Search results for: strength prediction

Authors: Harkaranjit Singh, Arun Kumar

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Depletion of natural resources is a common phenomenon in developing countries like India due to rapid urbanization and industrialization involving construction of infrastructure and other amenities. In view of this, people have started searching for suitable other viable alternative materials for concrete so that the existing natural resources could be preserved to the possible extent for the future generation. In this process, different industrial waste materials such as fly ash, blast furnace slag, quarry dust, tile waste, bricks, broken glass waste, waste aggregate from demolition of structures, ceramic insulator waste, etc. have been tried as a viable substitute material to the conventional materials in concrete and has also been succeeded. This paper describes the studies conducted on strength characteristics of concrete made with utilizing of crushed tiles as a coarse aggregate. The waste crushed tiles can be used as coarse aggregates with the replacement ratio of 0, 50, 75 and 100% were used. Mechanical and physical tests were conducted on specimens. It was found that, the concrete made of waste ceramic tile aggregate produced more strength in compression, and flexure.

Keywords: compressive strength, flexural strength, waste crushed tile, concrete

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Authors: Yuwaratu Syafira, Wahyu K. Y. Putra, Kusharisupeni Djokosujono

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Background/Objective: Body Mass Index (BMI) serves various purposes, including measuring the prevalence of obesity in a population, and also in formulating a patient’s diet at a hospital, and can be calculated with the equation = body weight (kg)/body height (m)². However, the BMI of an individual with difficulties in carrying their weight or standing up straight can not necessarily be measured. The aim of this study was to form a prediction model for the BMI of young adult students of Public Health Faculty of University of Indonesia. Subject/Method: This study used a cross sectional design, with a total sample of 132 respondents, consisted of 58 males and 74 females aged 21- 30. The dependent variable of this study was BMI, and the independent variables consisted of sex and anthropometric measurements, which included ulna length, arm length, tibia length, knee height, mid-upper arm circumference, and calf circumference. Anthropometric information was measured and recorded in a single sitting. Simple and multiple linear regression analysis were used to create the prediction equation for BMI. Results: The male respondents had an average BMI of 24.63 kg/m² and the female respondents had an average of 22.52 kg/m². A total of 17 variables were analysed for its correlation with BMI. Bivariate analysis showed the variable with the strongest correlation with BMI was Mid-Upper Arm Circumference/√Ulna Length (MUAC/√UL) (r = 0.926 for males and r = 0.886 for females). Furthermore, MUAC alone also has a very strong correlation with BMI (r = 0,913 for males and r = 0,877 for females). Prediction models formed from either MUAC/√UL or MUAC alone both produce highly accurate predictions of BMI. However, measuring MUAC/√UL is considered inconvenient, which may cause difficulties when applied on the field. Conclusion: The prediction model considered most ideal to estimate BMI is: Male BMI (kg/m²) = 1.109(MUAC (cm)) – 9.202 and Female BMI (kg/m²) = 0.236 + 0.825(MUAC (cm)), based on its high accuracy levels and the convenience of measuring MUAC on the field.

Keywords: body mass index, mid-upper arm circumference, prediction model, ulna length

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Authors: Ejaz Ahmed, Huang Yong

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The operation of aero engines has a critical importance in the vicinity of lean blowout (LBO) limits. Lefebvre’s model of LBO based on empirical correlation has been extended to flame volume concept by the authors. The flame volume takes into account the effects of geometric configuration, the complex spatial interaction of mixing, turbulence, heat transfer and combustion processes inside the gas turbine combustion chamber. For these reasons, flame volume based LBO predictions are more accurate. Although LBO prediction accuracy has improved, it poses a challenge associated with Vf estimation in real gas turbine combustors. This work extends the approach of flame volume prediction previously based on fuel iterative approximation with cold flow simulations to reactive flow simulations. Flame volume for 11 combustor configurations has been simulated and validated against experimental data. To make prediction methodology robust as required in the preliminary design stage, reactive flow simulations were carried out with the combination of probability density function (PDF) and discrete phase model (DPM) in FLUENT 15.0. The criterion for flame identification was defined. Two important parameters i.e. critical injection diameter (Dp,crit) and critical temperature (Tcrit) were identified, and their influence on reactive flow simulation was studied for Vf estimation. Obtained results exhibit ±15% error in Vf estimation with experimental data.

