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This paper presents a framework to better identify and measure defects in a bridge using drone-based inspection images integrated with grayscale image enhancement techniques. For this study, a DJI Matrice 210 drone was used for the inspection of a three-span timber bridge with concrete decking located in Keystone, South Dakota. During the inspection, the drone recorded a series of videos of the bridge using the MOVie (MOV, video file extension) video format. MOV-based image analysis was conducted to identify a variety of defect types (i.e., efflorescence, water leakage, spalling, and discoloration) on the bridge. For improvement of defect visibility, the grayscale image enhancement technique was applied to determine visually enhanced images for the individual defect. The technique used grayscale image histogram processing that can adjust images using realignment of contrast histograms, in which contrasts of each pixel of the grayscale images have their own number from 0 for black to 255 for white in the image. With the enhanced images, pixel-based measurement was conducted to quantify the defects, including efflorescence (3.75 m2), water leakage (4.21 m2), spalling (0.74 m2), and discoloration (2.12 m2). Based on these findings, the grayscale drone inspection image enhancement technique enabled the demonstration of defect visibility adjustment and improvement for more reliable identification and measurement of the defects in the bridge.

Unmanned Aerial System (UAS) technologies integrated with image processing algorithms are considered timely and useful for bridge inspections because of improved accessibility, recording ability, and cost-efficiency compared to the conventional inspection approach. The image processing algorithms can improve the ability of the UAS-aided bridge inspections in efficiently identifying and quantifying deterioration. This study was aimed to inspect an in-service single-span precast concrete bridge on a rural roadway in South Dakota using UAS technologies coupled with a Deep Neural Network (DNN) denoise algorithm. During the inspections, Phantom 4 and DJI Matrice 210 UASs recorded several videos for different bridge elements (e.g., girders and decking), and a total of 21,784 inspection images were extracted from the videos with a duration of more than 14 minutes. Deteriorations specific to the bridge elements such as spalling and rust were characterized by performing the DNN-aided image processing algorithm with the extracted inspection images. The DNN allowed for computation and analysis between input and output image data to reduce the noises on the images. Besides, a grayscale image enhancement algorithm was considered to improve the visibility of images by optimizing image contrast settings. With the visibility-improved images, detailed quantification on the detected deterioration per bridge element was carried out using a pixel-based measurement tool. Based upon the study’s results, it was revealed that the UAS technologies with the DNN denoise algorithm were able to successfully characterize and quantify visible deteriorations to the certain bridge elements using pixel-based tools.

Transportation industries have commonly used welded connections in the production of dynamic message signs (DMSs) for several years. Even though with numerous alternatives such as bolt/rive...

This study is intended to improve visual bridge damage measurement through image analysis of drone inspection images optimized in terms of quality. To optimize the drone-captured image quality, image properties, including brightness, contrast, and sharpness, are investigated and adjusted for this study. As part of the optimized damage measurement, visual inspections are initially conducted using DJI Matrice 210 for a multi-span timber bridge located in Fall River, South Dakota in the United States. To detect the entirety of any damage during the inspections, the drone records three videos of the bridge from which 15,996 images are extracted. From the drone-aided bridge inspection, several different types of damage, encompassing weathering, paint failure, crack, and split, are observed. Among all the images, the high-quality inspection images with distinct damage on certain bridge components such as timber girders are sorted out manually. Then, the various image properties for the high-quality inspection images are iteratively adjusted to optimize the images’ quality. Measurement of the individual damage detected during the inspection are completed with the optimized quality images. Particularly, the major challenge to identify the damaged area on the weathered timber bridge is resolved, as the edge domains of the damage are well-defined visualized and observed efficiently after the optimization of image quality. Key results indicate that the weathering area identified through the optimized image was measured to be 1.16 m2 but the identified area using the unfiltered image was 1.33 m2, showing a difference of 12.43%.

In this study, reinforcing bars acting as dowels were investigated. Direct shear tests on specimens with a wide range of strengths for concrete (30 and 60 MPa) and steel (400 and 600 MPa) w...

