In the present study, the mechanical properties of self-compacting concrete were investigated after the addition of different amounts of ZnO nanoparticles. The zinc oxide nanoparticles, with an average particle size of about 30 nm, were synthesized and their properties studied with the help of a scanning electron microscope (SEM) and X-ray diffraction. The prepared nanoparticles were partially added to self-compacting concrete at different concentrations (0.05, 0.1, 0.2, 0.5 and 1.0%), and the mechanical (flexural and split tensile) strength of the specimens measured after 7, 14, 21 and 28 days, respectively. The present results have shown that the ZnO nanoparticles were able to improve the flexural strength of self-compacting concrete. The increased ZnO content of more than 0.2% could increase the flexural strength, and the maximum flexural and split tensile strength was observed after the addition of 0.5% nanoparticles. Finally, ZnO nanoparticles could improve the pore structure of the self-compacted concrete and shift the distributed pores to harmless and less-harmful pores, while increasing mechanical strength.

This paper introduces a new application of Deep Belief Network (DBN) as an emerging artificial neural network for recognizing and classifying different partial discharge (PD) patterns. Phase Resolved Partial Discharge (PRPD) technique with different window intervals is used to manipulate three PD types including corona, surface and internal discharges measured in a High Voltage (HV) lab. Four approaches are proposed for extracting features from the raw measured data. In the first approach, the DBN is used as both a feature extractor and a PD classifier. The other three approaches extract discriminatory features using statistical and vector norm based operators to train a DBN classifier. The impact of the various phase windows through the PRPD method on the performance of the trained classifier is evaluated to obtain the best window interval. It is shown that the deep architectures are capable of learning important distinguishable features from PD data without any data preprocessing. This eliminates time-consuming feature extraction processes which produce the hand crafted features. Based on a comparison analysis, when the input data is corrupted by noise levels or no feature extraction technique is used to preprocess the data, the proposed approach outperforms other techniques such as Artificial Neural Networks (ANNs), Fuzzy logic classifiers and Support Vector Machines (SVMs).