Keywords: CFD, combustion, gas turbine combustor, lean blowout

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Authors: Balwinder Singh, Veerpaul Kaur Mann

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A composite material is formed by the combination of two or more phases or materials. Natural minerals-derived Basalt fiber is a kind of fiber being introduced in the polymer composite industry due to its good mechanical properties similar to synthetic fibers and low cost, environment friendly. Also, there is a rising trend towards the use of industrial wastes as fillers in polymer composites with the aim of improving the properties of the composites. The mechanical properties of the fiber-reinforced polymer composites are influenced by various factors like fiber length, fiber weight %, filler weight %, filler size, etc. Thus, a detailed study has been done on the characterization of short-chopped Basalt fiber-reinforced polymer matrix composites using fly ash as filler. Taguchi’s L9 orthogonal array has been used to develop the composites by considering fiber length (6, 9 and 12 mm), fiber weight % (25, 30 and 35 %) and filler weight % (0, 5 and 10%) as input parameters with their respective levels and a thorough analysis on the mechanical characteristics (tensile strength and impact strength) has been done using ANOVA analysis with the help of MINITAB14 software. The investigation revealed that fiber weight is the most significant parameter affecting tensile strength, followed by fiber length and fiber weight %, respectively, while impact characterization showed that fiber length is the most significant factor, followed by fly ash weight, respectively. Introduction of fly ash proved to be beneficial in both the characterization with enhanced values upto 5% fly ash weight. The present study on the natural fibres reinforced epoxy composites using fly ash as filler material to study the effect of input parameters on the tensile strength in order to maximize tensile strength of the composites. Fabrication of composites based on Taguchi L9 orthogonal array design of experiments by using three factors fibre type, fibre weight % and fly ash % with three levels of each factor. The Optimization of composition of natural fibre reinforces composites using ANOVA for obtaining maximum tensile strength on fabricated composites revealed that the natural fibres along with fly ash can be successfully used with epoxy resin to prepare polymer matrix composites with good mechanical properties. Paddy- Paddy fibre gives high elasticity to the fibre composite due to presence of approximately hexagonal structure of cellulose present in paddy fibre. Coir- Coir fibre gives less tensile strength than paddy fibre as Coir fibre is brittle in nature when it pulls breakage occurs showing less tensile strength. Banana- Banana fibre has the least tensile strength in comparison to the paddy & coir fibre due to less cellulose content. Higher fibre weight leads to reduction in tensile strength due to increased nuclei of air pockets. Increasing fly ash content reduces tensile strength due to nonbonding of fly ash particles with natural fibre. Fly ash is also not very strong as compared to the epoxy resin leading to reduction in tensile strength.

Keywords: tensile strength and epoxy resin. basalt Fiber, taguchi, polymer matrix, natural fiber

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Authors: Aasheesh Raturi, Vimal Kothiyal, P. D. Semalty

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We studied mechanical properties of Eucalyptus tereticornis using FT-NIR spectroscopy. Firstly, spectra were pre-processed to eliminate useless information. Then, prediction model was constructed by partial least squares regression. To study the influence of pre-processing on prediction of mechanical properties for NIR analysis of wood samples, we applied various pretreatment methods like straight line subtraction, constant offset elimination, vector-normalization, min-max normalization, multiple scattering. Correction, first derivative, second derivatives and their combination with other treatment such as First derivative + straight line subtraction, First derivative+ vector normalization and First derivative+ multiplicative scattering correction. The data processing methods in combination of preprocessing with different NIR regions, RMSECV, RMSEP and optimum factors/rank were obtained by optimization process of model development. More than 350 combinations were obtained during optimization process. More than one pre-processing method gave good calibration/cross-validation and prediction/test models, but only the best calibration/cross-validation and prediction/test models are reported here. The results show that one can safely use NIR region between 4000 to 7500 cm-1 with straight line subtraction, constant offset elimination, first derivative and second derivative preprocessing method which were found to be most appropriate for models development.

Keywords: FT-NIR, mechanical properties, pre-processing, PLS

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Authors: Tomas Vampola, Michael Valášek

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On the basis of simulation-generated failure states of structural elements of a turboprop engine suitable for the busy-jet class of aircraft, an algorithm for early prediction of damage or reduction in functionality of structural elements of the engine is designed and verified with real data obtained at dynamometric testing facilities of aircraft engines. Based on an expanding database of experimentally determined data from temperature and pressure sensors during the operation of turboprop engines, this strategy is constantly modified with the aim of using the minimum number of sensors to detect an inadmissible or deteriorated operating mode of specific structural elements of an aircraft engine. The assembled algorithm for the early prediction of reduced functionality of the aircraft engine significantly contributes to the safety of air traffic and to a large extent, contributes to the economy of operation with positive effects on the reduction of the energy demand of operation and the elimination of adverse effects on the environment.

