Search results for: compressive strength prediction

Authors: Saidia Houari

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The game of football is one of the global sports activities that have witnessed a remarkable development in recent years in the physical, technical, rhetorical and psychological aspects, so the modern play in different teams and international teams quickly and forcefully in the exact technical performance, and this is due to the interest of international coaches. The good training of the players during the youth stage at the level of various aspects to develop all the techniques that have a great effectiveness in competitions according to scientific methods studied. The muscle strength plays a very important role achieving the performance player during the game and it is clear the need for the player in many situations, especially when jumping to hit the ball head or the goal on the goal or long passes of different types and in the performance of various skills by force and speed appropriate to the possession of the ball or the control of the court of the court while overcoming the body weight during the game it is known that the stronger the muscles of the athlete and the reduced joints injuries, and the strength increases energy saving such as Latin phosphate and glycogen, and develop the player for a game football volitional qualities of the most important of courage, determination And self-confidence. There are also some skill movements that can not be performed without a certain level of strength, so the development of power may affect the effectiveness of the long-term training system.

Keywords: trainning program, maximum force and expolosive force, lowers limbs, under 17 years

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Authors: Rita Greco, Giuseppe Carlo Marano

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Chloride induced reinforcement corrosion is widely accepted to be the most frequent mechanism causing premature degradation of reinforced concrete members, whose economic and social consequences are growing up continuously. Prevention of these phenomena has a great importance in structural design, and modern Codes and Standard impose prescriptions concerning design details and concrete mix proportion for structures exposed to different external aggressive conditions, grouped in environmental classes. This paper focuses on reinforced concrete columns load carrying capacity degradation over time due to chloride induced steel pitting corrosion. The structural element is considered to be exposed to marine environment and the effects of corrosion are described by the time degradation of the axial-bending interaction diagram. Because chlorides ingress and consequent pitting corrosion propagation are both time-dependent mechanisms, the study adopts a time-variant predictive approach to evaluate the residual strength of corroded reinforced concrete columns at different lifetimes. Corrosion initiation and propagation process is modelled by taking into account all the parameters, such as external environmental conditions, concrete mix proportion, concrete cover and so on, which influence the time evolution of the corrosion phenomenon and its effects on the residual strength of RC columns.

Keywords: pitting corrosion, strength deterioration, diffusion coefficient, surface chloride concentration, concrete structures, marine environment

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Authors: Yukinori Taniguchi, Kazuyoshi Kurita, Kohei Mizuta, Keigo Nishitani, Ryuichi Fukuda

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Tungsten carbide is widely used as a tool material in metal manufacturing process. Since tungsten is typical rare metal, establishment of recycle process of tungsten carbide tools and restore into cemented carbide material bring great impact to metal manufacturing industry. Recently, recycle process of tungsten carbide has been developed and established gradually. However, the demands for quality of cemented carbide tool are quite severe because hardness, toughness, anti-wear ability, heat resistance, fatigue strength and so on should be guaranteed for precision machining and tool life. Currently, it is hard to restore the recycled tungsten carbide powder entirely as raw material for new processed cemented carbide tool. In this study, to suggest positive use of recycled tungsten carbide powder, we have tried to fabricate a carbon based sintered steel which shows reinforced mechanical properties with recycled tungsten carbide powder. We have made set of newly designed sintered steels. Compression test of sintered specimen in density ratio of 0.85 (which means 15% porosity inside) has been conducted. As results, at least 1.7 times higher in nominal strength in the amount of 7.0 wt.% was shown in recycled WC powder. The strength reached to over 600 MPa for the Fe-WC-Co-Cu sintered alloy. Wear test has been conducted by using ball-on-disk type friction tester using 5 mm diameter ball with normal force of 2 N in the dry conditions. Wear amount after 1,000 m running distance shows that about 1.5 times longer life was shown in designed sintered alloy. Since results of tensile test showed that same tendency in previous testing, it is concluded that designed sintered alloy can be used for several mechanical parts with special strength and anti-wear ability in relatively low cost due to recycled tungsten carbide powder.

Keywords: tungsten carbide, recycle process, compression test, powder metallurgy, anti-wear ability

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Authors: M. S. Aparna, Pushparaj Shetty D.

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Nowadays social networking sites have become so popular that the producers or the sellers look for these sites as one of the best options to target the right audience to market their products. There are several tools available to monitor or analyze the social networks. Our task is to identify the right community web pages and find out the behavior analysis of the members by using these tools and formulate an appropriate strategy to market the products or services to achieve the set goals. The advertising becomes more effective when the information of the product/ services come from a known source. The strategy explores great buying influence in the audience on referral marketing. Our methodology proceeds with critical budget analysis and promotes viral influence propagation. In this context, we encompass the vital bits of budget evaluation such as the number of optimal seed nodes or primary influential users activated onset, an estimate coverage spread of nodes and maximum influence propagating distance from an initial seed to an end node. Our proposal for Buyer Prediction mathematical model arises from the urge to perform complex analysis when the probability density estimates of reliable factors are not known or difficult to calculate. Order Statistics and Buyer Prediction mapping function guarantee the selection of optimal influential users at each level. We exercise an efficient tactics of practicing community pages and user behavior to determine the product enthusiasts on social networks. Our approach is promising and should be an elementary choice when there is little or no prior knowledge on the distribution of potential buyers on social networks. In this strategy, product news propagates to influential users on or surrounding networks. By applying the same technique, a user can search friends who are capable to advise better or give referrals, if a product interests him.

