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Quality control and quality assurance during construction is vital to ensure the structure is built as designed and durable. For reinforced concrete (RC) structures, rebar diameter, spacing, and concrete cover depth are critical in ensuring that the structure is designed for adequate strength and can maintain its service life without unplanned interventions. Once the rebar is laid out in the field, construction inspectors do the required quality control to ensure that the constructed rebar mat matches the design documents. The checks are made at finite points and thus can be improved with currently available technologies such as LiDAR, augmented reality (AR), and uncrewed aerial vehicles (UAVs). This paper summarizes the available technologies for such an effort and focuses on using Red Blue Green Depth (RGBD) cameras as a quality control tool for construction inspection of RC structures such as buildings and bridges. A study conducted using an RGBD camera for estimating rebar diameter is presented in this paper. Results show that RGBD cameras have a very high potential as a low-cost, efficient tool for quality control of RC structures during construction. RGBD cameras can potentially augment current visual inspections in assuring the structures are built as per design drawings, meeting the appropriate specifications with acceptable accuracy.

Structural Health Monitoring (SHM) is the automation of the condition assessment process of an engineered system. When applied to geometrically large components or structures, such as those found in civil and aerospace infrastructure and systems, a critical challenge is in designing the sensing solution that could yield actionable information. This is a difficult task to conduct cost-effectively, because of the large surfaces under consideration and the localized nature of typical defects and damages. There has been significant research efforts in empowering conventional measurement technologies for applications to SHM in order to improve performance of the condition assessment process. Yet, the field implementation of these SHM solutions is still in its infancy, attributable to various economic and technical challenges. The objective of this Roadmap publication is to discuss modern measurement technologies that were developed for SHM purposes, along with their associated challenges and opportunities, and to provide a path to research and development efforts that could yield impactful field applications.

Unmanned aerial vehicles (UAVs) have transitioned from a futuristic research concept to becoming a reality for practical, safe, cost-effective bridge inspections. Several studies have used UAVs to capture images from bridges and infrastructure to assess their condition. However, measuring the dynamic responses of bridges using a UAV involves the integration of UAV, sensors, and the use of dynamic equations. Measurement of dynamic transverse displacement especially is a difficult task in the field given the actual constraints of bridges, flights, and sensing under loading events. If transverse displacements could be measured easily, bridge owners could prioritize maintenance operations more cost-effectively by selecting to repair those bridges that move the most under trains. This paper discusses new requirements and solutions for fabricating an enhanced UAV to obtain dynamic transverse displacement benefiting from experiences gained from several field tests. This work follows the regulations of railroad bridge inspection guidelines and considers the aspects for an implementable system in its development. The paper first introduces the preliminary system that has been developed to this end and discusses potential improvements to this system that are identified through multiple field tests. The preliminary UAV system was developed using an algorithm combining the signals from sensors mounted on the UAV to measure dynamic displacements. This paper explains the step-by-step improvement of the existing system which resulted in a successful field test on a real bridge. Subsequently, some modifications and enhancements for the algorithm are proposed which are compatible with the new system data.

Bridge construction inspections require quantitative measurements and location information. The conventional approach is visual inspection, which in general, is rather time-consuming, expensive due to traffic closure, subjective, and needs special access. Therefore an automated rebar layout detection algorithm was developed to quickly extract quantitative rebar layout information from the LiDAR data. This systematic method can automatically cluster the bridge elements from a 3D point cloud by using LiDAR-equipped UAS data collection and unsupervised machine learning techniques. A new automated inspection system using a LIDAR-equipped UAS can eventually if developed and tested be more reliable as well as less expensive. In the future, if it can be automatized, it can be implemented to simplify the complexity of inspections. The authors developed a platform to mount the camera, sonar laser, and DAQ on the UAS and remotely controlled the data collection operation. Additionally, an algorithm was developed which can automatically obtain the geometric information of the rebar. The proposed automated RGBD-equipped UAS system was developed, fabricated, and tested in the Balloon Fiesta Park on a simulated bridge deck at different heights and with different UAS motions to obtain the best distance, speed, and motion for real construction field. The authors also conducted an outdoor experiment in a construction field at White Rock to validate the capability of the proposed system on the real site with vertical rebar and the challenges of the real construction site. The result confirmed that the LIDAR-equipped UAS system has the potential to help the inspection process in terms of time, accuracy, safety, and generating a permanent record of the inspection. Bridge construction information collected by LiDAR-equipped UAS technology can eventually provide bridge managers with transparent condition assessment and one-step decision-making support through quantitative measurement combined with 3D visualization to facilitate repair planning that can greatly facilitate maintenance., Tran-SET Project: 21STUNM04