Keywords: detectability of malfunction, dynamometric testing, prediction of damage, turboprop engine

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Authors: Kefaya Qaddoum

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Most of greenhouse growers desire a determined amount of yields in order to accurately meet market requirements. The purpose of this paper is to model a simple but often satisfactory supervised classification method. The original naive Bayes have a serious weakness, which is producing redundant predictors. In this paper, utilized regularization technique was used to obtain a computationally efficient classifier based on naive Bayes. The suggested construction, utilized L1-penalty, is capable of clearing redundant predictors, where a modification of the LARS algorithm is devised to solve this problem, making this method applicable to a wide range of data. In the experimental section, a study conducted to examine the effect of redundant and irrelevant predictors, and test the method on WSG data set for tomato yields, where there are many more predictors than data, and the urge need to predict weekly yield is the goal of this approach. Finally, the modified approach is compared with several naive Bayes variants and other classification algorithms (SVM and kNN), and is shown to be fairly good.

Keywords: tomato yield prediction, naive Bayes, redundancy, WSG

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Authors: Riznaldi Akbar

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In this study, we compared the performance of the Artificial Neural Network (ANN) model with back-propagation algorithm in correctly predicting in-sample and out-of-sample external debt crisis in Indonesia. We found that exchange rate, foreign reserves, and exports are the major determinants to experiencing external debt crisis. The ANN in-sample performance provides relatively superior results. The ANN model is able to classify correctly crisis of 89.12 per cent with reasonably low false alarms of 7.01 per cent. In out-of-sample, the prediction performance fairly deteriorates compared to their in-sample performances. It could be explained as the ANN model tends to over-fit the data in the in-sample, but it could not fit the out-of-sample very well. The 10-fold cross-validation has been used to improve the out-of-sample prediction accuracy. The results also offer policy implications. The out-of-sample performance could be very sensitive to the size of the samples, as it could yield a higher total misclassification error and lower prediction accuracy. The ANN model could be used to identify past crisis episodes with some accuracy, but predicting crisis outside the estimation sample is much more challenging because of the presence of uncertainty.

Keywords: debt crisis, external debt, artificial neural network, ANN

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Authors: Hlaing Zaw Oo, N. Kostromina, V. Osipchik, T. Kravchenko, K. Yakovleva

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The process of modification of ed-20 epoxy resin synthesized by vinyl-containing compounds is considered. It is shown that the introduction of vinyl-containing compounds into the composition based on epoxy resin ED-20 allows adjusting the technological and operational characteristics of the binder. For improvement of the properties of epoxy resin, following modifiers were selected: polyvinylformalethyl, polyvinyl butyral and composition of linear and aromatic amines (Аramine) as a hardener. Now the big range of hardeners of epoxy resins exists that allows varying technological properties of compositions, and also thermophysical and strength indicators. The nature of the aramin type hardener has a significant impact on the spatial parameters of the mesh, glass transition temperature, and strength characteristics. Epoxy composite materials based on ED-20 modified with polyvinyl butyral were obtained and investigated. It is shown that the composition of resins based on derivatives of polyvinyl butyral and ED-20 allows obtaining composite materials with a higher complex of deformation-strength, adhesion and thermal properties, better water resistance, frost resistance, chemical resistance, and impact strength. The magnitude of the effect depends on the chemical structure, temperature and curing time. In the area of concentrations, where the effect of composite synergy is appearing, the values of strength and stiffness significantly exceed the similar parameters of the individual components of the mixture. The polymer-polymer compositions form their class of materials with diverse specific properties that ensure their competitive application. Coatings with high performance under cyclic loading have been obtained based on epoxy oligomers modified with vinyl-containing compounds.

Keywords: epoxy resins, modification, vinyl-containing compounds, deformation, strength properties

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Authors: D. M. Bassuney, W. A. Ibrahim, Medhat A. E. Moustafa

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As awareness of the variable nature of food industrial wastewater and its environmental impact grows, a more stable treatment reactor is needed to treat such wastewater. In this paper, a performance of 5-compartment lab-scale Anaerobic Baffled Reactor (ABR) treating high strength wastewater with high pH variation was studied under three organic loading rates (OLRs). The reactor showed high COD removal efficiencies: 92.67, 97.44, and 98.19% corresponding to OLRs of 2.0, 3.0, and 4.8 KgCOD/m3 d, respectively. The first compartment showed a good buffering capacity and a distinct phase separation occurred in the ABR.