Keywords: viral marketing, social network analysis, community web pages, buyer prediction, influence propagation, budget constraints

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Authors: Peter David Reiss

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The concept of the psychosis prodrome has allowed for the identification of adolescent and young adult patients who have a significantly elevated risk of developing schizophrenia spectrum disorders. A number of different interventions have been tested in order to prevent or delay progression of symptoms. To date, there has been no consistent meta-analytical evidence to support efficacy of antipsychotic treatment for patients in the prodromal state, and their use remains therefore inconclusive. Although antipsychotics may manage symptoms transiently, they have not been found to prevent or delay onset of psychotic disorders. Furthermore, pharmacological intervention in high-risk individuals remains controversial, because of the antipsychotic side effect profile in a population in which only about 20 to 35 percent will eventually convert to psychosis over a two-year period, with even after two years conversion rates not exceeding 30 to 40 percent. This general estimate is additionally problematic, in that it ignores the fact that there is significant variation in individual risk among clinical high-risk cases. The current lack of reliable tests for at-risk patients makes it difficult to justify individual treatment decisions. Preventive treatment should ideally be dictated by an individual’s risk while minimizing potentially harmful medication exposure. This requires more accurate predictive assessments by using valid and accessible prognostic markers. The following will compare prediction and risk modification potential of behavioral biomarkers such as disturbances of basic sense of self and emotion awareness, neurocognitive biomarkers such as attention, working and declarative memory, and neurophysiological biomarkers such as glutamatergic abnormalities and NMDA receptor dysfunction. Identification of robust biomarkers could therefore not only provide more reliable means of psychosis prediction, but also help test and develop new clinical interventions targeted at the prodromal state.

Keywords: at-risk mental state, biomarkers, glutamatergic system, NMDA receptor, psychosis prodrome, schizophrenia

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Authors: Muhammad Shahid, Muhammad Mansoor

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Increasing demands of contemporary applications for high strength and lightweight materials prompted the development of metal-matrix composites (MMCs). After the discovery of carbon nanotubes (CNTs) in 1991 (revealing an excellent set of mechanical properties) became one of the most promising strengthening materials for MMC applications. Additionally, the relatively low density of the nanotubes imparted high specific strengths, making them perfect strengthening material to reinforce MMCs. In the present study, aluminum-multiwalled carbon nanotubes (Al-MWCNTs) composite was prepared in an air induction furnace. The dispersion of the nanotubes in molten aluminum was assisted by inherent string action of induction heating at 790°C. During the fabrication process, multifunctional fluxes were used to avoid oxidation of the nanotubes and molten aluminum. Subsequently, the melt was cast in to a copper mold and cold rolled to 0.5 mm thickness. During metallographic examination using a scanning electron microscope, it was observed that the nanotubes were effectively dispersed in the matrix. The mechanical properties of the composite were significantly increased as compared to pure aluminum specimen i.e. the yield strength from 65 to 115 MPa, the tensile strength from 82 to 125 MPa and hardness from 27 to 30 HV for pure aluminum and Al-CNTs composite, respectively. To recognize the associated strengthening mechanisms in the nanocomposites, three foremost strengthening models i.e. shear lag model, Orowan looping and Hall-Petch have been critically analyzed; experimental data were found to be closely satisfying the shear lag model.

Keywords: carbon nanotubes, induction melting, strengthening mechanism, nanocomposite

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Authors: Juan Sebastián Hernández

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The Nevado del Ruíz volcano, located between the limits of the Departments of Caldas and Tolima in Colombia, presented an unstable behaviour in the course of the year 2020, this volcanic activity led to secondary effects on the crust, which is why the prediction of deformations becomes the task of geoscientists. In the course of this article, the use of tropospheric variables such as evapotranspiration, UV aerosol index, carbon monoxide, nitrogen dioxide, methane, surface temperature, among others, is used to train a set of neural networks that can predict the behaviour of the resulting phase of an unrolled interferogram with the DInSAR technique, whose main objective is to identify and characterise the behaviour of the crust based on the environmental conditions. For this purpose, variables were collected, a generalised linear model was created, and a set of neural networks was created. After the training of the network, validation was carried out with the test data, giving an MSE of 0.17598 and an associated r-squared of approximately 0.88454. The resulting model provided a dataset with good thematic accuracy, reflecting the behaviour of the volcano in 2020, given a set of environmental characteristics.

