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The condition and hazard monitoring of bridges play important roles in ensuring their service continuity not only throughout their entire lifespan but also under extreme conditions such as those of earthquakes. Advanced structural health monitoring (SHM) systems using vision-based technology, such as surveillance, traffic, or drone cameras, may assist in preventing future impacts due to structural deficiency and are critical to the emergence of sustainable and smart transportation infrastructure. This study evaluates several feature detection and tracking algorithms and implements them in the vision-based SHM of bridges along with their systematic procedures. The proposed procedures are implemented via a two-span accelerated bridge construction (ABC) system undergoing a large-scale shake-table test. The research objectives are to explore the effect of refined matching algorithms on blob-based features in improving their accuracies and to implement the proposed algorithms on large-scale bridges tested under seismic loads using vision-based SHM. The procedure begins by adopting blob-based feature detectors, i.e., the scale-invariant feature transform (SIFT), speeded-up robust features (SURF), and KAZE algorithms, and their stability is compared. The least medium square (LMEDS), least trimmed square (LTS), random sample consensus (RANSAC), and its generalization maximum sample consensus (MSAC) algorithms are applied for model fitting, and their sensitivity for removing outliers is analyzed. The raw data are corrected using mathematical models and scaled to generate displacement data. Finally, seismic vibrations of the bridge are generated, and the seismic responses are compared. The data are validated using target-tracking methods and mechanical sensors, i.e., string potentiometers. The results show a good agreement between the proposed blob feature detection and matching algorithms and target-tracking data and reference data obtained using mechanical sensors. [ABSTRACT FROM AUTHOR]

Computer vision in the structural health monitoring (SHM) field has become popular, especially for processing unmanned aerial vehicle (UAV) data, but still has limitations both in experimental testing and in practical applications. Prior works have focused on UAV challenges and opportunities for the vibration-based SHM of buildings or bridges, but practical and methodological gaps exist specifically for linear infrastructure systems such as pipelines. Since they are critical for the transportation of products and the transmission of energy, a feasibility study of UAV-based SHM for linear infrastructures is essential to ensuring their service continuity through an advanced SHM system. Thus, this study proposes a single UAV for the seismic monitoring and safety assessment of linear infrastructures along with their computer vision-aided procedures. The proposed procedures were implemented in a full-scale shake-table test of a natural gas pipeline assembly. The objectives were to explore the UAV potential for the seismic vibration monitoring of linear infrastructures with the aid of several computer vision algorithms and to investigate the impact of parameter selection for each algorithm on the matching accuracy. The procedure starts by adopting the Maximally Stable Extremal Region (MSER) method to extract covariant regions that remain similar through a certain threshold of image series. The feature of interest is then detected, extracted, and matched using the Speeded-Up Robust Features (SURF) and K-nearest Neighbor (KNN) algorithms. The Maximum Sample Consensus (MSAC) algorithm is applied for model fitting by maximizing the likelihood of the solution. The output of each algorithm is examined for correctness in matching pairs and accuracy, which is a highlight of this procedure, as no studies have ever investigated these properties. The raw data are corrected and scaled to generate displacement data. Finally, a structural safety assessment was performed using several system identification models. These procedures were first validated using an aluminum bar placed on an actuator and tested in three harmonic tests, and then an implementation case study on the pipeline shake-table tests was analyzed. The validation tests show good agreement between the UAV data and reference data. The shake-table test results also generate reasonable seismic performance and assess the pipeline seismic safety, demonstrating the feasibility of the proposed procedure and the prospect of UAV-based SHM for linear infrastructure monitoring. [ABSTRACT FROM AUTHOR]

Random Sample Consensus (RANSAC) is an iterative algorithm for robust model parameter estimation from observed data in the presence of outliers. First proposed by Fischler and Bolles back in 1981, it still is a very popular algorithm in the computer vision community. The primary objective of their paper was to find an effective strategy for excluding outliers from estimation process, but it did not consider the presence of noise among the inliers. A common practice among implementations of RANSAC is to take a few samples extra than the minimum required for estimation problem, but implications of this heuristic are lacking in the literature. In this paper, we present a probabilistic analysis of this common heuristic and explore the possibility of finding an optimal size for the randomly sampled data points per iteration of RANSAC. We also improve upon the lower bound for the number of iterations of RANSAC required to recover the model parameters. On the basis of this analysis, we propose an improvement in the hypothesis step of RANSAC algorithm. Since this step is shared (unchanged) by many of the variants of RANSAC, their performance can also be improved upon. The paper also presents the improvements achieved by incorporating the findings of our analysis in two of the popular variants of RANSAC. [ABSTRACT FROM AUTHOR]

Objectives: In 1998, a formal process using full health technology assessments (HTAs) was implemented to determine the suitability for public subsidy of new and emerging medical technologies in the Australian private healthcare sector. This process is overseen by the Medical Services Advisory Committee (MSAC). In 2004, horizon scanning was introduced in Australia with the stated objective of identifying new and emerging medical technologies into the public healthcare sector, with consideration to the publicly subsidized private healthcare sector. How well horizon scanning works in identifying new and emerging technologies suitable for government subsidized funding in the private healthcare sector is examined in this study. Methods: A descriptive evaluation of the impact of horizon scanning as an early alert and awareness system identifying new and emerging technologies before these technologies are submitted to MSAC for a full HTA. All MSAC HTAs commenced after the introduction of horizon scanning in 2004 were cross-checked with the list of Prioritizing Summaries or Horizon Scanning Reports to determine whether a prior Prioritizing Summary or Horizon Scanning Report had been carried out. Results: Of the forty-three technologies that were the subject of a full MSAC HTAs in the time period examined, only eleven had been the subject of either a Prioritizing Summary or Horizon Scanning Report. As a result of a full MSAC HTA, twelve of the technologies that were not the subject of a Prioritizing Summary or Horizon Scanning Report were given positive recommendations for public funding. Conclusions: Horizon scanning was set up to scan the introduction of new and emerging medical technologies into the public healthcare sector, with consideration to the publicly subsidized private healthcare sector. Based on the number of new and emerging technologies that have been the subject of a full MSAC HTA without first being subjected to either a Prioritizing Summary or Horizon Scanning Report, horizon scanning in Australia does not function as an "early alert and awareness system" for funding in the publicly subsidized private healthcare sector in Australia. [ABSTRACT FROM AUTHOR]