Search results for: strength prediction.

Authors: Rahmat A, Radin Rafeeuddin R. D., Norasrudin S., Mastura M.

Abstract:

The objective of the study is to investigate the effect of a footballer-s postural on selected physical fitness components. Twenty-one (21) subjects of the university male footballers under the Sport Excellence Center programme were photographed using qualitative analysis. The postural variables were stratified manually into normal and anomalies group and their flexibility, strength and SAQ performance were compared using the Mann-Whitney Test. The AROM assessment and SAQ test reported no significance difference (Z=-.398, p=0.711, p>0.05), similar to the lower body strength was shown with no significance different (Z=-.493, p=0.640, p>0.05). In contrast, only 1 RM strength test for the upper body strength test shown with a significance different (Z=- 2.537, p=0.009, p<0.05) the. Hence, the Body posture among the football athletes with anomalies does not influence selected physical fitness components. This study has proven, that postural anomalies will not affect or influence the physical performance the respective athletes.

Keywords: Postural Analysis, Anomalies, Flexibility, Strength, SAQ.

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Authors: Yu Bin, Zhang Yan

Abstract:

The prediction of transmembrane helical segments (TMHs) in membrane proteins is an important field in the bioinformatics research. In this paper, a new method based on discrete wavelet transform (DWT) has been developed to predict the number and location of TMHs in membrane proteins. PDB coded as 1KQG was chosen as an example to describe the prediction of the number and location of TMHs in membrane proteins by using this method. To access the effect of the method, 80 proteins with known 3D-structure from Mptopo database are chosen at random as the test objects (including 325 TMHs), 308 of which can be predicted accurately, the average predicted accuracy is 96.3%. In addition, the above 80 membrane proteins are divided into 13 groups according to their function and type. In particular, the results of the prediction of TMHs of the 13 groups are satisfying.

Keywords: discrete wavelet transform, hydrophobicity, membrane protein, transmembrane helical segments

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Authors: Asmir Gogic, Aljo Mujcic, Sandra Ibric, Nermin Suljanovic

Abstract:

Ubiquity of natural disasters during last few decades have risen serious questions towards the prediction of such events and human safety. Every disaster regardless its proportion has a precursor which is manifested as a disruption of some environmental parameter such as temperature, humidity, pressure, vibrations and etc. In order to anticipate and monitor those changes, in this paper we propose an overall system for disaster prediction and monitoring, based on wireless sensor network (WSN). Furthermore, we introduce a modified and simplified WSN routing protocol built on the top of the trickle routing algorithm. Routing algorithm was deployed using the bluetooth low energy protocol in order to achieve low power consumption. Performance of the WSN network was analyzed using a real life system implementation. Estimates of the WSN parameters such as battery life time, network size and packet delay are determined. Based on the performance of the WSN network, proposed system can be utilized for disaster monitoring and prediction due to its low power profile and mesh routing feature.

Keywords: Bluetooth low energy, disaster prediction, mesh routing protocols, wireless sensor networks.

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Authors: Sallehan Ismail, Mahyuddin Ramli

Abstract:

This paper presents the experiment results of investigating the effects of adding various types and proportions of fibre on mechanical strength and permeability characteristics of recycled aggregate concrete (RAC), which was produced with treated coarse recycled concrete aggregate (RCA). Two types of synthetic fibres (i.e., barchip and polypropylene fibre) with various volume fractions were added to the RAC, which was calculated by the weight of the cement. The hardened RAC properties such as compressive strength, flexural strength, ultrasonic pulse velocity, water absorption and total porosity at the curing ages of 7 and 28 days were evaluated and compared with the properties of the control specimens. Results indicate that the treated coarse RCA enhances the mechanical strength and permeability properties of RAC and adding barchip fibre further optimises the results. Adding 1.2% barchip fibre has the best effect on the mechanical strength performance of the RAC.

Keywords: Barchip fibre, polypropylene fibre, recycled aggregate concrete.