Keywords: crustal deformation, Tropomi, neural networks (ANN), volcanic activity, DInSAR

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Authors: Nattapong Techarattanased, Paweena Sribunrueng

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The objective of this study is to investigate strategies in customer relationship management and customers’ behavior in making decision on buying car insurance of Southeast Insurance Co. Ltd. in Bangkok. Subjects in this study included 400 customers with the age over 20 years old to complete questionnaires. The data were analyzed by arithmetic mean and multiple regressions. The results revealed that the customers’ opinions on the strategies in customer relationship management, i.e. customer relationship, customer feedback, customer follow-up, useful service suggestions, customer communication, and service channels were in moderate level but on the customer retention was in high level. Moreover, the strategy in customer relationship management, i.e. customer relationship, and customer feedback had an influence on customers’ buying decision on buying car insurance. The two factors above can be used for the prediction at the rate of 34%. In addition, the strategy in customer relationship management, i.e. customer retention, customer feedback, and useful service suggestions had an influence on the customers’ buying decision on period of being customers. The three factors could be used for the prediction at the rate of 45%.

Keywords: strategies, customer relationship management, behavior in buying decision, car insurance

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Authors: Esra Yel, Tabriz Aslanov, Merve Sogancioglu, Suheyla Kocaman, Gulnare Ahmetli

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The disposal of waste plastics has become a major worldwide environmental problem. Pyrolysis of waste plastics is one of the routes to waste minimization and recycling that has been gaining interest. In pyrolysis, the pyrolysed material is separated into gas, liquid (both are fuel) and solid (char) products. All fractions have utilities and economical value depending upon their characteristics. The first objective of this study is to determine the co-pyrolysis product fractions of waste HDPE- (high density polyethylene) and LDPE (low density polyethylene)-olive pomace (OP) and to determine the qualities of the solid product char. Chars obtained at 700 °C pyrolysis were used in biocomposite preparation as additive. As the second objective, the effects of char on biocomposite quality were investigated. Pyrolysis runs were performed at temperature 700 °C with heating rates of 5 °C/min. Biocomposites were prepared by mixing of chars with bisphenol-F type epoxy resin in various wt%. Biocomposite properties were determined by measuring electrical conductivity, surface hardness, Young’s modulus and tensile strength of the composites. The best electrical conductivity results were obtained with HDPE-OP char. For HDPE-OP char and LDPE-OP char, compared to neat epoxy, the tensile strength values of the composites increased by 102% and 78%, respectively, at 10% char dose. The hardness measurements showed similar results to the tensile tests, since there is a correlation between the hardness and the tensile strength.

Keywords: biocomposite, char, olive pomace, pyrolysis

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Authors: Chau-Kuang Chen, John Hughes, Jr., A. Dexter Samuels

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The prediction models for the United States Medical Licensure Examination (USMLE) Steps 1 and 2 performances were constructed by the Monte Carlo simulation modeling approach via linear regression. The purpose of this study was to build robust simulation models to accurately identify the most important predictors and yield the valid range estimations of the Steps 1 and 2 scores. The application of simulation modeling approach was deemed an effective way in predicting student performances on licensure examinations. Also, sensitivity analysis (a/k/a what-if analysis) in the simulation models was used to predict the magnitudes of Steps 1 and 2 affected by changes in the National Board of Medical Examiners (NBME) Basic Science Subject Board scores. In addition, the study results indicated that the Medical College Admission Test (MCAT) Verbal Reasoning score and Step 1 score were significant predictors of the Step 2 performance. Hence, institutions could screen qualified student applicants for interviews and document the effectiveness of basic science education program based on the simulation results.

Keywords: prediction model, sensitivity analysis, simulation method, USMLE

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Authors: Haiyan Wu, Ying Liu, Shaoyun Shi

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Authorship attribution is to extract features to identify authors of anonymous documents. Many previous works on authorship attribution focus on statistical style features (e.g., sentence/word length), content features (e.g., frequent words, n-grams). Modeling these features by regression or some transparent machine learning methods gives a portrait of the authors' writing style. But these methods do not capture the syntactic (e.g., dependency relationship) or semantic (e.g., topics) information. In recent years, some researchers model syntactic trees or latent semantic information by neural networks. However, few works take them together. Besides, predictions by neural networks are difficult to explain, which is vital in authorship attribution tasks. In this paper, we not only utilize the statistical style and content features but also take advantage of both syntactic and semantic features. Different from an end-to-end neural model, feature selection and prediction are two steps in our method. An attentive n-gram network is utilized to select useful features, and logistic regression is applied to give prediction and understandable representation of writing style. Experiments show that our extracted features can improve the state-of-the-art methods on three benchmark datasets.

Keywords: authorship attribution, attention mechanism, syntactic feature, feature extraction

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Authors: T. A. Sakr, Hanaa E. Abd-El-Mottaleb

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Outrigger-braced wall systems are commonly used to provide high rise buildings with the required lateral stiffness for wind and earthquake resistance. The existence of outriggers adds to the stiffness and strength of walls as reported by several studies. The effects of different parameters on the elasto-plastic dynamic behavior of outrigger-braced wall systems to earthquakes are investigated in this study. Parameters investigated include outrigger stiffness, concrete strength, and reinforcement arrangement as the main design parameters in wall design. In addition to being significant to the wall behavior, such parameters may lead to the change of failure mode and the delay of crack propagation and consequently failure as the wall is excited by earthquakes. Bi-linear stress-strain relation for concrete with limited tensile strength and truss members with bi-linear stress-strain relation for reinforcement were used in the finite element analysis of the problem. The famous earthquake record, El-Centro, 1940 is used in the study. Emphasis was given to the lateral drift, normal stresses and crack pattern as behavior controlling determinants. Results indicated significant effect of the studied parameters such that stiffer outrigger, higher grade concrete and concentrating the reinforcement at wall edges enhance the behavior of the system. Concrete stresses and cracking behavior are sigbificantly enhanced while lesser drift improvements are observed.

