Inelastic Analysis of Seismic Loading of Precast Concrete Cladding Using Commercially Available Software

Two nonlinear pushover analyses and three displacement-controlled time history analyses of two precast concrete panel assemblies were completed. The analytical software used was SAP2000 (version 15.0.0). The precast concrete panel modeled was a three-dimensional single panel connected to a one-story, one-bay, concrete-reinforced structural frame with four flexing rods and two bearing connections. The results showed that static analysis is suitable to predict only dynamic analysis with a long input period of vibrations (low acceleration vibrations). As the period of input vibration neared the fundamental period of vibration of the precast concrete panel, the maximum value of forces developed in connections increased. The amplification ratio for both models decreased as the period of the input vibrations varied from 100 to 0.32 s, and the amount of time that each flexing rod experienced a local maximum of force changed for the periods of input vibrations of 100, 1, and 0.32 s

LiDAR Scanning with Supplementary UAV Captured Images for Structural Inspections

Structural assessment using remote sensing technologies can be performed efficiently and effectively using such technologies as LiDAR (light detection and ranging). LiDAR can be employed for various structural assessments, such as as-built conditions for a newly constructed facility, routine inspection during its service life, or structural collapse evaluation after a natural hazard or extreme event. However, the main disadvantage of LiDAR is that it is a line-of-sight technology that can result in significant occlusions. Architectural or structural components can be partially or fully occluded by another object with respect to the location of the laser scanner. Supplemental photogrammetry techniques, such as structure from motion (SfM), can be introduced into the workflow to reduce the occlusion in the final result. Since high-resolution cameras have the ability to be mounted on unmanned aerial vehicles (UAVs), typical areas of occlusion associated with ground-based LiDAR and supported structural coverings (e.g. roof or bridge deck) can be reconstructed. In this approach, aerial SfM is selected due to the low investment and operational costs in comparison to airborne LiDAR. This paper demonstrates the techniques and results of both LiDAR and aerial SfM for a case study building. Images captured with a UAV supplement the collected LiDAR and allow for a holistic scene reconstruction. The benefits of deployment of a combined remote sensing platform, such as this, are demonstrated in the case of reconnaissance in the aftermath of extreme events

Post-Earthquake Structural Damage Assessment Through Point Cloud Data

Structural damage assessment following an extreme event can provide valuable information and insight into unanticipated damage and failure modes to improve design philosophies and design codes as well as reduce vulnerability. Oftentimes, structural engineers create finite element models (FEM) of the structure in which numerous model parameters require calibration to simulate the current state. This information may include structural plan details (geometry), material characteristics (strength and stiffness parameters), as well as observed damage patterns (cracks, spalling, etc.). Ground-based lidar (GBL) scans and Structure-from-Motion (SfM) can rapidly capture dimensionally accurate point clouds of the structure or facility of interest. Furthermore, point clouds can used to efficiently document perishable structural damage data digitally prior recovery or retrofit efforts. Within these point clouds, information can be extracted to objectively locate damage patterns in non-temporal datasets. Localization and quantification of damage can serve to update models with high fidelity within forensic investigations as well as to estimate the remaining structural capacity. In this work, an algorithm based on two spatially invariant geometrical features was used to identify and quantify structural damage from point cloud data for two case study buildings. The first case-study building is an 18-story high-rise condominium building that was significantly damaged during the 2015 Gorkha (Nepal) Earthquake. The damage included significant cracks in partition walls, unreinforced masonry infill walls, and section-loss within coupling beams and staircases at various levels. The second case-study structure, from the same earthquake event, is a five-tiered pagoda style temple built using timber beams and thick brick masonry walls. The temple sustained moderate damage where shear cracks developed at lower levels and seam of the wall piers. Through the developed damage detection method, cracking, concrete spalling, and loss of cross-section within the point cloud data of the nonstructural and structural elements are quantified

Francis Turbine Draft Tube Troubleshooting during Operational Conditions Using CFD Analysis

Hydropower plant vibrations due to pressure fluctuations and their troubleshooting methods are some of the most challenging issues in power plant operation and maintenance. This paper targets these fluctuations in a prototype turbine in two geometries: the initially approved design and the as-built design. Due to topographic conditions downstream, these geometries slightly differ in the draft tube height; the potential effect of such a slight geometrical change on the applicability of troubleshooting techniques is investigated. Therefore, the water flow was simulated using the CFD scheme at three operating points based on the SST k–ω turbulence model, while the injection of water/air was examined to decrease the pressure fluctuations in the draft tube, and the outputs were compared with no-injection simulations. The results show that a slight change in draft tube geometry causes the pressure fluctuations to increase 1.2 to 2.8 times after 4 s injecting at different operating points. The modification in the location of the air injection also could not reduce the increase in pressure fluctuations and caused a 3.6-fold increase in pressure fluctuations. Therefore, the results show that despite water/air injection being a common technique in the hydropower industry to reduce pressure fluctuations, it is effective only in the initially approved design geometry. At the same time, it has a reverse effect on the as-built geometry and increases the pressure fluctuations. This research highlights the importance of binding the construction phase with the design and troubleshooting stages and how slight changes in construction can affect operational issues.<br/

