The state-of-the art paper provides an extensive literature review on the work pertaining to structural health monitoring (SHM) systems used to investigate the structural integrity of highway bridges. The focus of this review is on identifying the SHM research efforts that include damage detection, structural capacity evaluation, and remaining service life estimates on such structures. These efforts have spanned a broad range of data processing methods devoted to tracking changes in structural characteristics for damage detection, codified frameworks enabling structural capacity estimating, and reliability analysis to predict remaining life. Our findings are that a large number of studies considered damage detection by data processing methods, whereas a relatively small number of studies were devoted to the estimation of structural capacities and the remaining service life of bridges. We conclude that the critical gaps include a lack of validated SHM systems that use ambient data to examine design...

Although the steel slit dampers commonly utilized for aseismic design approach can dissipate considerable energy created by the yielding of base materials, large residual deformation may happen in the entire frame structure. After strong external excitation, repair costs will be incurred in restoring a structure to its original condition and to replace broken components. For this reason, alternative recentering devices characterized by smart structures, which mitigate the damage for such steel energy dissipation slit dampers, are developed in this study. These devices, feasibly functioning as seismic restrainers, can be improved by implementing superelastic shape memory alloy (SMA) bending bars in a parallel motion with the steel energy-dissipating damper. The bending bars fabricated with superelastic SMAs provide self-centering forces upon unloading, and accordingly contribute to reducing permanent deformation in the integrated slit damper system. The steel slit dampers combined with the superelastic SMA bending bars are evaluated with respect to inelastic behavior as simulated by refined finite element (FE) analyses. The FE slit damper models subjected to cyclic loads are calibrated to existing test results in an effort to predict behavior accurately. The responses of the proposed slit damper systems are compared to those of the conventionally used slit damper systems. From the analysis results, it is concluded that innovative steel slit dampers combined with superelastic SMA bending bars generate remarkable performance improvements in terms of post-yield strength, energy dissipation, and recentering capability.

This paper is intended to provide the state-of-the-art and of-the-practice on visual inspection, monitoring, and analysis of infrastructure using unmanned aerial vehicles (UAVs). Several researchers have inspected various civil infrastructures, including bridges, buildings, and other structures, by capturing close-up images or recording videos, while operating UAVs. Various image analysis tools, such as the algorithm Morphological Link for Crack (Morpholink-C), were able to conduct precise measurements of crack thickness and length. Corrosion has also been detected using texture and color algorithms to investigate UAV-based images. Other analysis methods include structurally integrated sensors, such as digital image correlation equipment, which have helped to capture structural behaviors using UAVs. After the literature review was completed, a nationwide survey was distributed to Departments of Transportation (DOTs) to evaluate the current UAV-enabled inspection techniques that different DOTs have used or are planning to use for visual damage assessment for critical transportation infrastructures, especially bridges. Furthermore, a pertinent UAV selection was completed to indicate suitable UAVs for bridge inspection. Primary findings have shown that UAV-enabled infrastructure inspection techniques have been successfully developed to detect a broad variety of damage (including cracks and corrosions), and a few DOTs have used UAVs to inspect bridges as a more economical and versatile tool.

This paper aims to summarize central findings from a literature review and technical survey on unmanned aerial vehicle (UAV) techniques for bridge inspection and damage quantification. This literature review includes a detailed compilation of different algorithms on high-quality image selection, image-based damage detection and quantification, and various UAV applications for bridge inspections. To gather current bridge inspection practices in the United States, a technical survey referring to UAV-enabled bridge inspections was also conducted for state DOTs and USDA Forest Service (USDA FS) regions. Responses to the survey were assembled from 17 state DOTs (e.g., Nevada DOT, South Dakota DOT, and Texas DOT) and two USDA FS regions such as Region 8 (Southern). From both the review and survey, it was revealed that researchers, state DOTs, and USDA FS regions have interest in using a UAV for bridge inspections, but they have struggled to use it for bridge damage quantification. Specifically, it was found from the review that some recent studies using different algorithms such as deep learning and pattern recognition have been carried out to quantify different types of damage. Key findings from the survey are that over 56% of respondents have used or are planning to use UAVs for bridge inspections, but only 19% of respondents have begun to quantify damage using images captured from UAVs.

This paper focuses on evaluating the effectiveness of an unmanned aerial vehicle (UAV) as a supplementary bridge damage quantification tool. For this study, a glued-laminated timber arch bridge in South Dakota was selected, and an UAV was utilized for the bridge damage quantification. A recommended four-stage UAV-enabled bridge damage quantification protocol involving image quality assessment and image-based damage quantification was developed. A field application using the UAV to measure crack lengths, thicknesses, and rust stain areas of the selected bridge was conducted following the recommended protocol. The image quality parameters, including sharpness and entropy, were used to determine the quality of the UAV-captured images. Pixel- and photogrammetry-based measurements using the high-quality images were obtained to quantify the bridge damage, and the damage was compared to that from actual field measurements. Once the damage information was gathered, the UAV image–based damage level classification was established based on the damage levels defined by current standards. The findings confirmed the accuracy of the recommended protocol, with results within 3.5, 7.9, and 14.9% difference for crack length, thickness, and rust stain area, respectively, when compared with the field measurements.

The use of Unmanned Aerial Systems (UASs), commonly known as drones, has significantly increased over recent years in the field of civil engineering. In detail, the need for a more efficient alternative for bridge inspection has risen because of the increased interest from bridge owners. The primary goal of this paper is to evaluate the efficiency of a drone as a supplemental bridge inspection tool. To complete this study, a glued laminated (glulam) girder with a composite concrete deck bridge was chosen in South Dakota, and a Dà-Jiāng Innovations (DJI) Phantom 4 drone, was employed to perform the bridge inspection. Based on the literature review, an inspection procedure with a drone was developed to efficiently identify damage on the bridge. A drone-enabled inspection was performed following the procedure, and resulting images were checked with those available in the past inspection report from South Dakota Department of Transportation (DOT). This study includes UAS-based bridge inspection considerations to capture appropriate image data necessary for bridge damage determination. A key finding demonstrated throughout this project is that different types of structural damage on the bridge were identified using the UAS.