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

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

Deep Learning-Based Damage Detection from Aerial SfM Point Clouds

Aerial data collection is well known as an efficient method to study the impact following extreme events. While datasets predominately include images for post-disaster remote sensing analyses, images alone cannot provide detailed geometric information due to a lack of depth or the complexity required to extract geometric details. However, geometric and color information can easily be mined from three-dimensional (3D) point clouds. Scene classification is commonly studied within the field of machine learning, where a workflow follows a pipeline operation to compute a series of engineered features for each point and then points are classified based on these features using a learning algorithm. However, these workflows cannot be directly applied to an aerial 3D point cloud due to a large number of points, density variation, and object appearance. In this study, the point cloud datasets are transferred into a volumetric grid model to be used in the training and testing of 3D fully convolutional network models. The goal of these models is to semantically segment two areas that sustained damage after Hurricane Harvey, which occurred in 2017, into six classes, including damaged structures, undamaged structures, debris, roadways, terrain, and vehicles. These classes are selected to understand the distribution and intensity of the damage. The point clouds consist of two distinct areas assembled using aerial Structure-from-Motion from a camera mounted on an unmanned aerial system. The two datasets contain approximately 5000 and 8000 unique instances, and the developed methods are assessed quantitatively using precision, accuracy, recall, and intersection over union metrics

Multi-Scale Remote Sensing of Tornado Effects

To achieve risk-based engineered structural designs that provide safety for life and property from tornadoes, sufficient knowledge of tornado wind speeds and wind flow characteristics is needed. Currently, sufficient understanding of the magnitude, frequency, and velocity structure of tornado winds remain elusive. Direct measurements of tornado winds are rare and nearly impossible to acquire, and the pursuit of in situ wind measurements can be precarious, dangerous, and even necessitating the development of safer and more reliable means to understand tornado actions. Remote-sensing technologies including satellite, aerial, lidar, and photogrammetric platforms, have demonstrated an ever-increasing efficiency for collecting, storing, organizing, and communicating tornado hazards information at a multitude of geospatial scales. Current remote-sensing technologies enable wind-engineering researchers to examine tornado effects on the built environment at various spatial scales ranging from the overall path to the neighborhood, building, and ultimately member and/or connection level. Each spatial resolution contains a unique set of challenges for efficiency, ease, and cost of data acquisition and dissemination, as well as contributions to the body of knowledge that help engineers and atmospheric scientists better understand tornado wind speeds. This paper examines the use of remote sensing technologies at four scales in recent tornado investigations, demonstrating the challenges of data collection and processing at each level as well as the utility of the information gleaned from each level in advancing the understanding of tornado effects

Low-contrast Pattern-reversal Visual Evoked Potential in Different Spatial Frequencies

Purpose: To evaluate the pattern-reversal visual evoked potential (PRVEP) in lowcontrast, spatial frequencies in time, frequency, and time-frequency domains. Methods: PRVEP was performed in 31 normal eyes, according to the International Society of Electrophysiology of Vision (ISCEV) protocol. Test stimuli had checkerboard of 5% contrast with spatial frequencies of 1, 2, and 4 cycles per degree (cpd). For each VEP waveform, the time domain (TD) analysis, Fast Fourier Transform(FFT), and discrete wavelet transform (DWT) were performed using MATLAB software. The VEP component changes as a function of spatial frequency (SF) were compared among time, frequency, and time–frequency dimensions. Results: As a consequence of increased SF, a significant attenuation of the P100 amplitude and prolongation of P100 latency were seen, while there was no significant difference in frequency components. In the wavelet domain, an increase in SF at a contrast level of 5% enhanced DWT coefficients. However, this increase had no meaningful effect on the 7P descriptor. Conclusion: At a low contrast level of 5%, SF-dependent changes in PRVEP parameters can be better identified with the TD and DWT approaches compared to the Fourier approach. However, specific visual processing may be seen with the wavelet transform

A Novel Multi-Criteria Decision-Making Framework in Electrical Utilities Based on Gray Number Approach

Given the current trend of reviving the power system, which is considered by competitive markets, the privatization of the power system is forcing them to develop the necessary decision-making policies from a technical and economic point of view to improve their asset management practices. Reliability-centered maintenance is an efficient process to consider these two important aspects, i.e. technical and economic ones when performing maintenance optimization. This paper proposes a new technique to solve the actual stochastic Multi-Criteria Decision-Making (MCDM) problems with uncertain weight information using a combination of Stochastic Multi-Criteria Acceptability Analysis (SMAA) and Elimination Et Choice Translating Reality (ELECTREIII) methods combined with gray system theory. In maintenance planning, gray system theory is used to determine the specific types of power system components that should receive the most attention. Then, the optimal maintenance strategy of every critical component is determined by recognizing the lowest costs associated with various strategies. The suggested framework demonstrates its relevance and efficacy for actual asset management optimizations in electric power systems, as demonstrated in the IEEE 14-bus test system.© 2022 the Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed

Determination of histamine in Iranian cheese using enzyme-linked immunosorbent assay (ELISA) method

Histamine is a simple chemical substance created during processing of the amine acid histidine. Histamine is also an agent in inflammation and the increased presence of histamine causes allergic reaction. Histamine may play a role in the increased prevalence of food intolerances. The objective of this study was to determine histamine contents. Forty four (44) samples of traditional and commercial cheese were analyzed by enzyme-linked immunosorbent assay (ELISA) method in Iran. In the two cheese samples of the 44 samples (4.5%), the presence of histamine was 26 and 46.7 mg/100 g. Histamine in any of the cheese samples was not higher than the tolerance limit of histamine contents (50 mg histamine/100 g) accepted by European countries. The values were comparable and in the range of the literature values. The results of this study indicate that the produced cheese and marketed cheese in Iran have concentrations below 50 mg histamine/100 g. Further studies should be done to investigate the presence of this toxin in different foodstuffs.Keywords: Histamine, cheese, enzyme-linked immunosorbent assay (ELISA), IranAfrican Journal of Biotechnology Vol. 12(3), pp. 308-31