Keywords: anaerobic baffled reactor, food industrial wastewater, high strength wastewater, organic loading, pH

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Authors: Phawichsak Prapassornpitaya, Wanida Jinsart

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Daily monitoring PM₁₀ and PM₂.₅ data from 2007 to 2017 were analyzed to provide baseline data for prediction of the air pollution in Bangkok in the period of 2018 -2020. Two statistical models, Autoregressive Integrated Moving Average model (ARIMA) were used to evaluate the trends of pollutions. The prediction concentrations were tested by root means square error (RMSE) and index of agreement (IOA). This evaluation of the traffic PM₂.₅ and PM₁₀ were studied in association with the regulatory control and emission standard changes. The emission factors of particulate matter from diesel vehicles were decreased when applied higher number of euro standard. The trends of ambient air pollutions were expected to decrease. However, the Bangkok smog episode in February 2018 with temperature inversion caused high concentration of PM₂.₅ in the air environment of Bangkok. The impact of traffic pollutants was depended upon the emission sources, temperature variations, and metrological conditions.

Keywords: fine particulate matter, ARIMA, RMSE, Bangkok

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Authors: Rajkamal Kumar, Sudhir Mishra

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The connections between various elements of concrete structures play a vital role in determining the durability of structures. These connections produce discontinuities and to ensure the monolithic behavior of structures, these connections should be carefully designed. The connections between concrete layers may occur in various situations such as structure repairing and rehabilitation or construction of huge structures with cast-in-situ or pre-cast elements, etc. Bond strength at the interface of these concrete layers should be able to prevent the progressive slip from taking place and it should also ensure satisfactory performance of the structure. Different approaches to enhance the bond strength at interface have been a major area of research. Nowadays, micro-concrete is getting popular as a repair material. Under this ambit, this paper aims to present the experimental results of connections between concrete layers of different age with artificial indentation at interface with two types of repair material: Concrete with same parent concrete composition and ready-mix mortar (micro-concrete), artificial indentations (grooves and holes) were made on the old layer of concrete to increase the bond strength. Curing plays an important role in determining the bond strength. Optimum duration for curing have also been discussed for each type of repair material. Different types of failure patterns have also been mentioned.

Keywords: adhesion, cohesion, compressive stress, micro-concrete, shear stress, slant shear test

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Authors: K. Nirmalkumar

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An acute water scarcity is prevailing in the dry season in and around Perundurai (Erode district, Tamil Nadu, India) where there are more number of tannery units. Hence an attempt was made to use the effluent from the tannery industry for construction purpose. The mechanical properties such as compressive strength, tensile strength, flexural strength and the special properties such as chloride attack, sulphate attack and chemical attack were studied by casting various concrete specimens in form of cube, cylinders and beams, etc. It was observed that the concrete had some reduction in strength while subjected to chloride attack, sulphate attack and chemical attack. So admixtures were selected and optimized in suitable proportion to counter act the adverse effects and the results were found to be satisfactory. In this research study the corrosion results of specimens prepared by using treated and untreated tannery effluent were compared with the concrete specimens prepared by using potable water. It was observed that by the addition of admixtures, the adverse effects due to the usage of the treated and untreated tannery effluent are counteracted.

Keywords: corrosion, calcium nitrite, concrete, fly ash

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Authors: Ziad. Sh. Al Sarraf

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Ultrasonic metal welding has been the subject of ongoing research and development, most recently concentrating on metal joining in miniature devices, for example to allow solder-free wire bonding. As well as at the small scale, there are also opportunities to research the joining of thicker sheet metals and to widen the range of similar and dissimilar materials that can be successfully joined using this technology. This study presents the design, characterisation and test of a lateral-drive ultrasonic metal spot welding device. The ultrasonic metal spot welding horn is modelled using finite element analysis (FEA) and its vibration behaviour is characterised experimentally to ensure ultrasonic energy is delivered effectively to the weld coupon. The welding stack and fixtures are then designed and mounted on a test machine to allow a series of experiments to be conducted for various welding and ultrasonic parameters. Weld strength is subsequently analysed using tensile-shear tests. The results show how the weld strength is particularly sensitive to the combination of clamping force and ultrasonic vibration amplitude of the welding tip, but there are optimal combinations of these and also limits that must be clearly identified.