Keywords: outrigger, shear wall, earthquake, nonlinear

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Authors: D. Castillo T., Luis F. Jimenez

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The compressed earth blocks (CEBs) constitute an alternative as a constructive element for building homes in regions with high levels of poverty and marginalization. Such is the case of Southeastern Mexico, where the population, predominantly indigene, build their houses with feeble materials like wood and palm, vulnerable to extreme weather in the area, because they do not have the financial resources to acquire concrete blocks. There are several advantages that can provide BTCs compared to traditional vibro-compressed concrete blocks, such as the availability of materials, low manufacturing cost and reduced CO2 emissions to the atmosphere for not be subjected to a burning process. However, to improve its mechanical properties and resistance to adverse weather conditions in terms of humidity and temperature of the sub-humid climate zones, it requires the use of a chemical stabilizer; in this case we chose Ca(OH)2. The stabilization method Eades-Grim was employed, according to ASTM C977-03. This method measures the optimum amount of lime required to stabilize the soil, increasing the pH to 12.4 or higher. The minimum amount of lime required in this experiment was 1% and the maximum was 10%. The employed material was clay unconsolidated low to medium plasticity (CL type according to the Unified Soil Classification System). Based on these results, the CEBs manufacturing process was determined. The obtained blocks were from 10x15x30 cm using a mixture of soil, water and lime in different proportions. Later these blocks were put to dry outdoors and subjected to several physical and mechanical tests, such as compressive strength, absorption and drying shrinkage. The results were compared with the limits established by the Mexican Standard NMX-C-404-ONNCCE-2005 for the construction of housing walls. In this manner an alternative and sustainable material was obtained for the construction of rural households in the region, with better security conditions, comfort and cost.

Keywords: calcium hydroxide, chemical stabilization, compressed earth blocks, sub-humid warm weather

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Authors: Triveni Gogoi, Rima Chatterjee

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Shear wave velocity (Vs) estimation is an important approach in the seismic exploration and characterization of a hydrocarbon reservoir. There are varying methods for prediction of S-wave velocity, if recorded S-wave log is not available. But all the available methods for Vs prediction are empirical mathematical models. Shear wave velocity can be estimated using P-wave velocity by applying Castagna’s equation, which is the most common approach. The constants used in Castagna’s equation vary for different lithologies and geological set-ups. In this study, multiple regression analysis has been used for estimation of S-wave velocity. The EMERGE module from Hampson-Russel software has been used here for generation of S-wave log. Both single attribute and multi attributes analysis have been carried out for generation of synthetic S-wave log in Upper Assam basin. Upper Assam basin situated in North Eastern India is one of the most important petroleum provinces of India. The present study was carried out using four wells of the study area. Out of these wells, S-wave velocity was available for three wells. The main objective of the present study is a prediction of shear wave velocities for wells where S-wave velocity information is not available. The three wells having S-wave velocity were first used to test the reliability of the method and the generated S-wave log was compared with actual S-wave log. Single attribute analysis has been carried out for these three wells within the depth range 1700-2100m, which corresponds to Barail group of Oligocene age. The Barail Group is the main target zone in this study, which is the primary producing reservoir of the basin. A system generated list of attributes with varying degrees of correlation appeared and the attribute with the highest correlation was concerned for the single attribute analysis. Crossplot between the attributes shows the variation of points from line of best fit. The final result of the analysis was compared with the available S-wave log, which shows a good visual fit with a correlation of 72%. Next multi-attribute analysis has been carried out for the same data using all the wells within the same analysis window. A high correlation of 85% has been observed between the output log from the analysis and the recorded S-wave. The almost perfect fit between the synthetic S-wave and the recorded S-wave log validates the reliability of the method. For further authentication, the generated S-wave data from the wells have been tied to the seismic and correlated them. Synthetic share wave log has been generated for the well M2 where S-wave is not available and it shows a good correlation with the seismic. Neutron porosity, density, AI and P-wave velocity are proved to be the most significant variables in this statistical method for S-wave generation. Multilinear regression method thus can be considered as a reliable technique for generation of shear wave velocity log in this study.