The information impulse function (IIF), running Variance, and local Hölder Exponent are three conceptually different time-series evaluation techniques. These techniques examine time-series for local changes in information content, statistical variation, and point-wise smoothness, respectively. Using simulated data emulating a randomly excited nonlinear dynamical system, this study interrogates the utility of each method to correctly differentiate a transient event from the background while simultaneously locating it in time. Computational experiments are designed and conducted to evaluate the efficacy of each technique by varying pulse size, time location, and noise level in time-series. Our findings reveal that, in most cases, the first instance of a transient event is more easily observed with the information-based approach of IIF than with the Variance and local Hölder Exponent methods. While our study highlights the unique strengths of each technique, the results suggest that very robust and reliable event detection for nonlinear systems producing noisy time-series data can be obtained by incorporating the IIF into the analysis.

Fatigue constitutes a critical limit state affecting the safety of civil infrastructure. Under repetitive loading, structural members are susceptible to fatigue cracking under stresses much lower than the yield strength of the material. In this study, computer vision-based fatigue crack detection using a short video stream taken from a nonstationary camera is presented. Videos taken from a hand-held camera, or an unmanned aerial vehicle (UAV) contain two types of movements: 1) true object movements, and 2) unwanted camera movement due to hand shaking or UAV hovering. In most vision-based structural health monitoring research, feature-based motion compensation techniques are used that require manual selection of fixed objects in the video for feature point selection. Feature point selection from true moving objects in the video could produce inaccuracy in video stabilization. In this study, we propose to use hierarchical model-based motion estimation for global motion compensation, which does not require manual selection of fixed objects. First, we construct a pyramid of target and reference images and then estimate motion from top to bottom of the pyramid while accumulating the geometric transformation, by which the camera movement can be removed. Then, we detect salient feature points in the region of interest and track the motion of feature points throughout the video using the Kanade-Lucas-Tomasi (KLT) feature tracking algorithm. Subsequently, a crack detection, and localization algorithm is applied to search for differential point movements caused by fatigue crack opening and closing. To evaluate effectiveness of the proposed method, a laboratory experiment was conducted on a C(T) specimen with an in-plane fatigue crack. Results show that proposed method was able to effectively compensate the camera motion and detect the presence of the fatigue crack.

Rockfalls are a hazard for the safety of infrastructure as well as people. Identifying loose rocks by inspection of slopes adjacent to roadways and other infrastructure and removing them in advance can be an effective way to prevent unexpected rockfall incidents. This paper proposes a system towards an automated inspection for potential rockfalls. A robot is used to repeatedly strike or tap on the rock surface. The sound from the tapping is collected by the robot and subsequently classified with the intent of identifying rocks that are broken and prone to fall. Principal Component Analysis (PCA) of the collected acoustic data is used to recognize patterns associated with rocks of various conditions, including intact as well as rock with different types and locations of cracks. The PCA classification was first demonstrated simulating sounds of different characteristics that were automatically trained and tested. Secondly, a laboratory test was conducted tapping rock specimens with three different levels of discontinuity in depth and shape. A real microphone mounted on the robot recorded the sound and the data were classified in three clusters within 2D space. A model was created using the training data to classify the reminder of the data (the test data). The performance of the method is evaluated with a confusion matrix.