Search results for: compressive strength prediction

Authors: Hyun-Woo Cho

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Detection of incipient abnormal events in production processes is important to improve safety and reliability of manufacturing operations and reduce losses caused by failures. The construction of calibration models for predicting faulty conditions is quite essential in making decisions on when to perform preventive maintenance. This paper presents a multivariate calibration monitoring approach based on the statistical analysis of process measurement data. The calibration model is used to predict faulty conditions from historical reference data. This approach utilizes variable selection techniques, and the predictive performance of several prediction methods are evaluated using real data. The results shows that the calibration model based on supervised probabilistic model yielded best performance in this work. By adopting a proper variable selection scheme in calibration models, the prediction performance can be improved by excluding non-informative variables from their model building steps.

Keywords: calibration model, monitoring, quality improvement, feature selection

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Authors: Juliane P. De Oliveira, Carlos H. Dos Santos, Marcia Shoji, Maria E. C. Ferreira, Natalia U. Yamaguchi

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The construction sector is considered a major generator of environmental impacts due to the high consumption of natural resources and waste generation. Thus, this article aims to evaluate the performance of a concrete produced by the partial and total replacement of natural coarse aggregate by recycled coarse aggregate, derived from the concrete residue of buildings and demolitions. The study was made by comparing the compressive strength and absorption of three different concrete traces, keeping the water/cement factor of 0.60 and changing only the proportions of recycled coarse aggregate between 0%, 50% and 100%. Traces 50% and 100% obtained good results by comparing the actual specific mass, because the material used is lighter to the natural coarse aggregate. It was concluded that the concrete produced with recycled aggregates, even with inferior results, can be used where it is not needed a structural function, giving an adequate destination to the construction and demolition waste and consequently reducing the extraction and consumption of natural resources.

Keywords: green concrete, recycled aggregate, recycling, sustainable development

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Authors: R. Islam, Rafiqul A. Tarefder

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This study investigates the effect of moisture conditioning on the Indirect Tensile Strength (ITS) of asphalt concrete. As a first step, cylindrical samples of 100 mm diameter and 50 mm thick were prepared using a Superpave gyratory compactor. Next, the samples were conditioned using Moisture Induced Susceptibility Test (MIST) device at different numbers of moisture conditioning cycles. In the MIST device, samples are subjected water pressure through the sample pores cyclically. The MIST conditioned samples were tested for ITS. Results show that the ITS does not change significantly with MIST conditioning at the specific pressure and cycles adopted in this study.

Keywords: asphalt concrete, tensile strength, moisture, laboratory test

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Authors: Xiangtuo Chen, Paul-Henry Cournéde

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Crop yield prediction is a paramount issue in agriculture. The main idea of this paper is to find out efficient way to predict the yield of corn based meteorological records. The prediction models used in this paper can be classified into model-driven approaches and data-driven approaches, according to the different modeling methodologies. The model-driven approaches are based on crop mechanistic modeling. They describe crop growth in interaction with their environment as dynamical systems. But the calibration process of the dynamic system comes up with much difficulty, because it turns out to be a multidimensional non-convex optimization problem. An original contribution of this paper is to propose a statistical methodology, Multi-Scenarios Parameters Estimation (MSPE), for the parametrization of potentially complex mechanistic models from a new type of datasets (climatic data, final yield in many situations). It is tested with CORNFLO, a crop model for maize growth. On the other hand, the data-driven approach for yield prediction is free of the complex biophysical process. But it has some strict requirements about the dataset. A second contribution of the paper is the comparison of these model-driven methods with classical data-driven methods. For this purpose, we consider two classes of regression methods, methods derived from linear regression (Ridge and Lasso Regression, Principal Components Regression or Partial Least Squares Regression) and machine learning methods (Random Forest, k-Nearest Neighbor, Artificial Neural Network and SVM regression). The dataset consists of 720 records of corn yield at county scale provided by the United States Department of Agriculture (USDA) and the associated climatic data. A 5-folds cross-validation process and two accuracy metrics: root mean square error of prediction(RMSEP), mean absolute error of prediction(MAEP) were used to evaluate the crop prediction capacity. The results show that among the data-driven approaches, Random Forest is the most robust and generally achieves the best prediction error (MAEP 4.27%). It also outperforms our model-driven approach (MAEP 6.11%). However, the method to calibrate the mechanistic model from dataset easy to access offers several side-perspectives. The mechanistic model can potentially help to underline the stresses suffered by the crop or to identify the biological parameters of interest for breeding purposes. For this reason, an interesting perspective is to combine these two types of approaches.

