Metallographic Preparation of Zn-21Al-2Cu Alloy for Analysis by Electron Backscatter Diffraction (EBSD)
Abstract Samples of Zn-21Al-2Cu alloy (Zinalco) that will be heavily deformed were prepared using five different manual mechanical metallographic methods. Samples were analyzed before tensile testing using the orientation imaging microscopy-electron backscatter diffraction (OIM-EBSD) technique. The effect of type and particle size during the final polishing stages for this material were studied in order to identify a method that produces a flat, damage free surface with a roughness of about 50 nm and clean from oxide layers, thereby producing diffraction patterns with high image quality (IQ) and adequate confidence indexes (CI). Our results show that final polishing with alumina and silica, as was previously suggested by other research groups for alloys that are difficult to prepare or alloys with low melting point, are not suitable for manual metallographic preparation of this alloy. Indexes of IQ and CI can be used to evaluate methods of metallographic preparation of samples studied using the OIM-EBSD technique.
EBSD of Zn-rich phases in Zn–Al-based alloys
Abstract Based on microstructure characterization, electron back-scatter Kikuchi diffraction (EBSD) methodology was used on a multi-phase alloy ZnAl4Cu11 (in wt.%). Electron back-scatter Kikuchi diffraction patterns (EBSDPs) were produced from the bulk Zn–Al-based alloy specimen using solely mechanical polishing instead of mechanical and electro-polishing. Both EBSDs of the Zn-rich hexagonal close-packed structure (hcp) phases of e and η were distinguished for the first time using pre-determined lattice parameters of these phases as the computer database for the multi-phase alloy. These two procedures were practical developments of EBSD technology in the study of crystalline orientation and phase identification.
Application of neural networks in bridge health prediction based on acceleration and displacement data domain
The health condition of the bridge can be predicted through sensors’ reading in bridge monitoring. The 
sensors measure the acceleration and displacement of bridge response. The data is sent to the local server through the data acquisition. Interpretation of the data applied neural network in the localized server system. This paper aims to define performance of the acceleration and displacement data domain as input in applied neural networks. The architecture of neural networks’ model used an input layer, one and two hidden layers with n neurons and an output layer. The input layer consists of time-acceleration domain and time-displacement domain of the 
bridge due to earthquake loads. Meanwhile, the output layer consists of bridge condition level which is determined using finite-element analysis software. The training activation used Gradient Descent Back-propagation and activation transfer function used Log Sigmoid function. The bridge condition is categorized in a range 0 to 3, which indicates the extent of bridge health condition ranging from safe to high-risk level. 
The case study is 3 spans of box-girder’s bridge subject to four earthquakes loads. The results showed that the prediction of bridge health condition based on displacement data domain with one hidden layer is more acceptable compared with based on acceleration. The comparison obtains the recommendation of the best of data reading from the sensors to predict the bridge health condition. The application neural networks in the bridge 
health prediction can help the authorities to know the condition of the bridge due to earthquake at monitoring time, as the repair and maintenance of bridges can be performed as early as possible before the bridge was damaged.