Search results for: compressive strength prediction

Authors: S. Holly, R. Schnitzer, P. Haslberger, D. Zügner

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The development of new filler consumables necessitates a high effort regarding samples and experiments to achieve the required mechanical properties and chemistry. In the scope of the development of a metal-cored wire with the target tensile strength of 1150 MPa and acceptable impact toughness, thermodynamic and kinetic calculations via MatCalc were used to reduce the experimental work and the resources required. Micro alloying elements were used to reach the high strength as an alternative approach compared to the conventional solid solution hardening. In order to understand the influence of different micro alloying elements in more detail, the influence of different elements on the precipitation behavior in the weld metal was evaluated. Investigations of the microstructure were made via atom probe and EBSD to understand the effect of micro alloying elements. The calculated results are in accordance with the results obtained by experiments and can be explained by the microstructural investigations. On the example of aluminium, the approach is exemplified and clarifies the efficient way of development.

Keywords: alloy development, high strength steel, MatCalc, metal-cored wire

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Authors: Rehan Waheed, Abdul Shakoor

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Distortion produced during welding of thin metal plates is a problem in many industries. The purpose of this research was to study distortion produced during welding in 2mm Mild Steel plate by simulating the welding process using Finite Element Analysis. Simulation of welding process requires a couple field transient analyses. At first a transient thermal analysis is performed and the temperature obtained from thermal analysis is used as input in structural analysis to find distortion. An actual weld sample is prepared and the weld distortion produced is measured. The simulated and actual results were in quite agreement with each other and it has been found that there is profound deflection at center of plate. Temperature dependent material properties play significant role in prediction of weld distortion. The results of this research can be used for prediction and control of weld distortion in large steel structures by changing different weld parameters.

Keywords: welding simulation, FEA, welding distortion, temperature dependent mechanical properties

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Authors: Maryam Shirmohammadi, Prasad K. D. V. Yarlagadda, YuanTong Gu

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The mechanical damage on the agricultural crop during and after harvesting can create high volume of damage on tissue. Uniaxial compression and tensile loading were performed on flesh and peel samples of pumpkin. To investigate the structural changes on the tissue, Scanning Electron Microscopy (SEM) was used to capture the cellular structure change before and after loading on tissue for tensile, compression and indentation tests. To obtain required mechanical properties of tissue for the finite element analysis (FEA) model, laser measurement sensors were used to record the lateral displacement of tissue under the compression loading. Uniaxial force versus deformation data were recorded using Universal Testing Machine for both tensile and compression tests. The experimental Results were employed to develop a material model with failure criteria. The results obtained by the simulation were compared with those obtained by experiments. Note that although modelling food materials’ behaviour is not a new concept however, majority of previous studies focused on elastic behaviour and damages under linear limit, this study, however, has developed FEA models for tensile and compressive loading of pumpkin flesh and peel samples using, as the first study, both elastic and elasto-plastic material types. In addition, pumpkin peel and flesh tissues were considered as two different materials with different properties under mechanical loadings. The tensile and compression loadings were used to develop the material model for a composite structure for FEA model of mechanical peeling of pumpkin as a tough skinned vegetable.

Keywords: compressive and tensile response, finite element analysis, poisson’s ratio, elastic modulus, elastic and plastic response, rupture and bio-yielding

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Authors: Parinaz Arashnia, Farzad Hatami, Saeed Ghaffarpour Jahromi

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Using steel in bridges’ construction because of their desired tensile and compressive strength and light weight especially in large spans was widely popular. Disadvantages of steel such as corrosion, buckling and weaknesses in high temperature and unsuitable weld could be solve with using Fibres Reinforced Polymer (FRP) profiles. The FRP is a remarkable class of composite polymers that can improve structural elements behaviour like corrosion resistance, fir resistance with good proofing and electricity and magnetic non-conductor. Nowadays except FRP reinforced bars and laminates, FRP I-beams are made and studied. The main reason for using FRP profiles is, prevent of corrosion and increase the load carrying capacity and durability, especially in large spans in bridges’ deck. In this paper, behaviour of I-section glass fibres reinforced polymer (GFRP) beam is discussed under point loads with numerical models and results has been compared and verified with experimental tests.

Keywords: glass fibres reinforced polymer, composite, I-section beam, durability, finite element method, numerical model

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Authors: Hyunchul Ahn, William X. S. Wong

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Corporate credit rating prediction is one of the most important topics, which has been studied by researchers in the last decade. Over the last decade, researchers are pushing the limit to enhance the exactness of the corporate credit rating prediction model by applying several data-driven tools including statistical and artificial intelligence methods. Among them, multiclass support vector machine (MSVM) has been widely applied due to its good predictability. However, heuristics, for example, parameters of a kernel function, appropriate feature and instance subset, has become the main reason for the critics on MSVM, as they have dictate the MSVM architectural variables. This study presents a hybrid MSVM model that is intended to optimize all the parameter such as feature selection, instance selection, and kernel parameter. Our model adopts genetic algorithm (GA) to simultaneously optimize multiple heterogeneous design factors of MSVM.