Keywords: crop yield prediction, crop model, sensitivity analysis, paramater estimation, particle swarm optimization, random forest

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Authors: Yang Zhou, Heli Sun, Jianbin Huang, Jizhong Zhao, Shaojie Qiao

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Traffic prediction is a fundamental problem in urban environment, facilitating the smart management of various businesses, such as taxi dispatching, bike relocation, and stampede alert. Most earlier methods rely on identifying the intrinsic spatial-temporal correlation to forecast. However, the complex nature of this problem entails a more sophisticated solution that can simultaneously capture the mutual influence of both adjacent and far-flung areas, with the information of time-dimension also incorporated seamlessly. To tackle this difficulty, we propose a new multi-phase architecture, DSTDS (Dual Spatial-Temporal Data Scheme for traffic prediction), that aims to reveal the underlying relationship that determines future traffic trend. First, a graph-based neural network with an attention mechanism is devised to obtain the static features of the road network. Then, a multi-granularity recurrent neural network is built in conjunction with the knowledge from a grid-based model. Subsequently, the preceding output is fed into a spatial-temporal super-resolution module. With this 3-phase structure, we carry out extensive experiments on several real-world datasets to demonstrate the effectiveness of our approach, which surpasses several state-of-the-art methods.

Keywords: traffic prediction, spatial-temporal, recurrent neural network, dual data scheme

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Authors: Volkmar Nolting

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Fatigue crack growth prediction has become an important topic in both engineering and non-destructive evaluation. Crack propagation is influenced by the mechanical properties of the material and is conveniently modelled by the Paris-Erdogan equation. The critical crack size and the total number of load cycles are calculated. From a Larson-Miller plot the maximum operational temperature can for a given stress level be determined so that failure does not occur within a given time interval t. The study is used to determine a reasonable inspection cycle and thus enhances operational safety and reduces costs.

Keywords: fracturemechanics, crack growth prediction, lifetime of a component, structural health monitoring

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Authors: Armin Najipour, A. M. Fattahi

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The aim of this research was to investigate the effect of the addition of multi walled carbon nanotubes on the mechanical properties of polyethylene/carbon nanotube nanocomposites. To do so, polyethylene and carbon nanotube were mixed in different weight percentages containing 0, 0.5, 1, and 1.5% carbon nanotube in two screw extruder apparatus by fusion. Then the nanocomposite samples were molded in injection apparatus according to ASTM:D638 standard. The effects of carbon nanotube addition in 4 different levels on the tensile strength, elastic modulus and elongation of the nanocomposite samples were investigated. The results showed that the addition of carbon nanotube had a significant effect on improving tensile strength of the nanocomposite samples such that by adding 1% w/w carbon nanotube, the tensile strength 23.4%,elastic modulus 60.4%and elongation 29.7% of the samples improved. Also, according to the results, Manera approximation model at percentages about 0.5% weight and modified Halpin-Tsai at percentages about 1% weight lead to favorite and reliable results.

Keywords: carbon nanotube, injection molding, Mechanical properties, Nanocomposite, polyethylene

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Authors: S. Adjiri-Bailiche, S. M. Boudia, H. Daaou, S. Hadouche, A. Benzaoui

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In this work the study of changes in the wind speed depending on the altitude is calculated and described by the model of the neural networks, the use of measured data, the speed and direction of wind, temperature and the humidity at 10 m are used as input data and as data targets at 50m above sea level. Comparing predict wind speeds and extrapolated at 50 m above sea level is performed. The results show that the prediction by the method of artificial neural networks is very accurate.

Keywords: MATLAB, neural network, power low, vertical extrapolation, wind energy, wind speed

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Authors: Sijing Xiong, Ran Su, Lit-Hsin Loo, Daniele Zink

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The kidney is a major target for toxic effects of drugs, industrial and environmental chemicals and other compounds. Typically, nephrotoxicity is detected late during drug development, and regulatory animal models could not solve this problem. Validated or accepted in silico or in vitro methods for the prediction of nephrotoxicity are not available. We have established the first and currently only pre-validated in vitro models for the accurate prediction of nephrotoxicity in humans and the first predictive platforms based on renal cells derived from human pluripotent stem cells. In order to further improve the efficiency of our predictive models, we recently developed a high content screening (HCS) platform. This platform employed automated imaging in combination with automated quantitative phenotypic profiling and machine learning methods. 129 image-based phenotypic features were analyzed with respect to their predictive performance in combination with 44 compounds with different chemical structures that included drugs, environmental and industrial chemicals and herbal and fungal compounds. The nephrotoxicity of these compounds in humans is well characterized. A combination of chromatin and cytoskeletal features resulted in high predictivity with respect to nephrotoxicity in humans. Test balanced accuracies of 82% or 89% were obtained with human primary or immortalized renal proximal tubular cells, respectively. Furthermore, our results revealed that a DNA damage response is commonly induced by different PTC-toxicants with diverse chemical structures and injury mechanisms. Together, the results show that the automated HCS platform allows efficient and accurate nephrotoxicity prediction for compounds with diverse chemical structures.