Keywords: Castagna's equation, multi linear regression, multi attribute analysis, shear wave logs

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Authors: Tomofumi Kubota, Mitsuhiro Okayasu

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In recent years, environmental problems and energy problems typified by global warming are intensifying, and transportation devices are required to reduce the weight of structural materials from the viewpoint of strengthening fuel efficiency regulations and energy saving. Carbon fiber reinforced plastic (CFRP) used in this research is attracting attention as a structural material to replace metallic materials. Among them, thermoplastic CFRP is expected to expand its application range in terms of recyclability and cost. High formability and weldability of the unidirectional CFRP sheets conducted by a proposed hot stamping process were proposed, in which the carbon fiber reinforced plastic sheets are heated by a designed technique. In this case, the CFRP sheets are heated by the high electric voltage applied through carbon fibers. In addition, the electric voltage was controlled by the area ratio of exposed carbon fiber on the sample surfaces. The lower exposed carbon fiber on the sample surface makes high electric resistance leading to the high sample temperature. In this case, the CFRP sheets can be heated to more than 150 °C. With the sample heating, the stamping and welding technologies can be carried out. By changing the sample temperature, the suitable stamping condition can be detected. Moreover, the proper welding connection of the CFRP sheets was proposed. In this study, we propose a fusion bonding technique using thermoplasticity, high current flow, and heating caused by electrical resistance. This technology uses the principle of resistance spot welding. In particular, the relationship between the carbon fiber exposure rate and the electrical resistance value that affect the bonding strength is investigated. In this approach, the mechanical connection using rivet is also conducted to make a comparison of the severity of welding. The change of connecting strength is reflected by the fracture mechanism. The low and high connecting strength are obtained for the separation of two CFRP sheets and fractured inside the CFRP sheet, respectively. In addition to the two fracture modes, micro-cracks in CFRP are also detected. This approach also includes mechanical connections using rivets to compare the severity of the welds. The change in bond strength is reflected by the destruction mechanism. Low and high bond strengths were obtained to separate the two CFRP sheets, each broken inside the CFRP sheets. In addition to the two failure modes, micro cracks in CFRP are also detected. In this research, from the relationship between the surface carbon fiber ratio and the electrical resistance value, it was found that different carbon fiber ratios had similar electrical resistance values. Therefore, we investigated which of carbon fiber and resin is more influential to bonding strength. As a result, the lower the carbon fiber ratio, the higher the bonding strength. And this is 50% better than the conventional average strength. This can be evaluated by observing whether the fracture mode is interface fracture or internal fracture.

Keywords: CFRP, hot stamping, weliding, deforamtion, mechanical property

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Authors: Alba Barbara-i-Molinero, Rosalia Cascon Pereira, Ana Beatriz Hernandez Lara

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Our general motivation in undertaking this study is to propose alternative measures to lighten students experienced tensions during the transitions from high school to higher education based on the concept of professional identity strength. In order to do so, we measured the influence that three different factors external motivational conditionals, educational experience conditionals and personal motivation conditionals exerted over students’ professional identity strength and proposed the measures considering the obtained results. By using hierarchical regression analysis we addressed this issue, across disciplines and bachelor degrees, allowing us to gain also deeper insight into first-year university students PID. Our findings suggest that students’ from the different disciplines are influenced by personal motivational conditionals; while students from sciences are also influenced by external motivational conditionals. Based on the obtained results we propose three different alternative educational and recruitment strategies which aim to increase students’ professional identity strength and reduce the tensions generated during high school-university transitions. From this study theoretical contributions regarding the differences in the influence of these factors on students from different bachelor degrees arise; and practical implications for universities, derived from the proposed strategies.

Keywords: professional identity, transitions, higher education, strategies

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Authors: Paul Han, Jun-Geol Baek

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In the flat panel display industry, the scheduler and dispatching system to meet production target quantities and the deadline of production are the major production management system which controls each facility production order and distribution of WIP (Work in Process). In dispatching system, delivery time is a key factor for the time when a lot can be supplied to the facility. In this paper, we use survival analysis methods to identify main factors and a forecasting model of delivery time. Of survival analysis techniques to select important explanatory variables, the cox proportional hazard model is used to. To make a prediction model, the Accelerated Failure Time (AFT) model was used. Performance comparisons were conducted with two other models, which are the technical statistics model based on transfer history and the linear regression model using same explanatory variables with AFT model. As a result, the Mean Square Error (MSE) criteria, the AFT model decreased by 33.8% compared to the existing prediction model, decreased by 5.3% compared to the linear regression model. This survival analysis approach is applicable to implementing a delivery time estimator in display manufacturing. And it can contribute to improve the productivity and reliability of production management system.

Keywords: delivery time, survival analysis, Cox PH model, accelerated failure time model

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Authors: M. Esmailian, M. Shakouri, A. Mottahedi, S. G. Shabestari

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Heat treatable aluminium alloys such as 7075 and 7055, because of high strength and low density, are used widely in aircraft industry. For best mechanical properties, T6 heat treatment has recommended for this regards, but this temper treatment is sensitive to corrosion induced and Stress Corrosion Cracking (SCC) damage. For improving this property, the over-aging treatment (T7) applies to this alloy, but it decreases the mechanical properties up to 30 percent. Hence, to increase the mechanical properties, without any remarkable decrease in SCC resistant, Retrogression and Re-Aging (RRA) heat treatment is used. This treatment performs in a relatively short time. In this paper, the RRA heat treatment was applied to 7055 aluminum alloy and then effect of RRA time on the mechanical properties of 7055 has been investigated. The results show that the 40 minute time is suitable time for retrogression of 7055 aluminum alloy and ultimate strength increases up to 625MPa.