Keywords: corporate credit rating prediction, Feature selection, genetic algorithms, instance selection, multiclass support vector machines

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Authors: Viriyavudh Sim, Jung Kyu Choi, Yong Ju Kwak, Oh Hyeon Jeon, Woo Young Jung

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In this study, we investigated the buckling performance of basalt fiber reinforced polymer (BFRP) sandwich infill panels. Fiber Reinforced Polymer (FRP) is a major evolution for energy dissipation when used as infill material of frame structure, a basic Polymer Matrix Composite (PMC) infill wall system consists of two FRP laminates surrounding an infill of foam core. Furthermore, this type of component is for retrofitting and strengthening frame structure to withstand the seismic disaster. In-plane compression was considered in the numerical analysis with ABAQUS platform to determine the buckling failure load of BFRP infill panel system. The present result shows that the sandwich BFRP infill panel system has higher resistance to buckling failure than those of glass fiber reinforced polymer (GFRP) infill panel system, i.e. 16% increase in buckling resistance capacity.

Keywords: Basalt Fiber Reinforced Polymer (BFRP), buckling performance, FEM analysis, sandwich infill panel

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Authors: Henrik L. Funke, Lars Ulke-Winter, Sandra Gelbrich, Lothar Kroll

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This work reports about an approach for an automatic adaptation of concrete formulations based on genetic algorithms (GA) to optimize a wide range of different fit-functions. In order to achieve the goal, a method was developed which provides a numerical description of a fibre reinforced concrete (FRC) mixture regarding the production technology and the property spectrum of the concrete. In a first step, the FRC mixture with seven fixed components was characterized by varying amounts of the components. For that purpose, ten concrete mixtures were prepared and tested. The testing procedure comprised flow spread, compressive and bending tensile strength. The analysis and approximation of the determined data was carried out by GAs. The aim was to obtain a closed mathematical expression which best describes the given seven-point cloud of FRC by applying a Gene Expression Programming with Free Coefficients (GEP-FC) strategy. The seven-parametric FRC-mixtures model which is generated according to this method correlated well with the measured data. The developed procedure can be used for concrete mixtures finding closed mathematical expressions, which are based on the measured data.

Keywords: concrete design, fibre reinforced concrete, genetic algorithms, GEP-FC

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Authors: Abdelkader Hocine, Abdelhakim Maizia

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The exponential growth of reinforced fibers composite materials use has prompted researchers to step up their work on the prediction of their reliability. Owing to differences between the properties of the materials used for the composite, the manufacturing processes, the load combinations and types of environment, the prediction of the reliability of composite materials has become a primary task. Through failure criteria, TSAI-WU and the maximum stress, the reliability of multilayer tubular structures under pressure is the subject of this paper, where the failure probability of is estimated by the method of Monte Carlo.

Keywords: composite, design, monte carlo, tubular structure, reliability

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Authors: K. Thawichai, R. Pornthep

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This study examined the effects of ten basic exercises of Muay Thai Chaiya training for balance and strength in male older adults. Thirty male older adult volunteer from Thayang elderly clubs, Thayang, Petchaburi, Thailand. All participants were randomly assigned to two groups a training group and a control group. The training group (n=15) participated in eight week training program of ten basic exercises of Muay Thai Chaiya training and not to change or increase another exercise during of the study. In the control group, (n=15) did not participate in ten basic exercises of Muay Thai Chaiya training. Both groups were tested before and after eight weeks of the study period on balance in terms of single leg stance with eyes closed and strength in terms of the thirty second chair stand. The data of the study show that the participants of the training group perform significantly different higher scores in single leg stance with eyes closed and thirty second chair stand than the participants in the control group. The results of this study suggested that ten basic exercises of Muay Thai Chaiya training can use to improve balance and strength in male older adults.

Keywords: balance, strength, Muay Thai Chaiya, older adults

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Authors: Songul Kurucay, Mustafa Acarer, Harun Sepet

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Static strain aging occurs when metallic materials are subjected to deformation and then heat treated at low temperatures such as 150-200oC. Static strain aging occurs in BCC metals and results and increasing in yield and tensile strength and decreasing ductility due to carbon and/or nitrogen atoms locking dislocations. The locked dislocations increase yield and tensile strength. In this study, static strain aging behaviors of ferritic and austenitic stainless steel were investigated. Ferritic stainless steel was prestained at %5, %10 and %15 and then aged at 150oC and 200oC for 30 minutes. Austenitic stainless steel was also prestained at %20 and %30 and then heat treated at 200, 400 and 600oC for 30 minutes. After the heat treatment, the tensile test was performed to determine the effect of prestain and heat treatment on the steels. Hardness measurements and detailed microstructure characterization were also done. While AISI 430 ferritic stainless steel sample which was prestained at 15% and aged at 200oC, showed the highest increasing in the yield strength, AISI 304 austenitic stainless steel which was prestained at 30% and aged at 600oC, has the highest yield strength. Microstructure photographs also support the mechanical test results.

