Efficient design in building construction with rubber bearing in medium risk seismicity: case study and assessment

Earthquakes pose tremendous threats to life, property and a country's economy, not least due to their capability of destroying buildings and causing enormous structural damage. The hazard from ground excitations should be properly assessed to mitigate their action on building structures. This study is concerned with medium risk seismic regions. Specifically, the heavily populated capital city Dhaka in Bangladesh has been considered. Recent earthquakes that occurred inside and very close to the city have manifested the city's earthquake sources and vulnerability. Micro-seismicity data supports the existence of at least four earthquake source points in and around Dhaka. The effects of the earthquakes on buildings are studied for this region. Rubber base isolation is selected as an innovative option to lessen seismic loads on buildings. Case studies have been carried out for fixed and isolated based multi-storey buildings. Lead rubber bearing and high damping rubber bearing have been designed and incorporated in building bases. Structural response behaviours have been evaluated through static and dynamic analyses. For the probable severe earthquake, rubber bearing isolation can be a suitable alternative as it mitigates seismic effects, reduces structural responses and provides structural and economic benefits

HipXNet: Deep Learning Approaches to Detect Aseptic Loos-Ening of Hip Implants Using X-Ray Images

Radiographic images are commonly used to detect aseptic loosening of the hip implant in patients with total hip replacement (THR) surgeries. These techniques of manual assessment by medical professionals can suffer from the drawback of low accuracy, poor inter-observer reliability, and delays due to the unavailability of experienced clinicians. Thus, the paper provides a reliable Deep Convolutional Neural Networks (DCNNs) based novel stacking approach (HipXNet) for detecting loosening of the hip implant using X-ray images. Two major investigations were done in this study. Firstly, the performance of four different state-of-the-art object detection YOLOv5 models was evaluated to detect the implant region from the hip X-ray images. Secondly, the study developed a stacking classifier using three different Convolutional neural networks (CNN) models to classify aseptic hip loosening and compared the performance with eight different state-of-the-art CNN networks. Moreover, one publicly accessible dataset with two sub-sets was created for these two experiments, where 200 hip implant X-ray images were collected and annotated by two expert radiologists for implant detection and 206 hip implant X-ray images were collected for loosening detection. YOLOv5m model outperformed the other variants of YOLOv5 to detect the implant region with the precision, recall, mean average precision (mAP)0.5, mAP0.5-0.95 of 100%, 100%, 100%, and 87.8%, respectively. Densenet201 CNN model outperformed other CNN models with the accuracy, precision, sensitivity, F1 score, and specificity of 94.66%, 94.66%, 94.66%, 94.66%, and 94.5%, respectively while the stacking technique with Random Forest meta learner classifier produced the best performance with the accuracy, precision, sensitivity, F1 score and specificity of 96.11%, 96.42%, 96.42%, 96.42%, and 96.74% respectively for loosening detection. The reliability of the performance was confirmed by the popular Score-CAM visualization. This study can help in the early and fast identification of hip implant loosening with the help of simple X-ray images and computed aided diagnosis. 2013 IEEE.This work was supported in part by the Qatar National Research Fund (QNRF) under Grant NPRP11S-0102-180178, and in part by the Qatar National Library.Scopu

Study of the Use of Ferronickel Slag in Concrete

This research evaluates the suitability of using ground granulated ferronickel slag (GFNS) as a partial replacement of cement in concrete. The high magnesium content of GFNS was present as stable forsterite ferroan that did not take part in hydration reaction. Overall, the soundness, strength and durability properties of GFNS blended cement binders were found comparable to those of binders blended with other common supplementary cementitious materials such as class F fly ash

DESIGN AND NON-LINEAR ANALYSIS OF A PARABOLIC LEAF SPRING

Abstract: Tapered cantilever beams, traditionally termed as leaf springs, undergo much larger deflections in comparison to a beam of constant cross-section that takes their study in the domain of geometric nonlinearity. This paper studies response of a leaf spring of parabolic shape, assumed to be made of highly elastic steel. Numerical simulation was carried out using both the small and large deflection theories to calculate the stress and the deflection of the same beam. Non-linear analysis is found to have significant effect on the beam’s response under a tip load. It is seen that the actual bending stress at the fixed end, calculated by nonlinear theory, is 2.30-3.39 % less in comparison to a traditional leaf spring having the same volume of material. Interestingly, the maximum stress occurs at a region far away from the fixed end of the designed parabolic leaf spring