Keywords: 7055 Aluminum alloy, mechanical properties, SCC resistance, heat Treatment

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Authors: F. J. Ma, A. K. H. Kwan

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Crack caused by shrinkage movement of concrete is a serious problem especially when restraint is provided. It may cause severe serviceability and durability problems. The existing prediction methods for crack width of concrete due to shrinkage movement are mainly numerical methods under simplified circumstances, which do not agree with each other. To get a more unified prediction method applicable to more sophisticated circumstances, finite element crack width analysis for shrinkage effect should be developed. However, no existing finite element analysis can be carried out to predict the crack width of concrete due to shrinkage movement because of unsolved reasons of conventional finite element analysis. In this paper, crack width analysis implemented by finite element analysis is presented with pseudo-discrete crack model, which combines traditional smeared crack model and newly proposed crack queuing algorithm. The proposed pseudo-discrete crack model is capable of simulating separate and single crack without adopting discrete crack element. And the improved finite element analysis can successfully simulate the stress redistribution when concrete is cracked, which is crucial for predicting crack width, crack spacing and crack number.

Keywords: crack queuing algorithm, crack width analysis, finite element analysis, shrinkage effect

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Authors: Ghufran Ahmed, Muhammad Osama Siddiqui, Shahbaz Siddiqui, Rauf Ahmad Shams Malick, Faisal Khan, Mubashir Khan

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In this paper, a machine learning-based approach for early prediction of diseases in cows is proposed. Different ML algos are applied to extract useful patterns from the available dataset. Technology has changed today’s world in every aspect of life. Similarly, advanced technologies have been developed in livestock and dairy farming to monitor dairy cows in various aspects. Dairy cattle monitoring is crucial as it plays a significant role in milk production around the globe. Moreover, it has become necessary for farmers to adopt the latest early prediction technologies as the food demand is increasing with population growth. This highlight the importance of state-ofthe-art technologies in analyzing how important technology is in analyzing dairy cows’ activities. It is not easy to predict the activities of a large number of cows on the farm, so, the system has made it very convenient for the farmers., as it provides all the solutions under one roof. The cattle industry’s productivity is boosted as the early diagnosis of any disease on a cattle farm is detected and hence it is treated early. It is done on behalf of the machine learning output received. The learning models are already set which interpret the data collected in a centralized system. Basically, we will run different algorithms on behalf of the data set received to analyze milk quality, and track cows’ health, location, and safety. This deep learning algorithm draws patterns from the data, which makes it easier for farmers to study any animal’s behavioral changes. With the emergence of machine learning algorithms and the Internet of Things, accurate tracking of animals is possible as the rate of error is minimized. As a result, milk productivity is increased. IoT with ML capability has given a new phase to the cattle farming industry by increasing the yield in the most cost-effective and time-saving manner.

Keywords: IoT, machine learning, health care, dairy cows

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Authors: Ploenpit Boochathum, Pitchayanad Kaolim, Phimjutha Srisangkaew

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In general, rubber foam is produced based on the sulfur curing system. However, the remaining sulfur in the rubber product waste is burned to sulfur dioxide gas causing the environment pollution. To avoid using sulfur as curing agent in the rubber foam products, this research work proposes non-sulfur curing system by using corn starch as a curing agent. The ether crosslinks were proposed to be produced via the functional bonding between hydroxyl groups of the starch molecules and chloroacetate groups added on the natural rubber molecules. The chloroacetated natural rubber (CNR) latex was prepared via the epoxidation reaction of the concentrated natural rubber latex, subsequently, epoxy rings were attacked by chloroacetic acid to produce hydroxyl groups and chloroacetate groups on the rubber molecules. Foaming agent namely NaHCO3 was selected to add in the CNR latex due to the low decomposition temperature at about 50°C. The appropriate curing temperature was assigned to be 90°C that is above gelatinization temperature; 60-70°C, of starch. The effect of weight ratio of starch, i.e., 0 phr, 3 phr and 5 phr, on the physical properties of CNR rubber foam was investigated. It was found that density reduced from 0.81 g/cm3 for 0 phr to 0.75 g/cm3 for 3 phr and 0.79 g/cm3 for 5 phr. The ability to return to its original thickness after prolonged compressive stresses of CNR rubber foam cured with starch loading of 5 phr was found to be considerably better than that of CNR rubber foam cured with starch 3 phr and CNR rubber foam without addition of starch according to the compression set that was determined to decrease from 66.67% to 40% and 26.67% with the increase loading of starch. The mechanical properties including tensile strength and modulus of CNR rubber foams cured using starch were determined to increase except that the elongation at break was found to decrease. In addition, all mechanical properties of CNR rubber foams cured with the starch 3 phr and 5 phr were found to be slightly different and drastically higher than those of CNR rubber foam without the addition of starch. This research work indicates that starch can be applicable as a curing agent for CNR rubber. This is confirmed by the increase of the elastic modulus (G') of CNR rubber foams that was cured with the starch over the CNR rubber foam without curing agent. This type of rubber foam is believed to be one of the biodegradable and environment-friendly product that can be cured at low temperature of 90°C.