Keywords: ultrasonic welding, vibration amplitude, welding force, weld strength

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Authors: Rama Seshu Doguparti

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This paper presents the experimental investigation on the bond behavior of geo polymer concrete. The bond behavior of geo polymer concrete cubes of grade M35 reinforced with 16 mm TMT rod is analyzed. The results indicate that the bond performance of reinforced geo polymer concrete is good and thus proves its application for construction.

Keywords: geo-polymer, concrete, bond strength, behaviour

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Authors: Mustafa Gulsen, Mitat Koz

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The aim of this study is to investigate the effect of lumbar stabilisation and proprioceptive neuromuscular facilitation (PNF) training on muscle strength and muscle endurance. The participants were 64 between the ages of 15-69 (53.04 ± 14.59), who were graded protrusion and bulging lumbar herniation according to 'Macnab Classification'. The participants were divided into four groups as each group had 16 participants: lumbar stabilitation training, PNF training, physical therapy and control groups. Sociodemographic features were recorded. Then their muscle strength tests (by isokinetic dynamometer (Cybex 770 Norm Lumex Inc, Ronkonkoma, NY, USA) were recorded. Before and after applications; visual analogue scale (VAS), Oswestry Disability İndex were applied by a physical therapist. The participants in lumbar stabilisation group performed 45 minutes, 5 days in a week for 4 weeks strength training with a physical therapist observation. The participants in PNF group performed 5 days in a week for 4 weeks with pelvic patterns of PNF by a physiotherapist. The participants in physical therapy group underwent Hotpack, Tens and Ultrasound therapy 5 days in a week for 4 weeks. The participants in control group didn’t take any training programme. After 4 weeks, the evaluations were repeated. There were significant increases in muscle strength and muscle endurance in lumbar stabilization training group. Also in pain intensity at rest and during activity in this group and in Oswestry disability index of patients, there were significant improvements (p < 0.05). In PNF training group likewise, there were significant improvements in muscle strength, muscle endurance, pain intensity at rest and with activity and in Oswestry disability index (p < 0.05). But improvements in the Lumbar Stabilization group was better than PNF Group. We found significant differences only in pain intensity at rest and with activity and in Oswestry disability index (p < 0.05). in the patients in Physical Therapy group. We think that appropriate physiotherapy and rehabilitation program which will be prepared for patients, to protect the waist circumference of patients with low muscle strength and low muscle endurance will increase muscle strength and muscle endurance. And it is expected that will reduce pain and will provide advances toward correcting functional disability of the patients.

Keywords: disc herniation, endurance, lumbar stabilitation exercises, PNF, strength

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Authors: Suzan Anwar, Utkarsh Goyal

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Heart rate (HR) is a predictor of cardiovascular, cerebrovascular, and all-cause mortality in the general population, as well as in patients with cardio and cerebrovascular diseases. Machine learning (ML) significantly improves the accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment while avoiding unnecessary treatment of others. This research examines relationship between the individual's various heart health inputs like age, sex, cp, trestbps, thalach, oldpeaketc, and the likelihood of developing heart disease. Machine learning techniques like logistic regression and decision tree, and Python are used. The results of testing and evaluating the model using the Heart Failure Prediction Dataset show the chance of a person having a heart disease with variable accuracy. Logistic regression has yielded an accuracy of 80.48% without data handling. With data handling (normalization, standardscaler), the logistic regression resulted in improved accuracy of 87.80%, decision tree 100%, random forest 100%, and SVM 100%.

Keywords: heart rate, machine learning, SVM, decision tree, logistic regression, random forest

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Authors: Devinder Sharma, Sanjay Sharma, Ajay Goyal, Ashish Kapoor

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The present research paper discussed the durability properties of high strength concrete (HSC) using Foundry Slag(FD) as partial substitute for fine aggregates (FA) and Alccofine (AF) in addition to portland pozzolana (PPC) cement. Specimens of Concrete M100 grade with water/binder ratio 0.239, with Foundry Slag (FD) varying from 0 to 50% and with optimum quantity of AF(15%) were casted and tested for durability properties such as Water absorption, water permeability, resistance to sulphate attack, alkali attack and nitrate attack of HSC at the age of 7, 14, 28, 56 and 90 days. Substitution of fine aggregates (FA) with up to 45% of foundry slag(FD) content and cement with 15% substitution and addition of alccofine showed an excellent resistance against durability properties at all ages but showed a decrease in these properties with 50% of FD contents. Loss of weight in concrete samples due to sulphate attack, alkali attack and nitrate attack of HSC at the age of 365 days was compared with loss in compressive strength. Correlation between loss in weight and loss in compressive strength in all the tests was found to be excellent.

