Deriving Generalized Temperature-Dependent Material Models for Masonry Through Fire Tests and Machine Learning

Masonry is one of the oldest and commonly used building materials in the construction industry. Among a variety of benefits, masonry provides low-cost construction, fire, and weather protection as well as thermal and sound insulation. In addition, masonry has superior material properties at elevated temperatures which is reflected by its slow degradation of its mechanical and thermal properties. Literature shows that we do not have a uniform material model that describes the mechanical degradation of masonry under fire conditions. As such, this limits the use of masonry in fire-based performance design of masonry structures. To bridge this knowledge gap, this thesis reviews regionally adopted fire testing methods on masonry and then presents findings from a fire experimental program aimed to explore the influence of elevated temperatures on the mechanical performance of concrete masonry units (CMUs). Our tests include heating and post heating evaluation of the compressive strength of CMUs exposed to realistic fire conditions. Then, this thesis delivers a methodology to derive generalized temperature-dependent material models for CMUs using statistical and Bayesian methods, as well as machine learning (by means of artificial neural networks). Finally, this work articulates limitations and research needs to be tackled in the near future

BIG DATA ANALYTICS SOLUTION FOR SMALL CELLS DEPLOYMENT USING MACHINE LEARNING TECHNIQUES

This thesis presents a “Novel Small Cell Planning Solution using Machine learning”. The Telecom service providers are interested in estimating various trends in order to plan future upgrades and deployments driven by real data. Fundamentally, the service provider landscape is changing. The numbers of devices are increasing in the network such as small cells to cater the growing demands. Also, the increasing amount of data has caused a big data revolution that is having an impact on telecom. With the advance big data analytics solutions and with fine grained analytics in real time, needs in bandwidth change from one place to another throughout the day, week, month, etc, becomes predictable. Hence, big data analytics solutions can help in deciding footprint of small cells and efficiently deployment of small cells. In this thesis, I have used the open big data that is published at the site: https://dandelion.eu/datamine/open-big-data/ under Open Data Commons Open Database License (ODbL) license. This dataset provides information about the telecommunication activities over the city of Milano. The dataset is the result of a computation over the Call Detail Records (CDRs) generated by the Telecom Italia cellular network over the city of Milano. Data mining is the technique to find concealed and fascinating pattern from dataset, which can be used in decision making and future prediction. In this thesis, data preprocessing has been performed on hadoop framework using hive with Cloudera's open source platform, CDH cloudera-quickstart-vm-5.3.0-0-vmware. In this thesis, the (Eps, MinPts) DBSCAN density based spatial clustering algorithm is used clustering the geospatial data. DBSCAN clusters a spatial data set based on two parameters namely physical distance from each point and a minimum cluster size. This method is best fit for spatial latitude-longitude data. In this thesis, the scikit-leran machine learning platform is used to implement the solution, scikit-learn in python is one of the widely used machine learning platform, it provides a wide range of supervised and unsupervised learning algorithms via a consistent interface in Python. For the validation of the clustering results, the data mining tool WEKA 3.6.11 is used. For benchmarking of the proposed solution, the DBSCAN algorithms clustering result is compared with the WEKA cluster’s results. The final results show that the solution produces very promising results. The three promising results are , it is able to reveal all the objects from the datasets on the basis of user defined algorithm input parameters. The input parameters have a decisive impact on the cluster result. It can extract spatial, temporal and semantically separated clusters. The detected clusters are visualized using Matplotlib plotting library for the Python, WEKA and geojson.io online tool

A Review on Separable Reversible Data Hiding in Encrypted Image

This work proposes a new system for separable reversible data hiding in color images encrypted with the approach of adding the matrix. In the proposed scheme, Take color image input and provide the encryption key to encrypt the picture image and then compressed encrypted with various algorithms. Due to the compression of the image to create a sparse space to accommodate the additional data, integrated data using the key data hiding. If the receiver has the encryption key, you can decrypt the received data to obtain the image only. If the receiver has the data hiding key, it can extract data only. If the receiver is both the key to hidden data and the encryption key, it can extract the additional data and recover the original content without any errors using the spatial correlation in the natural image if the amount of data extra is great

A Study on Concepts De-Laval Nozzle using CFD Tool

A device that is used to control the characteristics of fluid is known as a nozzle. Its primary function is to increase the fluid\u27s rate of motion. A traditional De-Laval nozzle will have three distinct parts: a throat, a converging section, and a diverging part. This paper will attempt to detail the bulk of the nozzle concepts developed by De Laval. This paper provides a comprehensive analysis of the nozzle\u27s operational philosophy. In addition, theoretical flow analysis is carried out at a number of locations along the length of the nozzle. The dynamic changes in flow parameters such as pressure, temperature, velocity, and density may be seen with the use of computational fluid dynamics (CFD). The use of CFD is also employed in the simulation of shockwaves