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Authors: Emad Amar, Tawfik Khattab, Fatma Zada

Abstract:

Predicting earthquakes is an important issue in the study of geography. Accurate prediction of earthquakes can help people to take effective measures to minimize the loss of personal and economic damage, such as large casualties, destruction of buildings and broken of traffic, occurred within a few seconds. United States Geological Survey (USGS) science organization provides reliable scientific information about Earthquake Existed throughout history & the Preliminary database from the National Center Earthquake Information (NEIC) show some useful factors to predict an earthquake in a seismic area like Aleutian Arc in the U.S. state of Alaska. The main advantage of this prediction method that it does not require any assumption, it makes prediction according to the future evolution of the object's time series. The article compares between simulation data result from trained BP and RBF neural network versus actual output result from the system calculations. Therefore, this article focuses on analysis of data relating to real earthquakes. Evaluation results show better accuracy and higher speed by using radial basis functions (RBF) neural network.

Keywords: BP neural network, Prediction, RBF neural network.

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Authors: Yiannis G. Smirlis

Abstract:

The classification and the prediction of efficiencies in Data Envelopment Analysis (DEA) is an important issue, especially in large scale problems or when new units frequently enter the under-assessment set. In this paper, we contribute to the subject by proposing a grid structure based on interval segmentations of the range of values for the inputs and outputs. Such intervals combined, define hyper-rectangles that partition the space of the problem. This structure, exploited by Interval DEA models and a dominance relation, acts as a DEA pre-processor, enabling the classification and prediction of efficiency scores, without applying any DEA models.

Keywords: Data envelopment analysis, interval DEA, efficiency classification, efficiency prediction.

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Authors: Radouane Iqdour, Abdelouhab Zeroual

Abstract:

The Multi-Layered Perceptron (MLP) Neural networks have been very successful in a number of signal processing applications. In this work we have studied the possibilities and the met difficulties in the application of the MLP neural networks for the prediction of daily solar radiation data. We have used the Polack-Ribière algorithm for training the neural networks. A comparison, in term of the statistical indicators, with a linear model most used in literature, is also performed, and the obtained results show that the neural networks are more efficient and gave the best results.

Keywords: Daily solar radiation, Prediction, MLP neural networks, linear model

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Authors: M. Karimpour, L. Hitihamillage, N. Elkhoury, S. Moridpour, R. Hesami

Abstract:

Rail transport authorities around the world have been facing a significant challenge when predicting rail infrastructure maintenance work for a long period of time. Generally, maintenance monitoring and prediction is conducted manually. With the restrictions in economy, the rail transport authorities are in pursuit of improved modern methods, which can provide precise prediction of rail maintenance time and location. The expectation from such a method is to develop models to minimize the human error that is strongly related to manual prediction. Such models will help them in understanding how the track degradation occurs overtime under the change in different conditions (e.g. rail load, rail type, rail profile). They need a well-structured technique to identify the precise time that rail tracks fail in order to minimize the maintenance cost/time and secure the vehicles. The rail track characteristics that have been collected over the years will be used in developing rail track degradation prediction models. Since these data have been collected in large volumes and the data collection is done both electronically and manually, it is possible to have some errors. Sometimes these errors make it impossible to use them in prediction model development. This is one of the major drawbacks in rail track degradation prediction. An accurate model can play a key role in the estimation of the long-term behavior of rail tracks. Accurate models increase the track safety and decrease the cost of maintenance in long term. In this research, a short review of rail track degradation prediction models has been discussed before estimating rail track degradation for the curve sections of Melbourne tram track system using Adaptive Network-based Fuzzy Inference System (ANFIS) model.

Keywords: ANFIS, MGT, Prediction modeling, rail track degradation.

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Authors: Mansour A. Mohamed, George G. Karady, Ali M. Yousef

Abstract:

This paper proposes transient angle stability agents to enhance power system stability. The proposed transient angle stability agents divided into two strategy agents. The first strategy agent is a prediction agent that will predict power system instability. According to the prediction agent-s output, the second strategy agent, which is a control agent, is automatically calculating the amount of active power reduction that can stabilize the system and initiating a control action. The control action considered is turbine fast valving. The proposed strategies are applied to a realistic power system, the IEEE 50- generator system. Results show that the proposed technique can be used on-line for power system instability prediction and control.