Keywords: austenitic stainless steel, ferritic stainless steel, static strain aging, tensile strength

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Authors: Nur Ilya Farhana Md Noh, Zakiah Ahmad

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Heat treatment on timbers is a process of applying heat to modify and equip the timbers with new improvised characteristics. It is environmental friendly compared to the common practice of treating timber by chemical preservatives. Malaysian hardwood timbers; Pauh Kijang and Kapur in green condition were heat treated at 150°C, 170°C, 190°C and 210°C in a specially design electronic furnace in one hour duration. The objectives were to determine the effect of heat treatment on bending strength properties of heat treated Pauh Kijang and Kapur in term of Modulus of Elasticity (MOE) and Modulus of Rupture (MOR) and to examine the significance changes at each temperature levels applied. Untreated samples for each species were used as a control sample. The results indicated that the bending strength properties for both species of timbers were affected by the heat exposure. Both MOE and MOR values for heat treated Pauh Kijang were increased when subjected to the specified temperature levels except at 210°C. The values were dropped compared to the control sample and sample treated at 190°C. Heat treated Kapur shows the same pattern of increment on its MOE and MOR values after exposure to heat at three temperature levels used and the values dropped at 210°C. However, differ to Pauh Kijang, even though there were decrement occurred at 210°C but the value is still higher compared to the control sample. The increments of MOE and MOR values are an indicator that heat treatment had successfully improvised the bending strength properties of these two species of hardwood timber. As the good strength of Malaysian timbers used as structural material is limited in numbers and expensive, heat treating timber with low strength properties is an alternative way to overcome this issue. Heat treatment is an alternative method need to be explored and made available in Malaysia as this country is still practicing chemical preservative treatment on the timbers.

Keywords: bending strength, hardwood timber, heat treatment, modulus of elasticity (MOE), modulus of rupture (MOR)

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Authors: P. Cavlan, B. Kırmızıgil

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Introduction: Each exercise in suspension exercises offer the use of gravity and body weight; and is thought to develop the equilibrium, flexibility and body stability necessary for daily life activities and sports, in addition to creating the correct functional force. Suspension exercises based on body weight focus the human body as an integrated system. Total Resistance Exercises (TRX) suspension training that physiotherapists, athletic health clinics, exercise centers of hospitals and chiropractic clinics now use for rehabilitation purposes. The purpose of this study is to investigate and compare the effects of TRX suspension exercises on physical performance in healthy individuals. Method: Healthy subjects divided into two groups; the study group and the control group with 40 individuals for each, between ages 20 to 45 with similar gender distributions. Study group had 2 sessions of suspension exercises per week for 8 weeks and control group had no exercises during this period. All the participants were given explosive strength, flexibility, strength and endurance tests before and after the 8 week period. The tests used for evaluation were respectively; standing long jump test and single leg (left and right) long jump tests, sit and reach test, sit up and back extension tests. Results: In the study group a statistically significant difference was found between prior- and final-tests in all evaluations, including explosive strength, flexibility, core strength and endurance of the group performing TRX exercises. These values were higher than the control groups’ values. The final test results were found to be statistically different between the study and control groups. Study group showed development in all values. Conclusions: In this study, which was conducted with the aim of investigating and comparing the effects of TRX suspension exercises on physical performance, the results of the prior-tests of both groups were similar. There was no significant difference between the prior and the final values in the control group. It was observed that in the study group, explosive strength, flexibility, strength, and endurance development was achieved after 8 weeks. According to these results, it was shown that TRX suspension exercise program improved explosive strength, flexibility, especially core strength and endurance; therefore the physical performance. Based on the results of our study, it was determined that the physical performance, an indispensable requirement of our life, was developed by the TRX suspension system. We concluded that TRX suspension exercises can be used to improve the explosive strength and flexibility in healthy individuals, as well as developing the muscle strength and endurance of the core region. The specific investigations could be done in this area so that programs that emphasize the TRX's physical performance features could be created.

