Background and Objective: The novel Coronavirus also called COVID-19 originated in Wuhan, China in December 2019 and has now spread across the world. It has so far infected around 1.8 million people and claimed approximately 114,698 lives overall. As the number of cases are rapidly increasing, most of the countries are facing shortage of testing kits and resources. The limited quantity of testing kits and increasing number of daily cases encouraged us to come up with a Deep Learning model that can aid radiologists and clinicians in detecting COVID-19 cases using chest X-rays.Methods: In this study, we propose CoroNet, a Deep Convolutional Neural Network model to automatically detect COVID-19 infection from chest X-ray images. The proposed model is based on Xception architecture pre-trained on ImageNet dataset and trained end-to-end on a dataset prepared by collecting COVID-19 and other chest pneumonia X-ray images from two different publically available databases.Results: CoroNet has been trained and tested on the prepared dataset and the experimental results show that our proposed model achieved an overall accuracy of 89.6%, and more importantly the precision and recall rate for COVID-19 cases are 93% and 98.2% for 4-class cases (COVID vs Pneumonia bacterial vs pneumonia viral vs normal). For 3-class classification (COVID vs Pneumonia vs normal), the proposed model produced a classification accuracy of 95%. The preliminary results of this study look promising which can be further improved as more training data becomes available. Conclusion:CoroNet achieved promising results on a small prepared dataset which indicates that given more data, the proposed model can achieve better results with minimum pre-processing of data. Overall, the proposed model substantially advances the current radiology based methodology and during COVID-19 pandemic, it can be very helpful tool for clinical practitioners and radiologists to aid them in diagnosis, quantification and follow-up of COVID-19 cases.

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Half-scaled reinforced concrete frame of two storeys and two bays with unreinforced masonry (URM) infill walls was subjected to base excitation on a shake table for seismic performance evaluation. Considering the high seismic hazard Zone IV of Pakistan, reinforcement detailing in the RC frame is provided according to special moment resisting frames (SMFRs) requirement of Building Code of Pakistan Seismic-Provisions (BCP SP-2007). The reinforced concrete frame was infilled with in-plane solid masonry walls in its interior frame, in-plane masonry walls with door and window openings in the exterior frame, out-of-plane solid masonry wall, and masonry wall with door and window openings in its interior frame. For seismic capacity qualification test, the structure was subjected to three runs of unidirectional base excitation with increasing intensity. For system identification, ambient-free vibration tests were performed at different stages of experiment. Seismic performance of brick masonry infill walls in reinforced concrete frame structures was evaluated. During the shake table test, performance of URM infill walls was satisfactory until design ground acceleration was 0.40g with a global drift of 0.23%. The test was continued till 1.24g of base acceleration. This paper presents key findings from the shake table tests, including the qualitative damage observations and quantitative force-displacement, and hysteretic response of the test specimen at different levels of excitation. Experimental results of this test will serve as a benchmark for validation of numerical and analytical models.