Keywords: Multi-agents, Fast Valving, Power System Transient Stability, Prediction methods,

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Authors: Surender Kumar Soni, Dhirendra Pratap Singh

Abstract:

Wireless Sensor Networks can be used to monitor the physical phenomenon in such areas where human approach is nearly impossible. Hence the limited power supply is the major constraint of the WSNs due to the use of non-rechargeable batteries in sensor nodes. A lot of researches are going on to reduce the energy consumption of sensor nodes. Energy map can be used with clustering, data dissemination and routing techniques to reduce the power consumption of WSNs. Energy map can also be used to know which part of the network is going to fail in near future. In this paper, Energy map is constructed using the prediction based approach. Adaptive alpha GM(1,1) model is used as the prediction model. GM(1,1) is being used worldwide in many applications for predicting future values of time series using some past values due to its high computational efficiency and accuracy.

Keywords: Adaptive Alpha GM(1, 1) Model, Energy Map, Prediction Based Data Reduction, Wireless Sensor Networks

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Authors: Min Ho Kwon, Woo Young Jung, Hyun Su Seo

Abstract:

A polymer cement mortar (PCM) has been widely used  as the material of repair and restoration work for concrete structure;  however a PCM usually induces an environmental pollutant.  Therefore, there is a need to develop PCM which is less impact to  environments. Usually, UM resin is known to be harmless to the  environment. Accordingly, in this paper, the properties of the PCM  using UM resin were studied. The general cement mortar and UM  resin were mixed in the specified ratio. A certain percentage of PVA  fibers, steel fibers and mixed fibers (PVA fiber and steel fiber) were  added to enhance the flexural strength. The flexural tests were  performed in order to investigate the flexural strength of each PCM.  Experimental results showed that the strength of proposed PCM using  UM resin is improved when they are compared with general cement  mortar.

 

Keywords: Polymer cement mortar (PCM), UM resin, Compressive strength, PVA fiber, Steel fiber.

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Authors: Nabilah Filzah Mohd Radzuan, Zalinda Othman, Azuraliza Abu Bakar, Abdul Razak Hamdan

Abstract:

Uncertain data is believed to be an important issue in building up a prediction model. The main objective in the time series uncertainty analysis is to formulate uncertain data in order to gain knowledge and fit low dimensional model prior to a prediction task. This paper discusses the performance of a number of techniques in dealing with uncertain data specifically those which solve uncertain data condition by minimizing the loss of compression properties.

Keywords: Compression properties, uncertainty, uncertain time series, mining technique, weather prediction.

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Authors: S. Channgam, A. Sae-Tang, T. Termsaithong

Abstract:

In this research, the occurrences of large size events in various system sizes of the Bak-Tang-Wiesenfeld sandpile model are considered. The system sizes (square lattice) of model considered here are 25×25, 50×50, 75×75 and 100×100. The cross-correlation between the ratio of sites containing 3 grain time series and the large size event time series for these 4 system sizes are also analyzed. Moreover, a prediction method of the large-size event for the 50×50 system size is also introduced. Lastly, it can be shown that this prediction method provides a slightly higher efficiency than random predictions.

Keywords: Bak-Tang-Wiesenfeld sandpile model, avalanches, cross-correlation, prediction method.

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Authors: Ulas Vural, M. Ergun Okay, E. Mesut Yildiz

Abstract:

Telecommunication service providers demand accurate and precise prediction of customer churn probabilities to increase the effectiveness of their customer relation services. The large amount of customer data owned by the service providers is suitable for analysis by machine learning methods. In this study, expenditure data of customers are analyzed by using an artificial neural network (ANN). The ANN model is applied to the data of customers with different billing duration. The proposed model successfully predicts the churn probabilities at 83% accuracy for only three months expenditure data and the prediction accuracy increases up to 89% when the nine month data is used. The experiments also show that the accuracy of ANN model increases on an extended feature set with information of the changes on the bill amounts.

Keywords: Customer relationship management, churn prediction, telecom industry, deep learning, Artificial Neural Networks, ANN.