Keywords: chloroacetated natural rubber, corn starch, non-sulfur curing system, rubber foam

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Authors: Yangsu Kwon, Hyo-Gyoung Kwak

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Nonlinear tendon constitutive model for nonlinear analysis of pre-stressed concrete structures are presented. Since the post-cracking behavior of concrete structures, in which bonded reinforcements such as tendons and/or reinforcing steels are embedded, depends on many influencing factors(the tensile strength of concrete, anchorage length of reinforcements, concrete cover, and steel spacing) that are deeply related to the bond characteristics between concrete and reinforcements, consideration of the tension stiffening effect on the basis of the bond-slip mechanism is necessary to evaluate ultimate resisting capacity of structures. In this paper, an improved tendon model, which considering the slip effect between concrete and tendon, and effect of tension stiffening, is suggested. The validity of the proposed models is established by comparing between the analytical results and experimental results in pre-stressed concrete beams.

Keywords: bond-slip, prestressed concrete, tendon, ultimate strength

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Authors: Florence Chinyere Nwangwu, Rosemary Ene

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An experimental study was carried out to develop an adhesive from Prosopis africana seed endosperm. The Prosopis seeds for this work were obtained from Enugu State in the South East part of Nigeria. The Prosopis seeds were prepared by separating the Prosopis endosperm from the seed coat and cotyledon. The dry adhesive gotten from the endosperm was later dissolved to get the adhesive solution. Confirmatory tests like viscosity, density, pH, and binding strength were carried out. The effect of time, temperature, concentration on the yield and properties of the adhesive were investigated. The results obtained showed that increase in concentration, time, temperature decreases the viscosity of the Prosopis adhesive and yield of Prosopis endosperm. It was also deduced that increase in viscosity increases the binding strength of the Prosopis adhesive. The percentage of the adhesive yield from Prosopis endosperm showed that the commercialization of the seed in Nigeria will be possible and profitable.

Keywords: adhesive, Prosopis, viscosity, endosperm

Procedia PDF Downloads 304

Authors: Ashish Dhamaniya, Vineet Jain, Rajesh Chouhan

Abstract:

Traffic management is one of the gigantic issue in most of the urban roads in al-most all metropolitan cities in India. Speed is one of the critical traffic parameters for effective Intelligent Transportation System (ITS) implementation as it decides the arrival rate of vehicles on an intersection which are majorly the point of con-gestions. The study aimed to leverage Machine Learning (ML) models to produce precise predictions of speed on urban roadway links. The research objective was to assess how categorized traffic volume and road width, serving as variables, in-fluence speed prediction. Four tree-based regression models namely: Decision Tree (DT), Random Forest (RF), Extra Tree (ET), and Extreme Gradient Boost (XGB)are employed for this purpose. The models' performances were validated using test data, and the results demonstrate that Random Forest surpasses other machine learning techniques and a conventional utility theory-based model in speed prediction. The study is useful for managing the urban roadway network performance under mixed traffic conditions and effective implementation of ITS.

Keywords: stream speed, urban roads, machine learning, traffic flow

Procedia PDF Downloads 65

Authors: Ameer A. Jebur, William Atherton, Rafid M. Alkhaddar, Edward Loffill

Abstract:

Understanding the behaviour of the piles under the action of the independent lateral loads and the precise prediction of the capacity of piles subjected to different lateral loads are vital topics in foundation design and analysis. Moreover, the laterally loaded behaviour of deep foundations penetrated in cohesive and non-cohesive soils is basically analysed by the Winkler Model (beam on elastic foundation), in which the interaction between the pile embedded depth and contacted soil is simulated by nonlinear p–y curves. The presence of many approaches to interpret the behaviour of soil-pile interaction has resulted in numerous outputs and indicates that no general approach has yet been adopted. The current study presents the result of numerical modelling of the behaviour of steel tubular piles (25.4mm) outside diameter with various embedment depth-to-diameter ratios (L/d) embedded in a sand calibrated chamber of known relative density. The study revealed that the shear strength parameters of the sand specimens and the (L/d) ratios are the most significant factor influencing the response of the pile and its capacity while taking into consideration the complex interaction between the pile and soil. Good agreement has been achieved when comparing the application of this modelling approach with experimental physical modelling carried out by another researcher.

Keywords: deep foundations, slenderness ratio, soil-pile interaction, winkler model (beam on elastic foundation), non-cohesive soil

Procedia PDF Downloads 295

Authors: R. M. Dodo, Z. Musa, K. A. Bello, U. Abdullahi, G. A. Faruna

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Temperature depended austempering of high carbon steel using epoxidized-transesterified cotton seed oil (ETO) was examined. Five set of samples were heated to 850oC and held for one hour then quenched in oil bath of ETO at 250oC at one hour holding time. The same procedure was performed on the rest of the samples and austempered at 270oC, 290oC, 310oC and 330oC. Next, mechanical properties’ tests conducted. The austempered samples were then analyzed for microstructure using scanning electron microscope (SEM). The results indicate that tensile strength and hardness dip with increase in the temperature. Again, impact strength improved with rise in the temperature. It was observed that 270oC is the best austempering temperature, since it produces austempered sample with the best combination of mechanical properties.