Keywords: alccofine, alkali attack, foundry slag, high strength concrete, nitrate attack, water absorption, water permeability

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Authors: Sagir M. Yusuf, Chris Baber

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In this paper, we describe how Bayesian inferential reasoning will contributes in obtaining a well-satisfied prediction for Distributed Constraint Optimization Problems (DCOPs) with uncertainties. We also demonstrate how DCOPs could be merged to multi-agent knowledge understand and prediction (i.e. Situation Awareness). The DCOPs functions were merged with Bayesian Belief Network (BBN) in the form of situation, awareness, and utility nodes. We describe how the uncertainties can be represented to the BBN and make an effective prediction using the expectation-maximization algorithm or conjugate gradient descent algorithm. The idea of variable prediction using Bayesian inference may reduce the number of variables in agents’ sampling domain and also allow missing variables estimations. Experiment results proved that the BBN perform compelling predictions with samples containing uncertainties than the perfect samples. That is, Bayesian inference can help in handling uncertainties and dynamism of DCOPs, which is the current issue in the DCOPs community. We show how Bayesian inference could be formalized with Distributed Situation Awareness (DSA) using uncertain and missing agents’ data. The whole framework was tested on multi-UAV mission for forest fire searching. Future work focuses on augmenting existing architecture to deal with dynamic DCOPs algorithms and multi-agent information merging.

Keywords: DCOP, multi-agent reasoning, Bayesian reasoning, swarm intelligence

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Authors: B. S. Al-Tulaian, M. J. Al-Shannag, A. M. Al-Hozaimy

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Manufacturing of fibers from industrial or postconsumer plastic waste is an attractive approach with such benefits as concrete performance enhancement, and reduced needs for land filling. The main objective of this study is to investigate the effect of Plastic fibers obtained locally from recycled waste on plastic shrinkage cracking of concrete. The results indicate that recycled plastic RP fiber of 50 mm length is capable of controlling plastic shrinkage cracking of concrete to some extent, but are not as effective as polypropylene PP fibers when added at the same volume fraction. Furthermore, test results indicated that there was The increase in flexural strength of RP fibers and PP fibers concrete were 12.34% and 40.30%, respectively in comparison to plain concrete. RP fiber showed a substantial increase in toughness and a slight decrease in flexural strength of concrete at a fiber volume fraction of 1.00% compared to PP fibers at fiber volume fraction of 0.50%. RP fibers caused a significant increase in compressive strengths up to 13.02% compared to concrete without fiber reinforcement.

Keywords: concrete, plastic, shrinkage cracking, compressive strength, flexural strength, toughness, RF recycled fibers, polypropylene PP fibers

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Authors: Mikhail Gritskevich, Sebastian Hohenstein

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The paper presents the results of a detailed assessment of several modern Reynolds Averaged Navier-Stokes (RANS) turbulence models for prediction of C3X vane film cooling at various injection regimes. Three models are considered, namely the Shear Stress Transport (SST) model, the modification of the SST model accounting for the streamlines curvature (SST-CC), and the Explicit Algebraic Reynolds Stress Model (EARSM). It is shown that all the considered models face with a problem in prediction of the adiabatic effectiveness in the vicinity of the cooling holes; however, accounting for the Reynolds stress anisotropy within the EARSM model noticeably increases the solution accuracy. On the other hand, further downstream all the models provide a reasonable agreement with the experimental data for the adiabatic effectiveness and among the considered models the most accurate results are obtained with the use EARMS.

Keywords: discrete holes film cooling, Reynolds Averaged Navier-Stokes (RANS), Reynolds stress tensor anisotropy, turbulent heat transfer

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Authors: Pooja Rani, G. S. Mahapatra, S. K. Pandey

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In this paper, we propose a novel approach of Neural Network and Particle Swarm Optimization methods for software reliability prediction. We first explain how to apply compound function in neural network so that we can derive a Flexible Logistic (S-shaped) Growth Curve (FLGC) model. This model mathematically represents software failure as a random process and can be used to evaluate software development status during testing. To avoid trapping in local minima, we have applied Particle Swarm Optimization method to train proposed model using failure test data sets. We drive our proposed model using computational based intelligence modeling. Thus, proposed model becomes Neuro-Particle Swarm Optimization (NPSO) model. We do test result with different inertia weight to update particle and update velocity. We obtain result based on best inertia weight compare along with Personal based oriented PSO (pPSO) help to choose local best in network neighborhood. The applicability of proposed model is demonstrated through real time test data failure set. The results obtained from experiments show that the proposed model has a fairly accurate prediction capability in software reliability.