Keywords: high content screening, in vitro models, nephrotoxicity, toxicity prediction

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Authors: Wei-Shan Wang, Klaus Vogel, Felix Gabler, Maik Wiemer, Thomas Gessner

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Metallic glasses, which are free of grain boundaries, have superior properties including large elastic limits, high strength, and excellent wear and corrosion resistance. Therefore, bulk metallic glasses (BMG) and thin film metallic glasses (TFMG) have been widely developed and investigated. Among various kinds of metallic glasses, Pd-Cu-Si TFMG, which has lower elastic modulus and better resistance of oxidation and corrosions compared to Zr- and Fe-based TFMGs, can be a promising candidate for MEMS applications. However, the study of Pd-TFMG membrane is still limited. This paper presents free-standing Pd-based metallic glass membranes with large area fabricated on wafer level for the first time. Properties of Pd-Cu-Si thin film metallic glass (TFMG) with various deposition parameters are investigated first. When deposited at 25°C, compressive stress occurs in the Pd76Cu6Si18 thin film regardless of Ar pressure. When substrate temperature is increased to 275°C, the stress state changes from compressive to tensile. Thin film stresses are slightly decreased when Ar pressure is higher. To show the influence of temperature on Pd-TFMGs, thin films without and with post annealing below (275°C) and within (370°C) supercooled liquid region are investigated. Results of XRD and TEM analysis indicate that Pd-TFMGs remain amorphous structure with well-controlled parameters. After verification of amorphous structure of the Pd-TFMGs, free-standing Pd-Cu-Si membranes were fabricated by depositing Pd-Cu-Si thin films directly on 200nm-thick silicon nitride membranes, followed by post annealing and dry etching of silicon nitride layer. Post annealing before SiNx removal is used to further release internal stress of Pd-TFMGs. The edge length of the square membrane ranges from 5 to 8mm. The effect of post annealing on Pd-Cu-Si membranes are discussed as well. With annealing at 370°C for 5 min, Pd-MG membranes are fully distortion-free after removal of SiNx layer. Results show that, by introducing annealing process, the stress-relief, distortion-free Pd-TFMG membranes with large area can be a promising candidate for sensing applications such as pressure and gas sensors.

Keywords: amorphous alloy, annealing, metallic glasses, TFMG membrane

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Authors: Yingkun Huang, Li Guo, Zekang Lan, Kai Tian

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Hard disk drives (HDD) failure of the exascale supercomputing system may lead to service interruption and invalidate previous calculations, and it will cause permanent data loss. Therefore, initiating corrective actions before hard drive failures materialize is critical to the continued operation of jobs. In this paper, a highly accurate analysis model based on CNN-LSTM and oversampling technique was proposed, which can correctly predict the necessity of a disk replacement even ten days in advance. Generally, the learning-based method performs poorly on a training dataset with long-tail distribution, especially fault prediction is a very classic situation as the scarcity of failure data. To overcome the puzzle, a new oversampling was employed to augment the data, and then, an improved CNN-LSTM with the shortcut was built to learn more effective features. The shortcut transmits the results of the previous layer of CNN and is used as the input of the LSTM model after weighted fusion with the output of the next layer. Finally, a detailed, empirical comparison of 6 prediction methods is presented and discussed on a public dataset for evaluation. The experiments indicate that the proposed method predicts disk failure with 0.91 Precision, 0.91 Recall, 0.91 F-measure, and 0.90 MCC for 10 days prediction horizon. Thus, the proposed algorithm is an efficient algorithm for predicting HDD failure in supercomputing.

