Experimental and numerical investigations on the seismic behavior of bridge piers with vertical unbonded prestressing strands

In the performance-based seismic bridge design, piers are expected to undergo large inelastic deformations during severe earthquakes, which in turn can result in large residual drift and concrete crack in the bridge piers. In this paper, longitudinal unbonded prestressing strands are used to minimize residual drift and residual concrete crack width in reinforced concrete (RC) bridge piers. Seven pier specimens were designed and tested quasi-statically and the numerical simulations were carried out. The effectiveness of using vertical unbonded prestressing strands to mitigate the residual drift and concrete crack width of RC bridge piers are examined and discussed in detail. It is found that the residual drift and residual concrete crack width of the piers can be reduced significantly by using the prestressing strands. Moreover, the strands can increase the lateral strength of the piers while have little influence on the ductility capacity of the piers. The hysteretic curves, residual drifts and strand stress of the piers predicted by the numerical model agree well with the testing data and can be used to assess the cyclic behavior of the piers

Impact of Graphene Quantum Dots on Gas Foam Stability

This study presents a new application of coal-derived graphene quantum dots (GQD) in stabilizing surfactant-based foams. The methane foam generated by the surfactant itself is susceptible to rapid collapse due to various factors. When the GQDs are added to the surfactant in a mass ratio between 1:8 to 1:16, they self-assemble at the lamella and prevent liquid drainage and coalescence. The nanofluid composed of GQD and amphoteric surfactant reduced both the oil–brine and brine–gas interfacial tension to a greater extent compared to the surfactant alone. In addition, GQD helped alter the rock wettability to strongly water-wet conditions, as compared to weakly water-wet conditions with pure surfactant. The foam formed by the nanofluid was made up of smaller uniformly shaped bubbles with a thick lamella, whereas the foam formed by the surfactant had large polyhedral shaped bubbles with a thin lamella. The transmission electron microscope micrographs of the nanofluid emulsion with crude oil showed that the GQDs are highly interfacially active and tend to assemble on the surface of the oil droplets. Next, the foam stability and strength were investigated with pure surfactant and nanofluid in high salinity brine using water- and oil-wet sandpacks at reservoir conditions relevant to the Bakken formation. The dependence of foam half-life and steady-state apparent viscosity was studied as a function of surfactant and GQD concentration, gas fraction, flow rate, and brine salinity. It was observed that the addition of GQD increases both the foam half-life and steady-state apparent viscosity compared to pure surfactant. This work paved the way for the application of novel carbonaceous nanoparticles under challenging conditions where traditional nanoparticles cannot be used

Deep learning-based identification and localization of intracranial hemorrhage in patients using a large annotated head computed tomography dataset: A retrospective multicenter study

Background: Accurately identifying and localizing the five subtypes of intracranial hemorrhage (ICH) are crucial steps for subsequent clinical treatment; however, the lack of a large computed tomography (CT) dataset with annotations of the categorization and localization of ICH considerably limits the development of deep learning-based identification and localization methods. We aimed to construct this large dataset and develop a deep learning-based model to identify and localize the five ICH subtypes, including intraventricular hemorrhage (IVH), intraparenchymal hemorrhage (IPH), subdural hemorrhage (SDH), subarachnoid hemorrhage (SAH), and epidural hemorrhage (EDH), in non-contrast head CT scans. Methods: Based on the public Radiological Society of North America (RSNA) 2019 dataset, we constructed a large CT dataset named RSNA 2019+ that was annotated for bleeding localization of the five ICH subtypes by three radiologists. An improved YOLOv8 architecture with the bidirectional feature pyramid network was proposed and trained using the RSNA 2019+ training dataset and evaluated on the RSNA 2019+ test dataset. The public CQ500, and two private datasets collected from the Xinqiao and Sunshine Union Hospitals, respectively, were also annotated to perform multicenter validation. Furthermore, the performance of the deep learning model was compared with that of four radiologists. Multiple performance metrics, including the average precision (AP), precision, recall and F1-score, were used for performance evaluation. The McNemar and chi-squared tests were performed, and the 95% Wilson confidence intervals were given for the precision and recall. Results: There were 175,125; 4,707; 8,259; and 3,104 bounding boxes after annotation on the RSNA 2019+; CQ500+; and the PD 1 and PD 2 datasets, respectively. With an intersection-over-union threshold of 0.5, the APs of IVH, IPH, SAH, SDH and EDH are 0.852, 0.820, 0.574, 0.639, and 0.558, respectively, yielding a mean average precision (mAP) of 0.688 for our proposed deep learning model on the RSNA 2019+ test dataset. For the multicenter validation involving the three external datasets, the mAPs for CQ500, PD1, and PD2 were 0.594, 0.734, and 0.66, respectively, which is comparable to those of radiologist with eight years of experience in head CT interpretation. Conclusion: The deep learning model developed from the constructed RSNA 2019+ dataset exhibited good potential in identifying and localizing the five ICH subtypes in CT slices and has the potential to assist in the clinical diagnosis

Rapid repair techniques for severely earthquake-damaged circular bridge piers with flexural failure mode

© 2017, Institute of Engineering Mechanics, China Earthquake Administration and Springer-Verlag Berlin Heidelberg. In this study, three rapid repair techniques are proposed to retrofit circular bridge piers that are severely damaged by the flexural failure mode in major earthquakes. The quasi-static tests on three 1:2.5 scaled circular pier specimens are conducted to evaluate the efficiency of the proposed repair techniques. For the purpose of rapid repair, the repair procedure for all the specimens is conducted within four days, and the behavior of the repaired specimens is evaluated and compared with the original ones. A finite element model is developed to predict the cyclic behavior of the repaired specimens and the numerical results are compared with the test data. It is found that all the repaired specimens exhibit similar or larger lateral strength and deformation capacity than the original ones. The initial lateral stiffness of all the repaired specimens is lower than that of the original ones, while they show a higher lateral stiffness at the later stage of the test. No noticeable difference is observed for the energy dissipation capacity between the original and repaired pier specimens. It is suggested that the repair technique using the early-strength concrete jacket confined by carbon fiber reinforced polymer (CFRP) sheets can be an optimal method for the rapid repair of severely earthquake-damaged circular bridge piers with flexural failure mode