Keywords: software reliability, flexible logistic growth curve model, software cumulative failure prediction, neural network, particle swarm optimization

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Authors: John Trihatmoko, Luky Handoko

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A series of experimental program was undertaken to study the effect of curing temperature on the unconfined compression strength of bagasse ash (BA) - calcium carbide residue (CCR) stabilized organic clay (OC). A preliminary experiment was performed to get the physical properties of OC, and to get the optimum water content (OMC), the standard compaction test was done. The stabilizing agents used in this research was (40% BA + 60% CCR) . Then to obtain the best binder proportion, unconfined compression test was undertaken for OC + 3, 6, 9, 12 and 15% of binder with 7, 14, 21, 28 and 56 days curing period. The best quantity of the binder was found on 9%. Finally, to study the effect of curing temperature, the unconfined compression test was performed on OC + 9% binder with 7, 14, 21, 28 and 56 days curing time with 20O, 25O, 30O, 40O, and 50O C curing temperature. The result indicates that unconfined compression strength (UCS) of treated OC improve according to the increase of curing temperature at the same curing time. The improvement of UCS is probably due to the degree of cementation and pozzolanic reactions.

Keywords: curing temperature, organic clay, bagasse ash, calcium carbide residue, unconfined compression strength

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Authors: Oualid Benyamina Douma

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Repair is always the result of the appearance of apparent disorder or aggravation of a mass. Which had hitherto been considered minor if not negligible: The work was not done according to plan. So; the examination of causes can lead to thinking about repair. While the application of the epoxy resin has become a hot topic. In this context, we conducted an experimental campaign (48 specimens are tested beakout) whose objective is based on three points: 1- Highlight the importance and influence of important parameters (compressive strength of concrete anchorage length and diameter of the steel bar) on routes (steel-concrete and steel–concrete epoxy resin) 2- Understanding the influence of the parameters mentioned above on the relationship that may exist between the peel strength and slippage. 3- Faces of cracks and failure modes. This study shows that passage of a compressive strength of 40 MPa to 62 MPa increases the adhesion between the steel bar and concrete and for specimens with or without epoxy resin. The loading force was increased form 40 to 81 kM kN, a rate if increase in loading over 100% In addition, for specimens with and without epoxy resin. increased breakout force through a specimen without a specimen with resin ranging from 20% to 32%.

Keywords: epoxy resin, peel strength, anchors, slip diameter steel rod, anchor plain concrete and concrete with moderate resistance

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Authors: A. Hakimiyan, H. Namazi

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Human behavior is defined as a range of behaviors exhibited by humans who are influenced by different internal or external sources. Human behavior is the subject of much research in different areas of psychology and neuroscience. Despite some advances in studies related to forecasting of human behavior, there are not many researches which consider the effect of the time delay between the presence of stimulus and the related human response. Analysis of EEG signal as a fractal time series is one of the major tools for studying the human behavior. In the other words, the human brain activity is reflected in his EEG signal. Artificial Neural Network has been proved useful in forecasting of different systems’ behavior especially in engineering areas. In this research, a time delay neural network is trained and tested in order to forecast the human EEG signal and subsequently human behavior. This neural network, by introducing a time delay, takes care of the lagging time between the occurrence of the stimulus and the rise of the subsequent action potential. The results of this study are useful not only for the fundamental understanding of human behavior forecasting, but shall be very useful in different areas of brain research such as seizure prediction.

Keywords: human behavior, EEG signal, time delay neural network, prediction, lagging time

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Authors: Mostafa Amirjan

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In the present study, the properties of Al-Al2O3 nanocomposite hollow sphere structures were investigated. For this reason, the Al-based nanocomposite hollow spheres with different amounts of nano alumina reinforcement (0-10wt %) and different ratio of thickness to diameter (t/D: 0.06-0.3) were prepared via a powder metallurgy method. Then, the effect of mentioned parameters was studied on physical and quasi static mechanical properties of their related prepared structures (open/closed cell) such as density, hardness, strength and energy absorption. It was found that as the t/D ratio increases the relative density, compressive strength and energy absorption increase. The highest values of strength and energy absorption were obtained from the specimen with 5 wt. % of nanoparticle reinforcement, t/D of 0.3 (t=1 mm, D=400µm) as 22.88 MPa and 13.24 MJ/m3, respectively. The moderate specific strength of prepared composites in the present study showed the good consistency with the properties of others low carbon steel composite with similar structure.