Keywords: HDD replacement, failure, CNN-LSTM, oversampling, prediction

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Authors: Waris Piyaphon, Sathaphorn O-Suwankul, Kittima Bootdee, Manit Nithitanakul

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Starch from rice was used as a filler in low density polyethylene in preparation of low density polyethylene/rice starch composite. This study aims to prepare LDPE/rice starch composites. Glycerol (GC) was used as a plasticizer in order to increase dispersion and reduce agglomeration of rice starch in low density polyethylene (LDPE) matrix. Low density polyethylene grafted maleic anhydride (LDPE-g-MA) was used as a compatibilizer to increase the compatibility between LDPE and rice starch. The content of rice starch was varied between 10, 20, and 30 %wt. Results indicated that increase of rice starch content reduced tensile strength at break, elongation, and impact strength of composites. LDPE-g-MA showed positive effect on mechanical properties which increased in tensile strength and impact properties as well as compatibility between rice starch and LDPE matrix. Moreover, the addition of LDPE-g-MA significantly improved the impact strength by 50% compared to neat composite. The incorporation of GC enhanced the processability of composite. Introduction of GC affected the viscosity after blending by reducing the viscosity at all shear rate. The presence of plasticizer increased the impact strength but decreased the stiffness of composite. Water absorption of the composite was increased when plasticizer was added.

Keywords: composite material, plastic starch composite, polyethylene composite, PE grafted maleic anhydride

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Authors: Ichihan Tai, Bill Olson, Paul Blessner

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Traditional early warning systems that alarm against crisis are generally based on structured or numerical data; therefore, a system that can make predictions based on unstructured textual data, an uncorrelated data source, is a great complement to the traditional early warning systems. The Chicago Board Options Exchange (CBOE) Volatility Index (VIX), commonly referred to as the fear index, measures the cost of insurance against market crash, and spikes in the event of crisis. In this study, news data is consumed for prediction of whether there will be a market-wide crisis by predicting the movement of the fear index, and the historical references to similar events are presented in an unsupervised manner. Topic modeling-based prediction and representation are made based on daily news data between 1990 and 2015 from The Wall Street Journal against VIX index data from CBOE.

Keywords: early warning system, knowledge management, market prediction, topic modeling.

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Authors: Rodrigo S. Fonseca, Antônio C. P. Veiga

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Aiming at helping people with some movement limitation that makes typing and communication difficult, there is a need to customize an assistive tool with a learning environment that helps the user in order to optimize text input, identifying the error and providing the correction and possibilities of choice in the Portuguese language. The work presents an Orthographic and Grammatical System that can be incorporated into writing environments, improving and facilitating the use of an alphanumeric keyboard, using a prototype built using a genetic algorithm in addition to carrying out the prediction, which can occur based on the quantity and position of the inserted letters and even placement in the sentence, ensuring the sequence of ideas using a Long Short Term Memory (LSTM) neural network. The prototype optimizes data entry, being a component of assistive technology for the textual formulation, detecting errors, seeking solutions and informing the user of accurate predictions quickly and effectively through machine learning.

Keywords: genetic algorithm, neural networks, word prediction, machine learning

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Authors: Siqi Lin, Yangang Zhao

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Concrete-filled steel tube column achieves the excellent performance of high strength, stiffness, and ductility due to the confinement from the steel tube. Well understanding the stress of the steel tube is important to make clear the confinement effect. In this paper, the ultimate stress of the steel tube in circular concrete-filled steel tube columns subjected to axial compression was studied. Experimental tests were conducted to investigate the effects of the parameters, including concrete strength, steel strength, and D/t ratio, on the ultimate stress of the steel tube. The stress of the steel tube was determined by employing the Prandtl-Reuss flow rule associated with isotropic strain hardening. Results indicate that the stress of steel tube was influenced by the parameters. Specimen with higher strength ratio fy/fc and smaller D/t ratio generally leads to a higher utilization efficiency of the steel tube.

Keywords: concrete-filled steel tube, axial compression, ultimate stress, utilization efficiency

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Authors: Kassaye Balkew Workagegn, Gunnar Klemetsdal, Hans Magnus Gjøen

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In this study, the first objective was to investigate whether non-lethal or non-invasive methods, utilizing body measurements, could be used to efficiently predict fillet weight and fillet yield for a complete diallel cross of three Nile tilapia (Oreochromis niloticus) strains collected from three Ethiopian Rift Valley lakes, Lakes Ziway, Koka and Chamo. The second objective was to estimate heritability of body weight, actual and predicted fillet traits, as well as genetic correlations between these traits. A third goal was to estimate additive, reciprocal, and heterosis effects for body weight and the various fillet traits. As in females, early sexual maturation was widespread, only 958 male fish from 81 full-sib families were used, both for the prediction of fillet traits and in genetic analysis. The prediction equations from body measurements were established by forward regression analysis, choosing models with the least predicted residual error sums of squares (PRESS). The results revealed that body measurements on live Nile tilapia is well suited to predict fillet weight but not fillet yield (R²= 0.945 and 0.209, respectively), but both models were seemingly unbiased. The genetic analyses were carried out with bivariate, multibreed models. Body weight, fillet weight, and predicted fillet weight were all estimated with a heritability ranged from 0.23 to 0.28, and with genetic correlations close to one. Contrary, fillet yield was only to a minor degree heritable (0.05), while predicted fillet yield obtained a heritability of 0.19, being a resultant of two body weight variables known to have high heritability. The latter trait was estimated with genetic correlations to body weight and fillet weight traits larger than 0.82. No significant differences among strains were found for their additive genetic, reciprocal, or heterosis effects, while total heterosis effects were estimated as positive and significant (P < 0.05). As a conclusion, prediction of prediction of fillet weight based on body measurements is possible, but not for fillet yield.