Keywords: core strength, endurance, explosive strength, flexibility, physical performance, suspension exercises

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Authors: Babagana Mohammed, Abdulmuminu Mustapha Ali, Solomon Ibrahim, Buba Ahmad Umdagas

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This research investigated the possibility of replacing coarse and fine aggregates with waste road-asphalt (RWA), when sieved appropriately, in concrete production. Interlock pavement block is used widely in many parts of the world as modern day solution to outdoor flooring applications. The weight-percentage replacements of both coarse and fine aggregates with RWA at 0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80% and 90% respectively using a concrete mix ratio of 1:2:4 and water-to-cement ratio of 0.45 were carried out. The interlock block samples produced were then cured for 28days. Unconfined compressive strength (UCS) and the water absorption properties of the samples were then tested. Comparison of the results of the RWA-containing samples to those of the respective control samples shows significant benefits of using RWA in interlock block production. UCS results of RWA-containing samples compared well with those of the control samples and the RWA content also influenced the lowering of the water absorption of the samples. Overall, the research shows that it is possible to replace both coarse and fine aggregates with RWA materials when sieved appropriately, hence indicating that RWA could be recycled beneficially.

Keywords: aggregate, block-production, pavement, road-asphalt, use, waste

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Authors: Siddharth Vishwakarma, Danie Shajie A., Mishra H. N.

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Milk powder becomes very useful in the low milk supply area but the exact amount to add for one glass of milk and the handling is difficult. So, the idea of instant soluble milk tablet comes into existence for its high solubility and easy handling. The moisture content of milk tablets is increased by the direct addition of water with no additives for binding. The variation of the tensile strength of instant soluble milk tablets and the flowability of milk powder with the moisture content is analyzed and optimized for the highest tensile strength of instant soluble milk tablets and flowability, above a particular value of milk powder using response surface methodology. The flowability value is necessary for ease in quantifying the milk powder, as a feed, in the designed tablet making machine. The instant soluble nature of milk tablets purely depends upon the disintegration characteristic of tablets in water whose study is under progress. Conclusions: The optimization results are very useful in the commercialization of milk tablets.

Keywords: flowability, milk powder, response surface methodology, tablet making machine, tensile strength

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Authors: Rashini Maduka, C. R. Wijesinghe, A. R. Weerasinghe

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Drug-drug interactions (DDIs) can happen when two or more drugs are taken together. Today DDIs have become a serious health issue due to adverse drug effects. In vivo and in vitro methods for identifying DDIs are time-consuming and costly. Therefore, in-silico-based approaches are preferred in DDI identification. Most machine learning models for DDI prediction are used chemical and biological drug properties as features. However, some drug features are not available and costly to extract. Therefore, it is better to make automatic feature engineering. Furthermore, people who have diabetes already suffer from other diseases and take more than one medicine together. Then adverse drug effects may happen to diabetic patients and cause unpleasant reactions in the body. In this study, we present a model with a graph convolutional autoencoder and a graph decoder using a dataset from DrugBank version 5.1.3. The main objective of the model is to identify unknown interactions between antidiabetic drugs and the drugs taken by diabetic patients for other diseases. We considered automatic feature engineering and used Known DDIs only as the input for the model. Our model has achieved 0.86 in AUC and 0.86 in AP.

Keywords: drug-drug interaction prediction, graph embedding, graph convolutional networks, adverse drug effects

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Authors: Shank Kulkarni, Alireza Tabarraei

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The fantastic properties of polyurea such as flexibility, durability, and chemical resistance have brought it a wide range of application in various industries. Effective prediction of the response of polyurea under different loading and environmental conditions necessitates the development of an accurate constitutive model. Similar to most polymers, the behavior of polyurea depends on both strain and strain rate. Therefore, the constitutive model should be able to capture both these effects on the response of polyurea. To achieve this objective, in this paper, a nonlinear hyper-viscoelastic constitutive model is developed by the superposition of a hyperelastic and a viscoelastic model. The proposed constitutive model can capture the behavior of polyurea under compressive loading conditions at various strain rates. Four parameter Ogden model and Mooney Rivlin model are used to modeling the hyperelastic behavior of polyurea. The viscoelastic behavior is modeled using both a three-parameter standard linear solid (SLS) model and a K-BKZ model. Comparison of the modeling results with experiments shows that Odgen and SLS model can more accurately predict the behavior of polyurea. The material parameters of the model are found by curve fitting of the proposed model to the uniaxial compression test data. The proposed model can closely reproduce the stress-strain behavior of polyurea for strain rates up to 6500 /s.