Keywords: hollow sphere structure foam, nanocomposite, thickness and diameter (t/D ), powder metallurgy

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Authors: Kunal Krishna Das, Eddie S. S. Lam

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Preplaced Aggregate Concrete (PAC) is produced by first placing the coarse aggregate into the formwork, followed by injection of grout to fill in the voids in between the coarse aggregates. In this study, tests were carried out to determine the effects of supplementary cementitious materials on the properties of PAC. Cement was partially replaced by ground granulated blast furnace slag (GGBS) and silica fume (SF) at different proportions. Grout properties were determined by the flow cone test and compressive strength test. Grout proportion was optimized statistically. It was applied to form PAC. Hardened properties of PAC, comprising compressive strength, splitting tensile strength, chloride-ion penetration and drying shrinkage, were evaluated. GGBS enhanced the flowability of the grout, whereas SF enhanced the strength of PAC. Both GGBS and SF improved the resistance to chloride-ion penetration with the drawback of increased drying shrinkage. Nevertheless, drying shrinkage was within the range to be classified as low shrinkage concrete.

Keywords: factorial design, ground granulated blast furnace slag, preplaced aggregate concrete, silica fume

Procedia PDF Downloads 119

Authors: G. Hemath Kumar, H. Mohit, K. Karthick

Abstract:

One of the most important issues in the composite technology is the repairing of parts of aircraft structures which is manufactured from composite materials. In such applications and also for joining various composite parts together, they are fastened together either using adhesives or mechanical fasteners. The tensile strength of these joints was carried out using Universal Testing Machine (UTM). A parametric study was also conducted to compare the performance of the hybrid joint with varying adherent thickness, adhesive thickness and overlap length. The composition of the material is combination of epoxy resin and carbon fibre under the method of reinforcement. To utilize the full potential of composite materials as structural elements, the strength and stress distribution of these joints must be understood. The study of tensile strength in the members involved under various design conditions and various joints were took place.

Keywords: carbon fiber, FRP composite, MMC, automotive

Procedia PDF Downloads 390

Authors: Wirat Sonchan, Pratoom Moungmee, Anek Sootmongkol

Abstract:

This study aimed to examine the effects of a circuit training program on muscle strength, agility, anaerobic performance and cardiovascular endurance. The study involved 24 freshmen (age 18.87+0.68 yr.) male students of the Faculty of Sport Science, Burapha University. They sample study were randomly divided into two groups: Circuit Training group (CT; n=12) and a Control group (C; n=12). Baseline data on height, weight, muscle strength (hand grip dynamometer and leg strength dynamometer), agility (agility T-Test), and anaerobic performance (Running-based Anaerobic Sprint Test) and cardiovascular endurance (20 m Endurance Shuttle Run Test) were collected. The circuit training program included one circuit of eight stations of 30/60 seconds of work/rest interval with two cycles in Week 1-4, and 60/90 seconds of work/rest interval with three cycles in Week 5-8, performed three times per week. Data were analyzed using paired t-tests and independent sample t-test. Statistically significance level was set at 0.05. The results show that after 8 weeks of a training program, muscle strength, agility, anaerobic capacity and cardiovascular endurance increased significantly in the CT Group (p < 0.05), while significant increase was not observed in the C Group (p < 0.05). The results of this study suggest that the circuit training program improved muscle strength, agility, anaerobic capacity and cardiovascular endurance of the study subjects. This program may be used as a guideline for selecting a set of exercise to improve physical fitness.

Keywords: circuit training, physical fitness, cardiovascular endurance, anaerobic performance

Procedia PDF Downloads 483

Authors: Heramb Somthankar, Anujoy Chakraborty

Abstract:

This paper presents a classification study on yield prediction in semiconductor production using machine learning approaches. A complicated semiconductor production process is generally monitored continuously by signals acquired from sensors and measurement sites. A monitoring system contains a variety of signals, all of which contain useful information, irrelevant information, and noise. In the case of each signal being considered a feature, "Feature Selection" is used to find the most relevant signals. The open-source UCI SECOM Dataset provides 1567 such samples, out of which 104 fail in quality assurance. Feature extraction and selection are performed on the dataset, and useful signals were considered for further study. Afterward, common machine learning algorithms were employed to predict whether the signal yields pass or fail. The most relevant algorithm is selected for prediction based on the accuracy and loss of the ML model.

Keywords: deep learning, feature extraction, feature selection, machine learning classification algorithms, semiconductor production monitoring, signal processing, time-series analysis

Procedia PDF Downloads 97