Stress Detection by Measuring Heart Rate Variability

In today’s world one of the major leading factor to health problem is STRESS. The detection and the solution is mainly dependent on the experience of the clinician is in detecting the factors of stress. The disadvantage of this method is that the clinician’s detection may be wrong at some stage, due to the unawareness of new problems. The basic parameter on which stress can be identified are Galvanic Skin Response(GSR), Heart Rate(HR), Body Temperature, Blood Pressure(BP) which provides detailed information of the state of mind of a person .These parameter’s vary from person to person on the basis of certain things such as their body condition, age, gender and experience. In our project ,we have focused on one such parameter i.e. heart rate variability(HRV) as major technique for detecting stress. HRV serves as a substitute for “vertical integration”. This “Vertical integration” of the brain mechanism guides flexible control over behavior with peripheral physiology and thus it provides beneficial information to understand the problems related to stress and health. In order to avoid clincian’s mistake in detecting stress level, we have introduced a new hardware device which easily calculates the accurate pulse rate of a person and gives appropriate solution to the stress level. DOI: 10.17762/ijritcc2321-8169.16047

Nanocoolants for engine cooling system

The automobile industry is constantly looking for increasing engine efficiency while complying with stringent emission norms. One such aspect studied in great detail is the effect of engine coolant temperature on fuel efficiency and emissions. It has been shown that coolant is responsible for maintaining the engine at optimum operating temperatures in addition to warming up the engine at start. In view of this, nanofluids have been proposed as potential replacement for conventional coolants based on their extra-ordinary lab-scale performance, however studies reported in literature are inadequate to predict the effect of nanofluids in automobiles. We have developed a process for large scale production of stable nanofluids using high energy milling. Using this top down approach, we have converted commercial engine coolants into nanocoolants. This study presents a comparison between commercial coolants and nanocoolants with respect to break specific fuel consumption (bsfc), log mean temperature difference (LMTD) of the heat exchanger (radiator) circuit, amount of NOx (ppm) and O2 (% vol.) in the exhaust gas. This study is performed on a three cylinder, direct injection, 38.5 bhp diesel engine test rig equipped with a hydraulic dynamometer. Addition of nanoparticles exhibits an enhancement of about 2-3% in LMTD, while brake specific fuel consumption and extent of oxygen in the exhaust gas decreases when nanocoolant is used.Papers presented to the 12th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, Costa de Sol, Spain on 11-13 July 2016

Generalized temperature-dependent material models for compressive strength of masonry using fire tests, statistical methods and artificial intelligence

Masonry has superior fire resistance properties stemming from its inert characteristics, and slow degradation of mechanical properties. However, once exposed to fire conditions, masonry undergoes a series of physio-chemical changes. Such changes are often described via temperature-dependent material models. Despite calls for standardization of such models, there is a lack in such standardized models. As a result, available temperature-dependent material models vary across various fire codes and standards. In order to bridge this knowledge gap, this paper presents three methodologies, namely, regression-based, probabilistic-based, and the use of artificial neural (ANN) networks, to derive generalized temperature-dependent material models for masonry with a case study on the compressive strength property. Findings from this paper can be adopted to establish updated temperature-dependent material models of fire design and analysis of masonry structures

Study and Survey of Social Networking and Facebook

Online social networking provides the user with the facilities love sharing, organizing and finding content and contacts. The utility and speedy development of those sites offers rise to review the characteristics and also the utilization of on-line social networks on giant scale. Understanding and analysis of social networking is incredibly vital to boost this system and to style new applications for on-line social networks. This text presents a user’s study and analysis of social networks love Facebook

Two Dimensional CFD Analysis on Different Rocket Nozzles

The reduction of Earth-to-orbit launch costs in conjunction with an increase in launcher reliability and operational Efficiency is the key demands on future space transportation systems, like single-stage-to-orbit vehicles (SSTO). The realization of these vehicles strongly depends on the performance of the engines, which should deliver high performance with low system complexity. Performance data for rocket engines are practically always lower than the theoretically attainable values because of imperfections in the mixing, combustion, and expansion of the propellants. The main part of the project addresses different nozzle concepts with improvements in performance as compared to conventional nozzles achieved by Different Mach numbers, thus, by minimizing losses caused by over- or under expansion. The design of different nozzle shapes and flow simulation is done in gambit and fluent software’s respectively for various parameter

EMOTION BASED CLASSIFICATION OF NATURAL SCENE IMAGES

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