Keywords: additive, fillet traits, genetic correlation, heritability, heterosis, prediction, reciprocal

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Authors: Dattaji K. Shinde, Ajit D. Kelkar

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Interlaminar shear strength (ILSS) of fiber reinforced polymer composite is an important property for most of the structural applications. Matrix modification is an effective method used to improve the interlaminar shear strength of composite. In this paper, EPON 862/w epoxy system was modified using Tetraethyl orthosilicate (TEOS) electrospun nanofibers (ENFs) which were produced using electrospinning method. Unmodified and nanofibers modified resins were used to fabricate glass fiber reinforced polymer composite (GFRP) using H-VARTM method. The ILSS of the Glass Fiber Reinforced Polymeric Composites (GFRP) was investigated. The study shows that introduction of TEOS ENFs in the epoxy resin enhanced the ILSS of GFRPby 15% with 0.6% wt. fraction of TEOS ENFs.

Keywords: electrospun nanofibers, H-VARTM, interlaminar shear strength, matrix modification

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Authors: Gloria P. Manu, Johnson K. Efavi, Abu Yaya, Grace K. Arkorful, Frank Godson

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Plastics have been used extensively in both food and water packaging and other applications because of their inherent properties of low bulk densities and inertness as well as its low cost. Waste management of these plastics after usage is troubling in Ghana. One way of addressing the environmental problems associated with these plastic wastes is by recycling into useful products such as composites for energy and construction applications using natural or local materials as reinforcement. In this work, composites have been formed from waste low-density polyethylene (LDPE) and kaolin at temperatures as low as 70 ֯C using low-cost solvents like kerosene. Chemical surface modifications have been employed to improve the interfacial bonding resulting in the enhancement of properties of the composites. Kaolin particles of sizes ≤ 90µm were dispersed in the polyethylene matrix. The content of the LDPE was varied between 10, 20, 30, 40, 50, 60, and 70 %wt. Results obtained indicated that all the composites exhibited impressive compressive and flexural strengths with the 50%wt. composition having the highest strength. The hardness value of the composites increased as the polyethylene composition reduces and that of the kaolin increased. The average density and water of absorption of the composites were 530kg/m³ and 1.3% respectively.

Keywords: polyethylene, recycling, waste, composite, kaolin

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Authors: Aswin Kumar Krishnan, Yat Choy Wong, Reiza Mukhlis, Zipeng Zhang, Arul Arulrajah

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The continuous increase in vehicle uptake escalates the number of rubber tyre waste which need to be managed to avoid landfilling and stockpiling. The present research focused on the sustainable use of rubber crumbs in clay roof tiles. The properties of roof tiles composed of clay, rubber crumbs, NaOH, and Na₂SiO₃ with a 10% alkaline activator were studied. Tile samples were fabricated by heating the compacted mixtures at 50°C for 72 hours, followed by a higher heating temperature of 200°C for 24 hours. The effect of rubber crumbs aggregates as a substitution for the raw clay materials was investigated by varying their concentration from 0% to 2.5%. X-ray diffraction (XRD) and scanning electron microscopy (SEM) analyses have been conducted to study the phases and microstructures of the samples. It was found that the optimum rubber crumbs concentration was at 0.5% and 1%, while cracks and larger porosity were found at higher crumbs concentrations. Water absorption and compressive strength test results demonstrated that rubber crumbs and clay satisfied the standard requirement for the roof tiles.