Keywords: constitutive modelling, ogden model, polyurea, SLS model, uniaxial compression test

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Authors: Hasan Taherkhani

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The performance of asphalt pavements is highly dependent on the mechanical properties of asphaltic layers. Improving the mechanical properties of asphaltic mixtures by fiber reinforcement is a common method. Randomly distribution of fibers in the bituminous mixtures and placing between the particles develop reinforcing property in all directions in the mixture and improve their engineering properties. In this research, the effects of the nylon fiber length and content on some engineering properties of a typical binder course asphalt concrete have been investigated. The fibers at different contents of 0.3, 0.4 and 0.5% (by the weight of total mixture), each at three different lengths of 10, 25 and 40 mm have been used, and the properties of the mixtures, such as, volumetric properties, Marshall stability, flow, Marshall quotient, indirect tensile strength and moisture damage have been studied. It is found that the highest Marshall quotient is obtained by using 0.4% of 25mm long nylon fibers. The results also show that the indirect tensile strength and tensile strength ratio, which is an indication of moisture damage of asphalt concrete, decreases with increasing the length of fibers and fiber content.

Keywords: asphalt concrete, moisture damage, nylon fiber, tensile strength,

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Authors: Soo-yeon Seo, Su-jin Jung

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In recent years, the need for remodeling of many apartments built 30 years ago is increasing. Therefore, researches on the structural reinforcement technology of existing apartments have been conducted. On the other hand, there is a growing need for research on the existing underground space expansion technology to expand the parking space required for remodeling. When expanding an existing underground space, for earthworks, an earth retaining wall must be installed between the existing apartment building and it. In order to maximize the possible underground space, it is necessary to minimize the thickness of the portion of earth retaining wall and underground basement wall. In this manner, the calculation procedure is studied for the evaluation of shear strength of the composite basement wall corresponding to shear span-to-depth ratio in this study. As a result, it was shown that the proposed calculation procedure can be used to evaluate the shear strength of the composite basement wall as safe. On the other hand, when shear span-to-depth ratio is small, shear strength is very underestimated.

Keywords: underground space expansion, combined structure, temporary retaining wall, basement wall, shear connectors

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Authors: Ravija Gunawardana, Banuka Athuraliya

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Machine learning has emerged as a powerful tool for disease diagnosis and prediction. The use of machine learning algorithms has the potential to improve the accuracy of disease prediction, thereby enabling medical professionals to provide more effective and personalized treatments. This study focuses on developing a machine-learning model for disease prediction using symptoms and X-ray images. The importance of this study lies in its potential to assist medical professionals in accurately diagnosing diseases, thereby improving patient outcomes. Respiratory diseases are a significant cause of morbidity and mortality worldwide, and chest X-rays are commonly used in the diagnosis of these diseases. However, accurately interpreting X-ray images requires significant expertise and can be time-consuming, making it difficult to diagnose respiratory diseases in a timely manner. By incorporating machine learning algorithms, we can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The study utilized the Mask R-CNN algorithm, which is a state-of-the-art method for object detection and segmentation in images, to process chest X-ray images. The model was trained and tested on a large dataset of patient information, which included both symptom data and X-ray images. The performance of the model was evaluated using a range of metrics, including accuracy, precision, recall, and F1-score. The results showed that the model achieved an accuracy rate of over 90%, indicating that it was able to accurately detect and segment regions of interest in the X-ray images. In addition to X-ray images, the study also incorporated symptoms as input data for disease prediction. The study used three different classifiers, namely Random Forest, K-Nearest Neighbor and Support Vector Machine, to predict diseases based on symptoms. These classifiers were trained and tested using the same dataset of patient information as the X-ray model. The results showed promising accuracy rates for predicting diseases using symptoms, with the ensemble learning techniques significantly improving the accuracy of disease prediction. The study's findings indicate that the use of machine learning algorithms can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The model developed in this study has the potential to assist medical professionals in diagnosing respiratory diseases more accurately and efficiently. However, it is important to note that the accuracy of the model can be affected by several factors, including the quality of the X-ray images, the size of the dataset used for training, and the complexity of the disease being diagnosed. In conclusion, the study demonstrated the potential of machine learning algorithms for disease prediction using symptoms and X-ray images. The use of these algorithms can improve the accuracy of disease diagnosis, ultimately leading to better patient care. Further research is needed to validate the model's accuracy and effectiveness in a clinical setting and to expand its application to other diseases.

Keywords: K-nearest neighbor, mask R-CNN, random forest, support vector machine

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Authors: Neveen AlQasas, Daniel Johnson

