FPV camera, the visual data can be lively streamed to the host computer during flight. The functions of manual configuration and waypoints control are explicitly designed in this platform to increase its flexibility and performance on path following and debugging. To evaluate the practicability of the proposed test platform, two existing drone-based path planning algorithms are tested. The results show that even though certain level of error existed, the quality of visual data and accuracy of path following are high enough for simulating most practical inspection applications., Comment: 6 pages, 6 figures, accepted in ICCCBE 2018

UAVs have been widely used in visual inspections of buildings, bridges and other structures. In either outdoor autonomous or semi-autonomous flights missions strong GPS signal is vital for UAV to locate its own positions. However, strong GPS signal is not always available, and it can degrade or fully loss underneath large structures or close to power lines, which can cause serious control issues or even UAV crashes. Such limitations highly restricted the applications of UAV as a routine inspection tool in various domains. In this paper a vision-model-based real-time self-positioning method is proposed to support autonomous aerial inspection without the need of GPS support. Compared to other localization methods that requires additional onboard sensors, the proposed method uses a single camera to continuously estimate the inflight poses of UAV. Each step of the proposed method is discussed in detail, and its performance is tested through an indoor test case., Comment: 8 pages, 5 figures, submitted to i3ce 2019

UAVs showed great efficiency on scanning bridge decks surface by taking a single shot or through stitching a couple of overlaid still images. If potential surface deficits are identified through aerial images, subsequent ground inspections can be scheduled. This two-phase inspection procedure showed great potentials on increasing field inspection productivity. Since aerial and ground inspection images are taken at different scales, a tool to properly fuse these multi-scale images is needed for improving the current bridge deck condition monitoring practice. In response to this need a data fusion platform is introduced in this study. Using this proposed platform multi-scale images taken by different inspection devices can be fused through geo-referencing. As part of the platform, a web-based user interface is developed to organize and visualize those images with inspection notes under users queries. For illustration purpose, a case study involving multi-scale optical and infrared images from UAV and ground inspector, and its implementation using the proposed platform is presented., Comment: 8 pages, 5 figures, submitted to i3ce 2019

Nondestructive detecting defects (NDD) in concrete structures have been explored for decades. Although limited successes were reported, major limitations still exist. The major limitations are the high noises to signal ratio created from the environmental factors, such as cloud, shadow, water, surface texture etc. and the decision making still relies on the engineering judgment of interpretation of image content. Time-series approach, such as principle component thermography approach has been experimented with some improved results. Recent progress in image processing using machine learning approach made it possible for detecting defects thermal features in more quantitative ways. In this paper, we provide a procedure to represent the thermal feature in the time domain by principal component analysis and regress the prediction of detection by two schemes of supervised learning models. Three independent experiments were conducted in a similar laboratory setup but varied in conditions to illustrate the performance and generalization of models. Results showed the effectiveness for the detection purpose with appropriate tuning for parameters. Future studies will focus on implementing more sophisticated structured models to handle more realistic cases under natural conditions., Comment: Accepted as Poster for 98th Annual Meeting of Transportation Research Board (TRB)

Using thermography as a nondestructive method for subsurface detection of the concrete structure has been developed for decades. However, the performance of current practice is limited due to the heavy reliance on the environmental conditions as well as complex environmental noises. A non-time-series method suffers from the issue of solar radiation reflected by the target during heating stage, and issues of potential non-uniform heat distribution. These limitations are the major constraints of the traditional single thermal image method. Time series-based methods such as Fourier transform-based pulse phase thermography, principle component thermography, and high order statistics have been reported with robust results on surface reflective property difference and non-uniform heat distribution under the experimental setting. This paper aims to compare the performance of above methods to that of the conventional static thermal imaging method. The case used for the comparison is to detect voids in a hollow concrete block during the heating phase. The result was quantitatively evaluated by using Signal-to-Noise Ratio. Favorable performance was observed using time-series methods compared to the single image approach., Comment: 11 pages, 5 color figures, and 1 table accepted by Construction Research Congress 2018 conference, April 2-7, 2018, New Orleans, LA

3D reconstruction can be used as a platform to monitor the performance of activities on construction site, such as construction progress monitoring, structure inspection and post-disaster rescue. Comparing to other sensors, RGB image has the advantages of low-cost, texture rich and easy to implement that has been used as the primary method for 3D reconstruction in construction industry. However, the image-based 3D reconstruction always requires extended time to acquire and/or to process the image data, which limits its application on time critical projects. Recent progress in Visual Simultaneous Localization and Mapping (SLAM) make it possible to reconstruct a 3D map of construction site in real-time. Integrated with Unmanned Aerial Vehicle (UAV), the obstacles areas that are inaccessible for the ground equipment can also be sensed. Despite these advantages of visual SLAM and UAV, until now, such technique has not been fully investigated on construction site. Therefore, the objective of this research is to present a pilot study of using visual SLAM and UAV for real-time construction site reconstruction. The system architecture and the experimental setup are introduced, and the preliminary results and the potential applications using Visual SLAM and UAV on construction site are discussed., Comment: 10 pages, 7 figures, conference paper submitted to CRC 2018

Image-based vibration mode identification gained increased attentions in civil and construction communities. A recent video-based motion magnification method was developed to measure and visualize small structure motions. This new approach presents a potential for low-cost vibration measurement and mode shape identification. Pilot studies using this approach on simple rigid body structures was reported. Its validity on complex outdoor structures have not been investigated. The objective is to investigate the capacity of video-based motion magnification approach in measuring the modal frequency and visualizing the mode shapes of complex steel bridges. A novel method that increases the performance of the current motion magnification for efficient structure modal analysis is introduced. This method was tested in both indoor and outdoor environments for validation. The results of the investigation show that motion magnification can be an efficient tool for modal analysis on complex bridge structures. With the developed method, mode frequencies of multiple structures are simultaneously measured and mode shapes of each structure are automatically visualized., Comment: 15 pages including 9 figures and references

A fluid-based method is demonstrated for preparing graphene nanomeshes (GNMs) directly from pristine graphite flakes by a one-step process. The high efficiency is attributed to the combination of fluid-assisted exfoliation and perforation of the graphene sheets. Atomic force microscopy shows that the as-produced GNMs are less than 1.5 nm thick. The total area of the pores within 1 um2 of the GNM sheet is estimated as ~0.15 um2 and the pore density as ~22 um-2, The yield of GNMs from pristine graphite powder and the power consumption for per gram GNM synthesis are evaluated as 5 wt% and 120 kWh, respectively. X-ray photoelectron spectroscopy, infrared spectroscopy, elemental analysis and Raman spectroscopy results indicate the purity of the GNMs and thus it is a green efficient method. The present work is expected to facilitate the production of GNMs in large scale., Comment: 5 pages, 4 figures

Bulk applications of graphene in fields such as advanced composites, conductive ink, and energy storage require cheap and scalable graphene. Fortunately, in the last decade, liquid-phase exfoliation of graphite to give pristine graphene has been thought as a promising way to massive production of graphene at high efficiency and low cost, in terms of the cheap and abundant graphite source and a variety of cost-effective exfoliation techniques. Though many exfoliation techniques are available so far, this article will highlight the recent progress of fluid dynamics route which emerges as a promising scalable and efficient way for graphene production in the last five years. The emphasis is set on vortex fluidic devices and pressure- and mixer-driven fluid dynamics, with our perspectives on the latest progress, exfoliation mechanism, and some key issues that require further study in order to realize industrial applications., Comment: 18 figures