Bond behavior of lightweight steel fibre-reinforced concrete

This research was undertaken for studying the bond behaviour of Lightweight Fibre-reinforced Concrete (LWFC). Lightweight concrete is inherently weak in tension and has higher brittleness than the conventional concrete. To improve these and other properties, it is generally reinforced with deformed bars and fibres. There are number of studies that favour the use of Steel fibres, however such studies are mainly focused either on normal weight concrete or on the mechanical properties of different concretes. There are also different committee reports and in some cases specific sections of codes that specifically deal with the normal weight fibre-reinforced concrete. However, such is not the case with lightweight fibre-reinforced concrete; there is limited literature available especially on the Bond of lightweight fibre-reinforced concrete. In current research work effect of fibres is studied on the bond behaviour of the lightweight reinforced concrete. Since most of code provisions for bond are based on experimental work originally carried out on conventional concrete, effect of fibres on bond of conventional concrete was therefore also included in present research domain. Main bond tests were carried out using Pull-out test methodology. Test results indicate that the ultimate bond strength of conventional concrete when reinforced with steel fibres increased by 29%. However due to very low density and high porosity of lightweight aggregates, no significant improvement on bond strength of LWFC, as a result of fibres’ addition could be observed. Nevertheless, there is noteworthy improvement in the post-cracking bond strength of LWFC. Besides this, current bond-stress slip law as defined by Model Code 2010 does not reflect the positive effect of fibres, hence some modifications are suggested. It is also found that among the existing code expressions for estimation of bond strength, expression proposed by Model Code 2010 presents better results and its effectiveness can be further increased if fibre factor and factor for lightweight concrete are considered

Towards Autonomous and Efficient Machine Learning Systems

Computation-intensive machine learning (ML) applications are becoming some of the most popular workloads running atop cloud infrastructure. While training ML applications, practitioners face the challenge of tuning various system-level parameters, such as the number of training nodes, communication topology during training, instance type, and the number of serving nodes, to meet the SLO requirements for bursty workload during the inference. Similarly, efficient resource utilization is another key challenge in cloud computing. This dissertation proposes high-performing and efficient ML systems to speed up training time and inference tasks while enabling automated and robust system management.To train an ML model in a distributed fashion we focus on strategies to mitigate the resource provisioning overhead and improve the training speed without impacting the model accuracy. More specifically, a system for autonomic and adaptive scheduling is built atop serverless computing that dynamically optimizes deployment and resource scaling for ML training tasks for cost-effectiveness and fast training. Similarly, a dynamic client selection framework is developed to address the stragglers problem caused by resource heterogeneity, data quality, and data quantity in a privacy-preserving Federated Learning (FL) environment without impacting the model accuracy.For serving bursty ML workloads we focus on developing highly scalable and adaptive strategies to serve the dynamically changing workload in a cost-effective manner in an autonomic fashion. We develop a framework that optimizes batching parameters on the fly using a lightweight profiler and an analytical model. We also devise strategies for serving ML workloads of varying sizes, leading to non-deterministic service time in a cost-effective manner. More specifically, we develop an SLO-aware framework that first analyzes the request size variations and workload variation to estimate the number of serving functions and intelligently route requests to multiple serving functions. Finally, resource utilization of burstable instances is optimized to benefit the cloud provider and end-user through a careful orchestration of resources (i.e., CPU, network, and I/O) using an analytical model and lightweight profiling, while complying with a user-defined SLO

Developing a sustainable Air Quality Monitoring Methodology Through the Use of Low-Cost Sensors.

The World Health Organization reports that outdoor air pollution is responsible for the deaths of over seven million people worldwide. Particulate matter has been recognized as a detrimental pollutant in the atmosphere. Traditional PM monitoring methods, limited by their urban/regional focus, high costs, and size, fail to capture the detailed spatial and temporal variations in PM concentrations, particularly in underrepresented urban and remote areas, thus they are unsustainable. Consequently, these constraints bring about the need for more sustainable methods. Low-cost air quality sensors offer a sustainable way to monitor particle matter and reduce atmospheric pollution due to their low cost, high spatial resolution, and accuracy. Employing low-cost sensors will allow us to improve the nature and precision of air quality observation, improving measures to decrease carbon emissions, which is good for people\u27s health and the environment. In this study, we employ the Triple Bottom Line sustainable model to investigate the environmental, economic, and social pillars of sustainability. We will conduct a stakeholder analysis to evaluate the engagement of various stakeholders in the development and implementation of air quality monitoring solutions. This approach is aligned with United Nations Sustainable Development Goals: good health and well-being, sustainable cities and communities, and climate action. This effort seeks to improve atmospheric monitoring aiming for a healthier, sustainable future

Application of Silicon Carbide in Abrasive Water Jet Machining

Silicon carbide (SiC) is a compound consisting of silicon and carbon. It is also known as carborundum. SiC is used as an abrasive material after it was mass produced in 1893. The credit of mass production of SiC goes to Edward Goodrich Acheson. Now SiC is used not only as an abrasive, but it is also extensively used in making cutting tools, structural material, automotive parts, electrical systems, nuclear fuel parts, jewelries, etc. AWJM is a well-established non-traditional machining technique used for cutting difficult-to machine materials. Nowadays, this process is being widely used for machining of hard materials like ceramics, ceramic composites, fiber-reinforced composites and titanium alloys where conventional machining fails to machine economically. The fact is that in AWJM no heat is developed and it has important implications where heat-affected zones are to be avoided. AWJM can cut everything what traditional machining can cut, as well as what traditional machining cannot cut such as too hard material (e.g. carbides), too soft material (e.g. rubber) and brittle material (e.g. glass, ceramics, etc.). The basic cutting tool used in water jet machining is highly pressurized water that is passed through a very small orifice, producing a very powerful tool that can cut almost any material. Depending on the materials, thickness of cut can range up to 25 mm and higher (Kalpakjian & Schmid, 2010). A water jet system consists of three components which are the water preparation system, pressure generation system and the cutting head and motion system

Feature-based tracking of multiple people for intelligent video surveillance.

Intelligent video surveillance is the process of performing surveillance task automatically by a computer vision system. It involves detecting and tracking people in the video sequence and understanding their behavior. This thesis addresses the problem of detecting and tracking multiple moving people with unknown background. We have proposed a feature-based framework for tracking, which requires feature extraction and feature matching. We have considered color, size, blob bounding box and motion information as features of people. In our feature-based tracking system, we have proposed to use Pearson correlation coefficient for matching feature-vector with temporal templates. The occlusion problem has been solved by histogram backprojection. Our tracking system is fast and free from assumptions about human structure. We have implemented our tracking system using Visual C++ and OpenCV and tested on real-world images and videos. Experimental results suggest that our tracking system achieved good accuracy and can process videos in 10-15 fps.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .A42. Source: Masters Abstracts International, Volume: 45-01, page: 0347. Thesis (M.Sc.)--University of Windsor (Canada), 2006