Keywords: rubber crumbs, clay, roof tiles, alkaline activators

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Authors: Sukron Makmun, Topan Firmandha, Siswanto

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Ultimate Strength Assessment on ship cross section in accordance with Biro Klasifikasi Indonesia (BKI) Rules for Hull, follows step by step incremental iterative approach. In this approach, ship cross section is divided into plate-stiffener combinations and hard corners element. The average stress-strain relationship (σ-ε) for all structural elements will be defined, where the subscript k refers to the modes 0, 1, 2, 3 or 4. These results would be verified with a commercial software calculation in similar cases. The numerical calculations of buckling strength are in accordance with the commercial software (GL Rules ND). Then the comparison of failure behaviours of stiffened panels and hard corners are presented. Where failure modes 3 are likely to occur first follows the failure mode 4 and the last one is the failure mode 1.

Keywords: ultimate strength assessment, BKI rules, incremental, plate-stiffener combination and hard corner, commercial software

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Authors: Sarka Keprdova, Jiri Bydzovsky

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This article describes to what extent the addition of energy by-products into the structures of the technical hemp filling materials influence their properties. The article focuses on the changes in physical-mechanical and thermal technical properties of materials after the addition of ash or FBC ash or slag in the binding component of material. Technical hemp filling materials are made of technical hemp shives bonded by the mixture of cement and dry hydrate lime. They are applicable as fillers of vertical or horizontal structures or roofs. The research used eight types of energy by-products of power or heating plants in the Czech Republic. Secondary energy products were dispensed in three different percentage ratios as a replacement of cement in the binding component. Density, compressive strength and determination of the coefficient of thermal conductivity after 28, 60 and 90 days of curing in a laboratory environment were determined and subsequently evaluated on the specimens produced.

Keywords: ash, binder, cement, energy by-product, FBC ash (fluidized bed combustion ash), filling materials, shives, slag, technical hemp

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Authors: R. Ziaie Moayed, H. Keshavarz Hedayati

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Over recent years, due to increased population and increased waste production, groundwater protection has become more important, therefore, designing engineered barrier systems such as landfill liners to prevent the entry of leachate into groundwater should be done with greater accuracy. These measures generally involve the application of low permeability soils such as clays. Bentonite is a natural clay with low permeability which makes it a suitable soil for using in liners. Also zeolite with high cation exchange capacity can help to reduce of hazardous materials risk. Bentonite expands when wet, absorbing as much as several times its dry mass in water. This property may effect on some structural properties of soil such as shear strength. In present study, shear strength parameters are determined by both leachates polluted and not polluted bentonite-amended zeolite soil with mixing rates (B/Z) of 5%-10% and 20% with unconfined compression test to obtain the differences. It is shown that leachate presence causes reduction in resistance in general.

Keywords: bentonite, leachate, shear strength parameters, unconfined compression test

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Authors: Aymen Arfaoui, Abdelkader Soumaya

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The Messalla-Ressas-Bou Kornine (MRB) and Hammamet Korbous (HK) major trending North-South fault zones provide a good opportunity to show the effects of the Eocene compressive phase in northern Tunisia. They acted as paleogeographical boundaries during the Mesozoic and belonged to a significant strike-slip corridor called the «North-South Axis,» extending from the Saharan platform at the South to the Gulf of Tunis at the North. Our study area is situated in a relay zone between two significant strike-slip faults (HK and MRB), separating the Atlas domain from the Pelagian Block. We used a multidisciplinary approach, including fieldwork, stress inversion, and geophysical profiles, to argue the shortening event that affected the study region. The MRB and HK contractional duplex is a privileged area for a local stress field and stress nucleation. The stress inversion of fault slip data reveals an Eocene compression with NW-SE trending SHmax, reactivating most of the ancient Mesozoic normal faults in the region. This shortening phase is represented in the MRB belt by an angular unconformity between the Upper Eocene over various Cretaceous strata. The stress inversion data reveal a compressive tectonic with an average NW-SE trending Shmax. The major N-S faults are reactivated under this shortening as sinistral oblique faults. The orientation of SHmax deviates from NW-SE to E-W near the preexisting deep faults of MRB and HK. This E-W stress direction generated the emerging overlap of Ressas-Messella and blind thrust faults in the Cretaceous deposits. The connection of the sub-meridian reverse faults in depth creates "flower structures" under an E-W local compressive stress. In addition, we detected a reorientation of the SHmax into an N-S direction in the central part of the MRB - HK contractional duplex, creating E-W reverse faults and overlapping zones. Finally, the Eocene compression constituted the first major tectonic phase which inverted the Mesozoic preexisting extensive fault system in Northern Tunisia.