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Membranes for water treatment are an established technology that attracts great attention due to its simplicity and cost effectiveness. However, membranes in operation suffer from the adverse effect of membrane fouling. Bio-fouling is a phenomenon that occurs at the water-membrane interface, and is a dynamic process that is initiated by the adsorption of dissolved organic material, including biomacromolecules, on the membrane surface. After initiation, attachment of microorganisms occurs, followed by biofilm growth. The biofilm blocks the pores of the membrane and consequently results in reducing the water flux. Moreover, the presence of a fouling layer can have a substantial impact on the membrane separation properties. Understanding the mechanism of the initiation phase of biofouling is a key point in eliminating the biofouling on membrane surfaces. The adhesion and attachment of different fouling materials is affected by the surface properties of the membrane materials. Therefore, surface properties of different polymeric materials had been studied in terms of their surface energies and Hansen solubility parameters (HSP). The difference between the combined HSP parameters (HSP distance) allows prediction of the affinity of two materials to each other. The possibilities of measuring the HSP of different polymer films via surface measurements, such as contact angle has been thoroughly investigated. Knowing the HSP of a membrane material and the HSP of a specific foulant, facilitate the estimation of the HSP distance between the two, and therefore the strength of attachment to the surface. Contact angle measurements using fourteen different solvents on five different polymeric films were carried out using the sessile drop method. Solvents were ranked as good or bad solvents using different ranking method and ranking was used to calculate the HSP of each polymeric film. Results clearly indicate the absence of a direct relation between contact angle values of each film and the HSP distance between each polymer film and the solvents used. Therefore, estimating HSP via contact angle alone is not sufficient. However, it was found if the surface tensions and viscosities of the used solvents are taken in to the account in the analysis of the contact angle values, a prediction of the HSP from contact angle measurements is possible. This was carried out via training of a neural network model. The trained neural network model has three inputs, contact angle value, surface tension and viscosity of solvent used. The model is able to predict the HSP distance between the used solvent and the tested polymer (material). The HSP distance prediction is further used to estimate the total and individual HSP parameters of each tested material. The results showed an accuracy of about 90% for all the five studied films

Keywords: surface characterization, hansen solubility parameter estimation, contact angle measurements, artificial neural network model, surface measurements

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Authors: Louis Ngai Yuen Wong, Varun Maruvanchery

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The Bukit Timah Granite, which is a major rock formation in Singapore, encompasses different rock types such as granite, adamellite, and granodiorite with various hybrid rocks. The present study focuses on the Central Singapore Granite found in the Mandai area. Even within this small aerial extent, lithological variations with respect to the composition, texture as well as the grain size have been recognized in this igneous body. Over the years, the research effort on the Bukit Timah Granite has been focused on achieving a better understanding of its engineering properties in association with civil engineering projects. To our best understanding, a few types of research attempted to systematically investigate the influence of grain size, mineral composition, texture etc. on the strength of Bukit Timah Granite rocks in a comprehensive manner. In typical local industry practices, the different lithological varieties are not differentiated, but all are grouped under Bukit Timah Granite during core logging and the subsequent determination of engineering properties. To address such a major gap in the local engineering geological practice, a preliminary study is conducted on the variations of uniaxial compressive strength (UCS) in seven distinctly different lithological varieties found in the Bukit Timah Granite. Other physical properties including Young’s modulus, P-wave velocity and dry density determined from laboratory testing will also be discussed. The study is supplemented by a petrographical thin section examination. In addition, the specimen failure mode is classified and further correlated with the lithological varieties by carefully observing the details of crack initiation, propagation and coalescence processes in the specimens undergoing loading tests using a high-speed camera. The outcome of this research, which is the first of its type in Singapore, will have a direct implication on the sampling and design practices in the field of civil engineering and particularly underground space development in Singapore.

Keywords: Bukit Timah Granite, lithological variety, thin section study, high speed video, failure mode

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Authors: G. Farkas, K. Mathis, J. Pilch, P. Minarik

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Deformation mechanisms in an Mg-Al-Ca alloy reinforced with short alumina fibres were studied by acoustic emission and in-situ neutron diffraction method. The fibres plane orientation with respect to the loading axis was found to be a key parameter, which influences the acting deformation processes, such as twinning or dislocation slip. In-situ neutron diffraction tests were measured at different temperatures from room temperature (RT) to 200°C. The measurement shows the lattice strain changes in the matrix and also in the reinforcement phase depending on macroscopic compressive deformation and stress. In case of parallel fibre plane orientation, the increment of compressive lattice strain is lower in the matrix and higher in the fibres in comparison to perpendicular fibre orientation. Furthermore, acoustic emission results indicate a larger twinning activity and more frequent fibre cracking in sample with perpendicular fibre plane orientation. Both types of mechanisms are more dominant at elevated temperatures.

Keywords: neutron diffraction, acoustic emission, magnesium based composite, deformation mechanisms

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Authors: Queen Suraajini Rajendran, Sai Hung Cheung

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Statistical down scaling models are required to connect the global climate model outputs and the local weather variables for climate change impact prediction. For reliable climate change impact studies, the uncertainty associated with the model including natural variability, uncertainty in the climate model(s), down scaling model, model inadequacy and in the predicted results should be quantified appropriately. In this work, a new approach is developed by the authors for statistical classification, statistical down scaling and uncertainty assessment and is applied to Singapore rainfall. It is a robust Bayesian uncertainty analysis methodology and tools based on coupling dependent modeling error with classification and statistical down scaling models in a way that the dependency among modeling errors will impact the results of both classification and statistical down scaling model calibration and uncertainty analysis for future prediction. Singapore data are considered here and the uncertainty and prediction results are obtained. From the results obtained, directions of research for improvement are briefly presented.

