Flexural strength characteristics of non-load bearing masonry walls in Kuwait

The controlling factor in designing non-load bearing masonry walls, such as those used in Kuwait, is the lateral resistance to wind loads. To ensure safety of the walls, data is needed on the flexural strength characteristics of walls constructed with locally-available materials. The flexural strength of masonry walls constructed with autoclaved aerated-concrete blocks, sand-cement concrete blocks or calcium silicate bricks was evaluated in a test program that involved testing small-scale walls or wallettes. The tests were performed in accordance with the British Standard for unreinforced masonry. The autoclaved aerated-concrete block wallettes were constructed with epoxy glue mortar, whereas the concrete block and calcium silicate brick walletters were constructed with sand-cement mortar. Two stages of testing were undertaken to evaluate bending parallel to bed joints and bending perpendicular to bed joints. The flexural strengths required by British and American codes exceed the strengths of the concrete block and calcium silicate brick walls used in Kuwait, implying that the allowable tensile stress requirements of these codes are not safe for assessing the lateral resistance of the walls. The format used for the autoclaved aerated-concrete block wallters, which is identical to the standardized format for concrete block wallettes in the British standard, is suitable for determining the flexural strength of full-size autoclaved aerated-concrete block walls.     

Click through Rate Effectiveness Prediction on Mobile Ads Using Extreme Gradient Boosting

Online advertisements have a significant influence over the success or failure of your business. Therefore, it is important to somehow measure the impact of your advertisement before uploading it online, and this is can be done by calculating the Click Through Rate (CTR). Unfortunately, this method is not eco-friendly, since you have to gather the clicks from users then compute the CTR. This is where CTR prediction come in handy. Advertisement CTR prediction relies on the users’ log regarding click information data. Accurate prediction of CTR is a challenging and critical process for e-advertising platforms these days. CTR prediction uses machine learning techniques to determine how much the online advertisement has been clicked by a potential client: The more clicks, the more successful the ad is. In this study we develop a machine learning based click through rate prediction model. The proposed study defines a model that generates accurate results with low computational power consumption. We used four classification techniques, namely K Nearest Neighbor (KNN), Logistic Regression, Random Forest, and Extreme Gradient Boosting (XGBoost). The study was performed on the Click-Through Rate Prediction Competition Dataset. It is a click-through data that is ordered chronologically and was collected over 10 days. Experimental results reveal that XGBoost produced ROC-AUC of 0.76 with reduced number of features.