Sustainable water use in construction

Water is needed in direct construction activities and also for the production of construction materials as embodied water. Water use during the life time of an infrastructure also needs to be considered in the planning and design phase of the infrastructure to save and conserve water in the long run. A typical building is associated with water use in direct and indirect forms. Direct forms include water consumed by workers, water used in washing aggregates, preparing raw concrete, curing concrete, dust suppression and washing of hard surfaces and equipment. Indirect use is related to embodied water, which has been used in production of construction materials. Use of water-efficient devices can save significant volumes of water during the operation phase of a building. It is expected that a greater environmental awareness, favorable government policy and continuing education will enhance the water use efficiency in the construction industry

TB-CXRNet: Tuberculosis and Drug-Resistant Tuberculosis Detection Technique Using Chest X-ray Images

Tuberculosis (TB) is a chronic infectious lung disease, which caused the death of about 1.5 million people in 2020 alone. Therefore, it is important to detect TB accurately at an early stage to prevent the infection and associated deaths. Chest X-ray (CXR) is the most popularly used method for TB diagnosis. However, it is difficult to identify TB from CXR images in the early stage, which leads to time-consuming and expensive treatments. Moreover, due to the increase of drug-resistant tuberculosis, the disease becomes more challenging in recent years. In this work, a novel deep learning-based framework is proposed to reliably and automatically distinguish TB, non-TB (other lung infections), and healthy patients using a dataset of 40,000 CXR images. Moreover, a stacking machine learning-based diagnosis of drug-resistant TB using 3037 CXR images of TB patients is implemented. The largest drug-resistant TB dataset will be released to develop a machine learning model for drug-resistant TB detection and stratification. Besides, Score-CAM-based visualization technique was used to make the model interpretable to see where the best performing model learns from in classifying the image. The proposed approach shows an accuracy of 93.32% for the classification of TB, non-TB, and healthy patients on the largest dataset while around 87.48% and 79.59% accuracy for binary classification (drug-resistant vs drug-sensitive TB), and three-class classification (multi-drug resistant (MDR), extreme drug-resistant (XDR), and sensitive TB), respectively, which is the best reported result compared to the literature. The proposed solution can make fast and reliable detection of TB and drug-resistant TB from chest X-rays, which can help in reducing disease complications and spread

PCovNet: A presymptomatic COVID-19 detection framework using deep learning model using wearables data

While the advanced diagnostic tools and healthcare management protocols have been struggling to contain the COVID-19 pandemic, the spread of the contagious viral pathogen before the symptom onset acted as the Achilles' heel. Although reverse transcription-polymerase chain reaction (RT-PCR) has been widely used for COVID-19 diagnosis, they are hardly administered before any visible symptom, which provokes rapid transmission. This study proposes PCovNet, a Long Short-term Memory Variational Autoencoder (LSTM-VAE)-based anomaly detection framework, to detect COVID-19 infection in the presymptomatic stage from the Resting Heart Rate (RHR) derived from the wearable devices, i.e., smartwatch or fitness tracker. The framework was trained and evaluated in two configurations on a publicly available wearable device dataset consisting of 25 COVID-positive individuals in the span of four months including their COVID-19 infection phase. The first configuration of the framework detected RHR abnormality with average Precision, Recall, and F-beta scores of 0.946, 0.234, and 0.918, respectively. However, the second configuration detected aberrant RHR in 100% of the subjects (25 out of 25) during the infectious period. Moreover, 80% of the subjects (20 out of 25) were detected during the presymptomatic stage. These findings prove the feasibility of using wearable devices with such a deep learning framework as a secondary diagnosis tool to circumvent the presymptomatic COVID-19 detection problem. 2022 Elsevier LtdThis work was supported by the Qatar National Research Grant: UREP28-144-3-046. The statements made herein are solely the responsibility of the authors.Scopu