Keywords: Tunisia, eocene compression, tectonic stress field, Bou Kornine-Ressas-Messella

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Authors: S. Chandrakaran, G. Govind

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Stone columns are effectively used to improve bearing strength of soils and also for many geotechnical applications. In soft soils when stone columns are loaded they undergo large settlements due to insufficient lateral confinement. Use of geosynthetics encasement has proved to be a solution for this problem. In this paper, results of a laboratory experimental study carried out with model stone columns with and without encasement. Sand was used for making test beds, and grain size of soil varies from 0.075mm to 4.75mm. Woven geotextiles produced by Gareware ropes India with mass per unit area of 240gm/M2 and having tensile strength of 52KN/m is used for the present investigation. Tests were performed with large scale direct shear box and also using scaled laboratory plate load tests. Stone column of 50mm and 75mm is used for the present investigation. Diameter of stone column, size of stones used for making stone columns is varied in making stone column in the present study. Two types of stone were used namely small and bigger in size. Results indicate that there is an increase in angle of internal friction and also an increase in the shear strength of soil when stone columns are encased. With stone columns with 50mm dia, an average increase of 7% in shear strength and 4.6 % in angle of internal friction was achieved. When large stones were used increase in the shear strength was 12.2%, and angle of internal friction was increased to 5.4%. When the stone column diameter has increased to 75mm increase in shear strength and angle of internal friction was increased with smaller size of stones to 7.9 and 7.5%, and with large size stones, it was 7.7 and 5.48% respectively. Similar results are obtained in plate load tests, also.

Keywords: stone columns, encasement, shear strength, plate load test

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Authors: Nataša Kalajdžija, Strahinja Kovačević, Davor Lončar, Sanja Podunavac Kuzmanović, Lidija Jevrić

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In order to investigate the relationship between retention and structure, a quantitative Structure Retention Relationships (QSRRs) study was applied for the prediction of retention times of a set of 23 secoestrane derivatives in a reversed-phase thin-layer chromatography. After the calculation of molecular descriptors, a suitable set of molecular descriptors was selected by using step-wise multiple linear regressions. Artificial Neural Network (ANN) method was employed to model the nonlinear structure-activity relationships. The ANN technique resulted in 5-6-1 ANN model with the correlation coefficient of 0.98. We found that the following descriptors: Critical pressure, total energy, protease inhibition, distribution coefficient (LogD) and parameter of lipophilicity (miLogP) have a significant effect on the retention times. The prediction results are in very good agreement with the experimental ones. This approach provided a new and effective method for predicting the chromatographic retention index for the secoestrane derivatives investigated.

Keywords: lipophilicity, QSRR, RP TLC retention, secoestranes

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Authors: Donna-Marie Phyllis Lanton

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Sarcopenia is a well-known geriatric syndrome characterized by the progressive and generalized loss of muscle quantity or quality. The incidence of sarcopenia increases with age and is associated with adverse outcomes such as the increased risk of falls, cognitive impairment, loss of independence, diminished quality of life, increased health costs, need for care in a skilled nursing facility, and increased mortality. Physical activity, including resistance training, is the most prevalent recommendation for treating and preventing sarcopenia. Residents (N = 23) of a skilled nursing facility in East Orlando, Florida, participated in a 90-day strength training program designed using the PARIHS framework to improve measures of muscle mass, muscle strength, physical performance, and quality of life. Residents engaged in both resistance and balance exercises for 1 hour two times a week. Baseline data were collected and compared to data at Days 30, 60, and 90. T tests indicated significant gains on all measures from baseline to 90 days: muscle mass increased by 1.2 (t[22] = 2.85, p = .009), grip strength increased by 4.02 (t[22] = 8.15, p < .001), balance increased by 2.13 (t[22] = 18.64, p < .001), gait speed increased by 1.83 (t[22] = 17.84, p < .001), chair speed increased 1.87 (t[22] = 16.36, p < .001), and quality of life score increased by 17.5 (t[22] = 19.26, p < .001). For residents with sarcopenia in skilled nursing facilities, a 90-day strength training program with resistance and balance exercises may provide an option for decreasing the incidence of sarcopenia among that population

Keywords: muscle mass, muscle strength, older adults, PARIHS framework

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Authors: Firas Daghistani, Hossam Abuel Naga