This paper was intended to investigate the strength of adhesive and welded connections to be used in a dynamic message sign (DMS). The tensile strength and shear strength were studied for both of the adhesive and welded specimens. A number of tensile specimens and shear specimens with variations in width and temperature were tested until failure according to the ASTM D638 and ASTM D1002, respectively. The specimens with a range of width from 13 mm to 38 mm were conditioned with temperatures between −56.67 °C and 93.33 °C. The effects of temperature and width on each of the strengths were evaluated by analysing the testing data in a graphical and statistical manner. As expected, all the tests revealed that the welded specimens have significantly higher strength compared to the adhesive specimens in tensile and shear loadings. For the adhesive specimens, due to the increment of temperature, the highest increments were found to be 31.9% and 30.4%. In addition to the tests, Response Surface Metamodels (RSMs) and practical design equations were developed with regression analysis of the testing data, so as to predict tensile and shear stresses of both adhesive and welded specimens in an efficient way. 3D plots generated from the RSMs showed a higher effect of the temperature and width on the ultimate tensile and shear strength for both adhesive and welded specimens, and the practical design equations were founded to be more reliable for the overall tensile and shear stress prediction compared to the RSMs.

Adhesively bonded joints used for the fabrication of an aluminum dynamic message sign (DMS) have increasingly gained popularity in the transportation signage manufacturing industry. However, in-depth numerical studies on the structural performance of DMSs are tremendously limited. The main goal of this study was to numerically examine the structural performance of the adhesively bonded DMSs. In this study, the performance of DMS specifically refers to the maximum strength resisted by the DMS under ultimate loadings. In pursuit of this goal, this study proposed a three-dimensional finite element (FE) analysis approach to delve into the performance of a full-scale adhesive DMS tested with ultimate loads. The FE model for the tested DMS was generated using ABAQUS software and calibrated with the testing data. A parametric study was conducted via the calibrated FE model associated with variation in key design parameters, such as damage factor and area of adhesive, in order to not only quantify their effects on the performance of adhesive DMSs, but also determine its optimized performance. Finally, a parametric equation in conjunction with the key parameters was developed in a statistical manner for the efficient prediction of the adhesive DMS performance. Significant findings revealed that the overall structural behavior of the generated FE model was observed analogous to that found in the DMS testing, and the ultimate strength from the FE analysis was only 1.26% lower than that gained from the testing. Additionally, it was found that the optimized FE model exhibited 8.32% higher ultimate strength than the tested DMS, and the proposed equation was capable of estimating the strength with an average error of 9.58% compared to the FE analysis.

Purpose To evaluate the prenatal sonographic predictive markers of the outcome in fetuses with bronchopulmonary sequestration (BPS). Methods BPS size and diameter of the feeding artery (FA) were measured prenatally and postnatally. Velocity of the FA and the left ventricular-modified myocardial performance index (LV mod-MPI) were also evaluated prenatally. Results Forty-seven women were included in the study. Mean gestational age, mass size, diameter and velocity of the FA, and LV mod-MPI at prenatal diagnosis were 23.5 ± 2.2 weeks, 3.6 ± 8.3 cm, 2.3 ± 0.6 mm, 46.6 ± 15.4 cm/s, and 0.46 ± 0.06, respectively. Mean mass diameter and FA diameter measured on postnatal CT examinations were 3.8 ± 1.0 cm and 2.3 ± 0.7 mm, respectively. Five patients had respiratory symptoms after birth. Twenty children (43%) underwent or were scheduled to undergo mass excision, and the remaining 27 (57%) were doing well without any intervention. There was no neonatal death. LV mod-MPI at diagnosis, the FA diameter after birth and the serial change in the FA size were significantly associated with postnatal mass excision. Conclusion The FA diameter and LV mod-MPI may be additional markers for predicting whether fetuses with BPS should undergo mass excision in early childhood or conservative care.