Keywords: epoxidized transesterified cotton seed oil, austempering temperature, high carbon steel, bainitic structure

Procedia PDF Downloads 35

Authors: Jeremy Jenrette, Z. Y. C. Liu, Pranav Chimote, Edward Fox, Trevor Hastie, Francesco Ferretti

Abstract:

Suitable shark conservation depends on well-informed population assessments. Direct methods such as scientific surveys and fisheries monitoring are adequate for defining population statuses, but species-specific indices of abundance and distribution coming from these sources are rare for most shark species. We can rapidly fill these information gaps by boosting media-based remote monitoring efforts with machine learning and automation. We created a database of shark images by sourcing 24,546 images covering 219 species of sharks from the web application spark pulse and the social network Instagram. We used object detection to extract shark features and inflate this database to 53,345 images. We packaged object-detection and image classification models into a Shark Detector bundle. We developed the Shark Detector to recognize and classify sharks from videos and images using transfer learning and convolutional neural networks (CNNs). We applied these models to common data-generation approaches of sharks: boosting training datasets, processing baited remote camera footage and online videos, and data-mining Instagram. We examined the accuracy of each model and tested genus and species prediction correctness as a result of training data quantity. The Shark Detector located sharks in baited remote footage and YouTube videos with an average accuracy of 89\%, and classified located subjects to the species level with 69\% accuracy (n =\ eight species). The Shark Detector sorted heterogeneous datasets of images sourced from Instagram with 91\% accuracy and classified species with 70\% accuracy (n =\ 17 species). Data-mining Instagram can inflate training datasets and increase the Shark Detector’s accuracy as well as facilitate archiving of historical and novel shark observations. Base accuracy of genus prediction was 68\% across 25 genera. The average base accuracy of species prediction within each genus class was 85\%. The Shark Detector can classify 45 species. All data-generation methods were processed without manual interaction. As media-based remote monitoring strives to dominate methods for observing sharks in nature, we developed an open-source Shark Detector to facilitate common identification applications. Prediction accuracy of the software pipeline increases as more images are added to the training dataset. We provide public access to the software on our GitHub page.

Keywords: classification, data mining, Instagram, remote monitoring, sharks

Procedia PDF Downloads 110

Authors: Xia Chen, Hua-Quan Yang, Shi-Hua Zhou

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Alkali-active sandstone aggregate was ground by vertical and ball mill into particles with residue over 45 μm less than 12%, and investigations have been launched on particles distribution and characterization of ground sandstone powder, fluidity, heat of hydration, strength as well as hydration products morphology of pastes with incorporation of ground sandstone powder. Results indicated that ground alkali-active sandstone powder with residue over 45 μm less than 8% was easily obtainable, and specific surface area was more sensitive to characterize its fineness with extension of grinding length. Incorporation of sandstone powder resulted in higher water demand and lower strength, advanced hydration of C₃A and C₂S within 3days and refined pore structure. Based on its manufacturing, characteristics and influence on properties of pastes, it was concluded that sandstone powder was a good selection for use in concrete as mineral admixture.

Keywords: concrete, mineral admixture, hydration, structure

Procedia PDF Downloads 325

Authors: Andreas Livera, Spyros Theocharides, Michalis Florides, Charalambos Anastassiou

Abstract:

A main challenge in the quest for ensuring quality of operation, especially for photovoltaic (PV) systems, is to safeguard the reliability and optimal performance by detecting and diagnosing potential failures and performance losses at early stages or before the occurrence through real-time monitoring, supervision, fault detection, and predictive maintenance. The purpose of this work is to present the functionalities and results related to the development and validation of a software platform for PV assets diagnosis and maintenance. The platform brings together proprietary hardware sensors and software algorithms to enable the early detection and prediction of the most common and critical faults in PV systems. It was validated using field measurements from operating PV systems. The results showed the effectiveness of the platform for detecting faults and losses (e.g., inverter failures, string disconnections, and potential induced degradation) at early stages, forecasting PV power production while also providing recommendations for maintenance actions. Increased PV energy yield production and revenue can be thus achieved while also minimizing operation and maintenance (O&M) costs.

Keywords: failure detection and prediction, operation and maintenance, performance monitoring, photovoltaic, platform, recommendations, predictive maintenance

Procedia PDF Downloads 43

Authors: Angga S. Fajar, Y. Takahashi, J. Kiyono, S. Sawada

Abstract:

The structural system concept of RC pier accompanied with multi sliding friction damping mechanism was developed based on numerical analysis approach. However in the implementation, to make design for such kind of this structural system consumes a lot of effort in case high of complexity. During making design, the special behaviors of this structural system should be considered including flexible small deformation, sufficient elastic deformation capacity, sufficient lateral force resistance, and sufficient energy dissipation. The confinement distribution of friction devices has significant influence to its. Optimization and prediction with multi function regression of this structural system expected capable of providing easier and simpler design method. The confinement distribution of friction devices is optimized with SNOPT in Opensees, while some design variables of the structure are predicted using multi function regression of ANN. Based on the optimization and prediction this structural system is able to be designed easily and simply.

Keywords: RC Pier, multi sliding friction device, optimal design, flexible small deformation

Procedia PDF Downloads 361