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Waste tyres is a global problem that has a negative effect on the environment, where there are approximately one billion waste tyres discarded worldwide yearly. Waste tyres are discarded in stockpiles, where they provide harm to the environment in many ways. Finding applications to these materials can help in reducing this global problem. One of these applications is recycling these waste materials and using them in geotechnical engineering. Recycled waste tyre particulates can be mixed with sand to form a lightweight material with varying shear strength characteristics. Contradicting results were found in the literature on the inclusion of particulate rubber to sand, where some experiments found that the inclusion of particulate rubber can increase the shear strength of the mixture, while other experiments stated that the addition of particulate rubber decreases the shear strength of the mixture. This research further investigates the inclusion of particulate rubber to sand and whether it can increase or decrease the shear strength characteristics of the mixture. For the experiment, a series of direct shear tests were performed on a poorly graded sand with a mean particle size of 0.32 mm mixed with recycled poorly graded particulate rubber with a mean particle size of 0.51 mm. The shear tests were performedon four normal stresses 30, 55, 105, 200 kPa at a shear rate of 1 mm/minute. Different percentages ofparticulate rubber content were used in the mixture i.e., 10%, 20%, 30% and 50% of sand dry weight at three density states, namely loose, slight dense, and dense state. The size ratio of the mixture,which is the mean particle size of the particulate rubber divided by the mean particle size of the sand, was 1.59. The results identified multiple parameters that can influence the shear strength of the mixture. The parameters were: normal stress, particulate rubber content, mixture gradation, mixture size ratio, and the mixture’s density. The inclusion of particulate rubber tosand showed a decrease to the internal friction angle and an increase to the apparent cohesion. Overall, the inclusion of particulate rubber did not have a significant influenceon the shear strength of the mixture. For all the dense states at the low normal stresses 33 and 55 kPa, the inclusion of particulate rubber showed aslight increase in the shear strength where the peak was at 20% rubber content of the sand’s dry weight. On the other hand, at the high normal stresses 105, and 200 kPa, there was a slight decrease in the shear strength.

Keywords: shear strength, direct shear, sand-rubber mixture, waste material, granular material

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Authors: A. Senthil Kumar, V. Murali Bhaskaran

Abstract:

In the information technology ground, people are using various tools and software for their official use and personal reasons. Nowadays, people are worrying to choose data accessing and extraction tools at the time of buying and selling their products. In addition, worry about various quality factors such as price, durability, color, size, and availability of the product. The main purpose of the research study is to find solutions to these unsolved existing problems. The proposed algorithm is a Multidirectional Rank Prediction (MDRP) decision making algorithm in order to take an effective strategic decision at all the levels of data extraction, uses a real time textile dataset and analyzes the results. Finally, the results are obtained and compared with the existing measurement methods such as PCC, SLCF, and VSS. The result accuracy is higher than the existing rank prediction methods.

Keywords: Knowledge Discovery in Database (KDD), Multidirectional Rank Prediction (MDRP), Pearson’s Correlation Coefficient (PCC), VSS (Vector Space Similarity)

Procedia PDF Downloads 286

Authors: Henok Hailemariam, Frank Wuttke

Abstract:

Collapsible soils are weak soils that appear to be stable in their natural state, normally dry condition, but rapidly deform under saturation (wetting), thus generating large and unexpected settlements which often yield disastrous consequences for structures unwittingly built on such deposits. In this study, a prediction model for the relative subsidence of stressed collapsible soils based on dielectric permittivity measurement is presented. Unlike most existing methods for soil subsidence prediction, this model does not require moisture content as an input parameter, thus providing the opportunity to obtain accurate estimation of the relative subsidence of collapsible soils using dielectric measurement only. The prediction model is developed based on an existing relative subsidence prediction model (which is dependent on soil moisture condition) and an advanced theoretical frequency and temperature-dependent electromagnetic mixing equation (which effectively removes the moisture content dependence of the original relative subsidence prediction model). For large scale sub-surface soil exploration purposes, the spatial sub-surface soil dielectric data over wide areas and high depths of weak (collapsible) soil deposits can be obtained using non-destructive high frequency electromagnetic (HF-EM) measurement techniques such as ground penetrating radar (GPR). For laboratory or small scale in-situ measurements, techniques such as an open-ended coaxial line with widely applicable time domain reflectometry (TDR) or vector network analysers (VNAs) are usually employed to obtain the soil dielectric data. By using soil dielectric data obtained from small or large scale non-destructive HF-EM investigations, the new model can effectively predict the relative subsidence of weak soils without the need to extract samples for moisture content measurement. Some of the resulting benefits are the preservation of the undisturbed nature of the soil as well as a reduction in the investigation costs and analysis time in the identification of weak (problematic) soils. The accuracy of prediction of the presented model is assessed by conducting relative subsidence tests on a collapsible soil at various initial soil conditions and a good match between the model prediction and experimental results is obtained.

Keywords: collapsible soil, dielectric permittivity, moisture content, relative subsidence

Procedia PDF Downloads 363

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

Abstract:

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

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

Procedia PDF Downloads 214