Keywords: statistical downscaling, global climate model, climate change, uncertainty

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Authors: Cristian Păuna

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After the widespread release of electronic trading, automated trading systems have become a significant part of the business intelligence system of any modern financial investment company. An important part of the trades is made completely automatically today by computers using mathematical algorithms. The trading decisions are taken almost instantly by logical models and the orders are sent by low-latency automatic systems. This paper will present a real-time price prediction methodology designed especially for algorithmic trading. Based on the price cyclicality function, the methodology revealed will generate price cyclicality bands to predict the optimal levels for the entries and exits. In order to automate the trading decisions, the cyclicality bands will generate automated trading signals. We have found that the model can be used with good results to predict the changes in market behavior. Using these predictions, the model can automatically adapt the trading signals in real-time to maximize the trading results. The paper will reveal the methodology to optimize and implement this model in automated trading systems. After tests, it is proved that this methodology can be applied with good efficiency in different timeframes. Real trading results will be also displayed and analyzed in order to qualify the methodology and to compare it with other models. As a conclusion, it was found that the price prediction model using the price cyclicality function is a reliable trading methodology for algorithmic trading in the financial market.

Keywords: algorithmic trading, automated trading systems, financial markets, high-frequency trading, price prediction

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Authors: Hatem Ghith

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This research paper experimentally investigates the effect of burning by fire flame from one face on the behavior and load carrying capacity for reinforced columns. Residual ultimate load carrying capacity, axial deformation, crack pattern and maximum crack width for column specimens with and without burning were recorded and discussed. Tested six reinforced concrete columns were divided into control specimen and two groups. The first group was exposed to a fire with a different temperature (300, 500, 700 °C) for an hour with reinforcement ratio 0.89% and the second group was exposed to a fire with a temperature 500 °C for an hour with different reinforcement ratio (0.89%, 2.18%, and 3.57%), then all columns were tested under short-term axial loading. From the obtained results, it could be concluded that the fire parameters significantly influence the fire resistance of R.C columns. The fire parameters cause axial deformation and moment on the column due to the eccentricity that generated from the difference in temperature and consequently the compressive stresses of both faces of the columns but the increased reinforcement ratio enhanced the resistance of columns for axial deformation and moment on the column due to the eccentricity.

Keywords: columns, reinforcement ratio, strength, time exposure

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Authors: Man Fung Ho, Lap So, Jiaqi Zhang, Yuheng Zhao, Huiyang Lu, Tat Shing Choi, K. Y. Michael Wong

Abstract:

Nowadays, a tremendous amount of data is available in the transportation system, enabling the development of various machine learning approaches to make short-term latency predictions. A natural question is then the choice of relevant information to enable accurate predictions. Using traffic data collected from the Taiwan Freeway System, we consider the prediction of short-term latency of a freeway segment with a length of 17 km covering 5 measurement points, each collecting vehicle-by-vehicle data through the electronic toll collection system. The processed data include the past latencies of the freeway segment with different time lags, the traffic conditions of the individual segments (the accumulations, the traffic fluxes, the entrance and exit rates), the total accumulations, and the weekday latency profiles obtained by Gaussian process regression of past data. We arrive at several important conclusions about how data should be refined to obtain accurate predictions, which have implications for future system-wide latency predictions. (1) We find that the prediction of median latency is much more accurate and meaningful than the prediction of average latency, as the latter is plagued by outliers. This is verified by machine-learning prediction using XGBoost that yields a 35% improvement in the mean square error of the 5-minute averaged latencies. (2) We find that the median latency of the segment 15 minutes ago is a very good baseline for performance comparison, and we have evidence that further improvement is achieved by machine learning approaches such as XGBoost and Long Short-Term Memory (LSTM). (3) By analyzing the feature importance score in XGBoost and calculating the mutual information between the inputs and the latencies to be predicted, we identify a sequence of inputs ranked in importance. It confirms that the past latencies are most informative of the predicted latencies, followed by the total accumulation, whereas inputs such as the entrance and exit rates are uninformative. It also confirms that the inputs are much less informative of the average latencies than the median latencies. (4) For predicting the latencies of segments composed of two or three sub-segments, summing up the predicted latencies of each sub-segment is more accurate than the one-step prediction of the whole segment, especially with the latency prediction of the downstream sub-segments trained to anticipate latencies several minutes ahead. The duration of the anticipation time is an increasing function of the traveling time of the upstream segment. The above findings have important implications to predicting the full set of latencies among the various locations in the freeway system.