Patient perceptions of pharmacist roles in guiding self-medication of over-the-counter therapy in Qatar

Kerry Wilbur1, Samah El Salam1, Ebrahim Mohammadi21Qatar University College of Pharmacy, Doha, Qatar; 2Qatar Petroleum Medical Services, Doha, QatarBackground: Self-care, including self-medication with over-the-counter (OTC) drugs, facilitates the public&amp;rsquo;s increased willingness to assume greater responsibility for their own health. Direct consultation with pharmacists provides efficient professional guidance for safe and appropriate OTC use.Objective: The purpose of this study was to characterize patient perceptions of pharmacists and use of nonprescription therapy in an ambulatory care population in Qatar. Methods: Patients having prescriptions filled at one organization&amp;rsquo;s private medical clinics during two distinct two-week periods were invited to participate in a short verbal questionnaire. Awareness of pharmacist roles in guiding OTC drug selection was assessed, as were patient preferences for OTC indications. Attitudes towards pharmacist and nurse drug knowledge and comfort with direct dispensing were also evaluated.Results: Five hundred seventy patients participated representing 29 countries. Most respondents were men (92.1%) with mean age of 38.3 years. Almost 1 in 7 did not know medical complaints could be assessed by a pharmacist (15.3%) and 1 in 5 (21.9%) were unaware pharmacists could directly supply OTC therapy. The majority (85.3%) would be interested in this service. In general, respondents were more comfortable with medication and related advice supplied by pharmacists as opposed to nursing professionals.Conclusion: Patients were familiar with the roles of pharmacists as they pertain to selfmedication with OTC therapy and described the desire to use such a service within this Qatar ambulatory health care setting.Keywords: patient, self-medication, over-the-counter, pharmacist, Qata

Deep Learning Classification of 2D Orthomosaic Images and 3D Point Clouds for Post-Event Structural Damage Assessment

Efficient and rapid data collection techniques are necessary to obtain transitory information in the aftermath of natural hazards, which is not only useful for post-event management and planning, but also for post-event structural damage assessment. Aerial imaging from unpiloted (gender-neutral, but also known as unmanned) aerial systems (UASs) or drones permits highly detailed site characterization, in particular in the aftermath of extreme events with minimal ground support, to document current conditions of the region of interest. However, aerial imaging results in a massive amount of data in the form of two-dimensional (2D) orthomosaic images and three-dimensional (3D) point clouds. Both types of datasets require effective and efficient data processing workflows to identify various damage states of structures. This manuscript aims to introduce two deep learning models based on both 2D and 3D convolutional neural networks to process the orthomosaic images and point clouds, for post windstorm classification. In detail, 2D convolutional neural networks (2D CNN) are developed based on transfer learning from two well-known networks AlexNet and VGGNet. In contrast, a 3D fully convolutional network (3DFCN) with skip connections was developed and trained based on the available point cloud data. Within this study, the datasets were created based on data from the aftermath of Hurricanes Harvey (Texas) and Maria (Puerto Rico). The developed 2DCNN and 3DFCN models were compared quantitatively based on the performance measures, and it was observed that the 3DFCN was more robust in detecting the various classes. This demonstrates the value and importance of 3D datasets, particularly the depth information, to distinguish between instances that represent different damage states in structures

Deep Learning Classification of 2D Orthomosaic Images and 3D Point Clouds for Post-Event Structural Damage Assessment

Efficient and rapid data collection techniques are necessary to obtain transitory information in the aftermath of natural hazards, which is not only useful for post-event management and planning, but also for post-event structural damage assessment. Aerial imaging from unpiloted (gender-neutral, but also known as unmanned) aerial systems (UASs) or drones permits highly detailed site characterization, in particular in the aftermath of extreme events with minimal ground support, to document current conditions of the region of interest. However, aerial imaging results in a massive amount of data in the form of two-dimensional (2D) orthomosaic images and three-dimensional (3D) point clouds. Both types of datasets require effective and efficient data processing workflows to identify various damage states of structures. This manuscript aims to introduce two deep learning models based on both 2D and 3D convolutional neural networks to process the orthomosaic images and point clouds, for post windstorm classification. In detail, 2D convolutional neural networks (2D CNN) are developed based on transfer learning from two well-known networks AlexNet and VGGNet. In contrast, a 3D fully convolutional network (3DFCN) with skip connections was developed and trained based on the available point cloud data. Within this study, the datasets were created based on data from the aftermath of Hurricanes Harvey (Texas) and Maria (Puerto Rico). The developed 2DCNN and 3DFCN models were compared quantitatively based on the performance measures, and it was observed that the 3DFCN was more robust in detecting the various classes. This demonstrates the value and importance of 3D datasets, particularly the depth information, to distinguish between instances that represent different damage states in structures