Effect of earth reinforcement,soil properties and wall properties on bridge MSE walls

Mechanically stabilized earth (MSE) retaining walls are popular for highway bridge structures. They have precast concrete panels attached to earth reinforcement. The panels are designed to have some lateral movement. However, in some cases, excessive movement and even complete dislocation of the panels have been observed. In this study, 3-D numerical modeling involving an existing MSE wall was undertaken to investigate various wall parameters. The effects of pore pressure, soil cohesion, earth reinforcement type and length, breakage/slippage of reinforcement and concrete strength, were examined. Results showed that the wall movement is affected by soil pore pressure and reinforcement integrity and length, and unaffected by concrete strength. Soil cohesion has a minor effect, while the movement increased by 13–20 mm for flexible geogrid reinforced walls compared with the steel grid walls. The steel grid stresses were below yielding, while the geogrid experienced significant stresses without rupture. Geogrid reinforcement may be used taking account of slippage resistance and wall movement. If steel grid is used, non-cohesive soil is recommended to minimize corrosion. Proper soil drainage is important for control of pore pressure.     

Texture Evolution During Cross Rolling and Annealing of High-Purity Nickel

Evolution of texture during cross rolling and subsequent annealing was studied in high-purity nickel. For this purpose nickel samples were subjected to multipass cross rolling up to 90 pct reduction in thickness followed by annealing at different temperatures ranging between 673 K and 1073 K (400 °C and 800 °C). Cross rolling was carried out by rotating the samples about the normal direction (ND) by 90 deg interchanging the rolling direction and transverse direction (TD) between each consecutive pass. The development of microstructure and texture was characterized using X-ray and electron backscattered diffraction (EBSD) techniques. The deformation texture was characterized by the presence of strong brass ({110}〈112〉) and ND-rotated brass ({011}〈21 $ \overline{13} $ 13〉)) orientations. Upon annealing at 673 K (400 °C), ND||[111] fiber could be observed in the microtexture which originated from the twin formation of the recrystallized TD-rotated cube ({027}〈0 $ \overline{7} $ 2〉) grains. The fiber was weakened after annealing at 1073 K (800 °C) because of the decreased propensity for twin formation, and the microtexture was found to be weak and diffused. EBSD studies on early recrystallization stages indicated the absence of preferential nucleation of cube grains being in agreement with a weak cube texture formation in annealed cross-rolled high-purity nickel.     

Hill Matrix and Radix-64 Bit Algorithm to Preserve Data Confidentiality

There are many cloud data security techniques and algorithms available that can be used to detect attacks on cloud data, but these techniques and algorithms cannot be used to protect data from an attacker. Cloud cryptography is the best way to transmit data in a secure and reliable format. Various researchers have developed various mechanisms to transfer data securely, which can convert data from readable to unreadable, but these algorithms are not sufficient to provide complete data security. Each algorithm has some data security issues. If some effective data protection techniques are used, the attacker will not be able to decipher the encrypted data, and even if the attacker tries to tamper with the data, the attacker will not have access to the original data. In this paper, various data security techniques are developed, which can be used to protect the data from attackers completely. First, a customized American Standard Code for Information Interchange (ASCII) table is developed. The value of each Index is defined in a customized ASCII table. When an attacker tries to decrypt the data, the attacker always tries to apply the predefined ASCII table on the Ciphertext, which in a way, can be helpful for the attacker to decrypt the data. After that, a radix 64-bit encryption mechanism is used, with the help of which the number of cipher data is doubled from the original data. When the number of cipher values is double the original data, the attacker tries to decrypt each value. Instead of getting the original data, the attacker gets such data that has no relation to the original data. After that, a Hill Matrix algorithm is created, with the help of which a key is generated that is used in the exact plain text for which it is created, and this Key cannot be used in any other plain text. The boundaries of each Hill text work up to that text. The techniques used in this paper are compared with those used in various papers and discussed that how far the current algorithm is better than all other algorithms. Then, the Kasiski test is used to verify the validity of the proposed algorithm and found that, if the proposed algorithm is used for data encryption, so an attacker cannot break the proposed algorithm security using any technique or algorithm.     

Unsupervised defect segmentation with pose priors

Single shot, semantic bounding box detectors, trained in a supervised manner are popular in computer vision-aided visual inspections. These methods have several key limitations: (1) bounding boxes capture too much background, especially when images experience perspective transformation; (2) insufficient domain-specific data and cost to label; and (3) redundant or incorrect detection results on videos or multi-frame data; where it is a nontrivial task to select the best detection and check for outliers. Recent developments in commercial augmented reality and robotic hardware can be leveraged to support inspection tasks. A common capability of the previous is the ability to obtain image sequences and camera poses. In this work, the authors leverage pose information as “prior” to address the limitations of existing supervised learned, single-shot, semantic detectors for the application of visual inspection. The authors propose an unsupervised semantic segmentation method (USP), based on unsupervised learning for image segmentation inspired by differentiable feature clustering coupled with a novel outlier rejection and stochastic consensus mechanism for mask refinement. USP was experimentally validated for a spalling quantification task using a mixed reality headset (Microsoft HoloLens 2). Also, a sensitivity study was conducted to evaluate the performance of USP under environmental or operational variations.