Keywords: data refinement, machine learning, mutual information, short-term latency prediction

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Authors: Christian Gabriel Mariveles, Darelle Jay Gallardo, Leslie Dayaoen, Laurenz Paul Diaz

Abstract:

True experimental research with descriptive analysis was conducted. Utilizing Quicklime (Calcium Oxide) for self-healing properties of coconut husk fibre concrete brick. There are 2 setups established: the first one has the 1:1:2 ratio of calcium oxide, cement and sand, and the second one has a 2:1:2 ratio of the same variables. The bricks are made from the residences along Barangay Greater Lagro. The mixture of sand and cement is mixed with coconut husk fibers and then molded with different ratios in the molder. After the drying of cement, the researchers tested the bricks in the laboratory for compressive strength. The brick with the highest PSI is picked by the researchers to drop into freefall testing, and it makes remarkable remarks as it is deformed after dropping to different heights with a maximum of 20 feet. Unfortunately, the self-healing capabilities were not observed during the 12 weeks of monitoring. However, the brick was weighed after 12 weeks of monitoring, and it increased in weight by 0.030 kg. from 1.833 kg. to 1.863 kg. meaning that this ratio 2 has the potential to self-heal, but 12 weeks of monitoring by the researchers is not enough to conclude that it has a significant difference.

Keywords: self healing, coconut husk bricks, research, calcium oxide, utilizing quicklime

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Authors: Mostafa Rezaee Saraji, Ali Keshavarz Panahi

Abstract:

In this work, the processing steps for producing alumina parts using powder injection molding (PIM) technique and nano-powder were investigated and the thermal conductivity and flexural strength of samples were determined as a function of sintering temperature and holding time. In the first step, the feedstock with 58 vol. % of alumina nano-powder with average particle size of 100nm was prepared using Extrumixing method to obtain appropriate homogeneity. This feedstock was injection molded into the two cavity mold with rectangular shape. After injection molding step, thermal and solvent debinding methods were used for debinding of molded samples and then these debinded samples were sintered in different sintering temperatures and holding times. From the results, it was found that the flexural strength and thermal conductivity of samples increased by increasing sintering temperature and holding time; in sintering temperature of 1600ºC and holding time of 5h, the flexural strength and thermal conductivity of sintered samples reached to maximum values of 488MPa and 40.8 W/mK, respectively.

Keywords: alumina nano-powder, thermal conductivity, flexural strength, powder injection molding

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Authors: Marian Sorin Ionescu, Olivia Negoita, Cosmin Dobrin

Abstract:

This scientific communication reports and discusses learning models adaptable to modern business problems and models specific to digital concepts and paradigms. In the PAC (probably approximately correct) learning model approach, in which the learning process begins by receiving a batch of learning examples, the set of learning processes is used to acquire a hypothesis, and when the learning process is fully used, this hypothesis is used in the prediction of new operational examples. For complex business models, a lot of models should be introduced and evaluated to estimate the induced results so that the totality of the results are used to develop a predictive rule, which anticipates the choice of new models. In opposition, for online learning-type processes, there is no separation between the learning (training) and predictive phase. Every time a business model is approached, a test example is considered from the beginning until the prediction of the appearance of a model considered correct from the point of view of the business decision. After choosing choice a part of the business model, the label with the logical value "true" is known. Some of the business models are used as examples of learning (training), which helps to improve the prediction mechanisms for future business models.

Keywords: machine learning, business models, convex analysis, online learning

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Authors: Imad Eddine Charif, Wafaa Benchouk, Sidi Mohamed Mekelleche

Abstract:

The regioselectivity of a series of 16 1,3-dipolar cycloaddition reactions of nitrile oxides with 2(5H)-furanones has been analysed by means of global and local electrophilic and nucleophilic reactivity indices using density functional theory at the B3LYP level together with the 6-31G(d) basis set. The local electrophilicity and nucleophilicity indices, based on Fukui and Parr functions, have been calculated for the terminal sites, namely the C1 and O3 atoms of the 1,3-dipole and the C4 and C5 atoms of the dipolarophile. These local indices were calculated using both Mulliken and natural charges and spin densities. The results obtained show that the C5 atom of the 2(5H)-furanones is the most electrophilic site whereas the O3 atom of the nitrile oxides is the most nucleophilic centre. It turns out that the experimental regioselectivity is correctly reproduced, indicating that both Fukui- and Parr-based indices are efficient tools for the prediction of the regiochemistry of the studied reactions and could be used for the prediction of newly designed reactions of the same kind.

Keywords: 1, 3-dipolar cycloaddition, density functional theory, nitrile oxides, regioselectivity, reactivity indices

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