In the current modern era of information and technology, emerging remote advancements have been widely established for detailed virtual inspections and assessments of infrastructure assets, especially bridges. These technologies are capable of creating an accurate digital representation of the existing assets, commonly known as the digital twins. Digital twins are suitable alternatives to in-person and on-site based assessments that can provide safer, cheaper, more reliable, and less distributive bridge inspections. In the case of bridge monitoring, Unmanned Aerial Vehicle (UAV) photogrammetry and Terrestrial Laser Scanning (TLS) are among the most common advanced technologies that hold the potential to provide qualitative digital models; however, the research is still lacking a reliable methodology to evaluate the generated point clouds in terms of quality and geometric accuracy for a bridge size case study. Therefore, this paper aims to provide a comprehensive methodology along with a thorough bridge case study to evaluate two digital point clouds developed from an existing Australian heritage bridge via both UAV-based photogrammetry and TLS. In this regard, a range of proposed approaches were employed to compare point clouds in terms of points’ distribution, level of outlier noise, data completeness, surface deviation, and geometric accuracy. The comparative results of this case study not only proved the capability and applicability of the proposed methodology and approaches in evaluating these two voluminous point clouds, but they also exhibited a higher level of point density and more acceptable agreements with as-is measurements in TLS-based point clouds subjected to the implementation of a precise data capture and a 3D reconstruction model.

Over the last decade, the digital twin (DT) concept has effectively revolutionized conventional bridge monitoring and management. Despite their overall success, current bridge DTs encounter conceptual ambiguities, hindering their inherent potential for practical implementation. Moreover, intelligent decision support models have not been properly considered as a component of the bridge DTs framework to enhance the reliability of decisions for asset maintenance. Therefore, this paper conducts a scientometric analysis and a comprehensive state-of-the-art review, exploring current bridge DT research trends and architectures and introducing an enhanced conceptual framework for bridge DTs. To this end, more than 480 research publications have been reviewed, compared, and analyzed. The research result encompasses the redevelopment of a multilayer DT framework, fostering its implementation in the full lifecycle of bridge infrastructure while exploring the potential integration of decision support systems and data fusion from advanced technologies to improve the overall efficiency of implementing DT technology in bridges.

Finding robust and correct keypoints in images remains a challenge, especially when repetitive patterns are present. In this article, we propose a universal two-step filtering method to solve the mismatch problem in repetitive patterns. Having applied a mean-shift clustering algorithm to remove obvious mismatches, the proposed confusion reduction (CR) method uses a novel confusion index (CI) in a gridding schema to identify and filter out the remaining confusing keypoints. In both steps, the descriptors’ statistical properties are evaluated using kernel density estimation. Various synthetic and real stereo pairs, along with multiview image blocks, were used to assess the performance of the presented algorithm. The results were also compared with those obtained by several state-of-the-art mismatch removal methods. The experiments showed that, on average, the proposed strategy improves the accuracy of matching by 10% and the accuracy of photogrammetric blocks by 20%–30%.

Over the past years, bridge inspection practices and condition assessments were predicated upon long-established manual and paper-based data collection methods which were generally unsafe, time-consuming, imprecise, and labor-intensive, influenced by the experience of the trained inspectors involved. In recent years, the ability to turn an actual civil infrastructure asset into a detailed and precise digital model using state-of-the-art emerging technologies such as laser scanners has become in demand among structural engineers and managers, especially bridge asset managers. Although advanced remote technologies such as Terrestrial Laser Scanning (TLS) are recently established to overcome these challenges, the research on this subject is still lacking a comprehensive methodology for a reliable TLS-based bridge inspection and a well-detailed Bridge Information Model (BrIM) development. In this regard, the application of BrIM as a shared platform including a geometrical 3D CAD model connected to non-geometrical data can benefit asset managers, and significantly improve bridge management systems. Therefore, this research aims not only to provide a practical methodology for TLS-derived BrIM but also to serve a novel sliced-based approach for bridge geometric Computer-Aided Design (CAD) model extraction. This methodology was further verified and demonstrated via a case study on a cable-stayed bridge called Werrington Bridge, located in New South Wales (NSW), Australia. In this case, the process of extracting a precise 3D CAD model from TLS data using the sliced-based method and a workflow to connect non-geometrical information and develop a BrIM are elaborated. The findings of this research confirm the reliability of using TLS and the sliced-based method, as approaches with millimeter-level geometric accuracy, for bridge inspection subjected to precise 3D model extraction, as well as bridge asset management and BrIM development.

Image matching is one of the most important tasks in Unmanned Arial Vehicles (UAV) photogrammetry applications. The number and distribution of extracted keypoints play an essential role in the reliability and accuracy of image matching and orientation results. Conventional detectors generally produce too many redundant keypoints. In this paper, we study the effect of applying various information content criteria to keypoint selection tasks. For this reason, the quality measures of entropy, spatial saliency and texture coefficient are used to select keypoints extracted using SIFT, SURF, MSER and BRISK operators. Experiments are conducted using several synthetic and real UAV image pairs. Results show that the keypoint selection methods perform differently based on the applied detector and scene type, but in most cases, the precision of the matching results is improved by an average of 15%. In general, it can be said that applying proper keypoint selection techniques can improve the accuracy and efficiency of UAV image matching and orientation results. In addition to the evaluation, a new hybrid keypoint selection is proposed that combines all of the information content criteria discussed in this paper. This new screening method was also compared with those of SIFT, which showed 22% to 40% improvement for the bundle adjustment of UAV images.