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Authors: P. Hedbávný, G. Bago, M. Kalichová

Abstract:

The contribution deals with influence of strength abilities on quality of performance of static balance movement structure – handstand. To test the strength abilities we selected following tests: number of push-ups per minute and persistence in trunk backward bend in sitting position. We tested the dependent variable by three tests – persistence in handstand position on a stabilometric platform, persistence in handstand position and evaluation of quality of handstand performance. Pearson’s correlation coefficient was used to formulate the relationship between variables. The results showed a statistically significant dependence using which we deduced conclusions for training practice.

Keywords: Strength abilities, handstand, balance.

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Authors: Anjan Kumar Kakati, M. Chandrasekaran, Amitava Mandal, Amit Kumar Singh

Abstract:

End milling process is one of the common metal cutting operations used for machining parts in manufacturing industry. It is usually performed at the final stage in manufacturing a product and surface roughness of the produced job plays an important role. In general, the surface roughness affects wear resistance, ductility, tensile, fatigue strength, etc., for machined parts and cannot be neglected in design. In the present work an experimental investigation of end milling of aluminium alloy with carbide tool is carried out and the effect of different cutting parameters on the response are studied with three-dimensional surface plots. An artificial neural network (ANN) is used to establish the relationship between the surface roughness and the input cutting parameters (i.e., spindle speed, feed, and depth of cut). The Matlab ANN toolbox works on feed forward back propagation algorithm is used for modeling purpose. 3-12-1 network structure having minimum average prediction error found as best network architecture for predicting surface roughness value. The network predicts surface roughness for unseen data and found that the result/prediction is better. For desired surface finish of the component to be produced there are many different combination of cutting parameters are available. The optimum cutting parameter for obtaining desired surface finish, to maximize tool life is predicted. The methodology is demonstrated, number of problems are solved and algorithm is coded in Matlab®.

Keywords: End milling, Surface roughness, Neural networks.

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Authors: Liping Jing, Michael K. Ng, Tieyong Zeng

Abstract:

Microarray data profiles gene expression on a whole genome scale, therefore, it provides a good way to study associations between gene expression and occurrence or progression of cancer. More and more researchers realized that microarray data is helpful to predict cancer sample. However, the high dimension of gene expressions is much larger than the sample size, which makes this task very difficult. Therefore, how to identify the significant genes causing cancer becomes emergency and also a hot and hard research topic. Many feature selection algorithms have been proposed in the past focusing on improving cancer predictive accuracy at the expense of ignoring the correlations between the features. In this work, a novel framework (named by SGS) is presented for stable gene selection and efficient cancer prediction . The proposed framework first performs clustering algorithm to find the gene groups where genes in each group have higher correlation coefficient, and then selects the significant genes in each group with Bayesian Lasso and important gene groups with group Lasso, and finally builds prediction model based on the shrinkage gene space with efficient classification algorithm (such as, SVM, 1NN, Regression and etc.). Experiment results on real world data show that the proposed framework often outperforms the existing feature selection and prediction methods, say SAM, IG and Lasso-type prediction model.

Keywords: Gene Selection, Cancer Prediction, Lasso, Clustering, Classification.

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Authors: Grzegorz Ulacha, Ryszard Stasiński

Abstract:

In the paper an effective context based lossless coding technique is presented. Three principal and few auxiliary contexts are defined. The predictor adaptation technique is an improved CoBALP algorithm, denoted CoBALP+. Cumulated predictor error combining 8 bias estimators is calculated. It is shown experimentally that indeed, the new technique is time-effective while it outperforms the well known methods having reasonable time complexity, and is inferior only to extremely computationally complex ones.

Keywords: Adaptive prediction, context coding, image losslesscoding, prediction error bias correction.

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Authors: Haider M. Alsaeq

Abstract:

This research investigates the effects of the opening shape and location on the structural behavior of reinforced concrete deep beam with openings, while keeping the opening size unchanged. The software ANSYS 12.1 is used to handle the nonlinear finite element analysis. The ultimate strength of reinforced concrete deep beam with opening obtained by ANSYS 12.1 shows fair agreement with the experimental results, with a difference of no more than 20%. The present work concludes that the opening location has much more effect on the structural strength than the opening shape. It was concluded that placing the openings near the upper corners of the deep beam may double the strength, and the use of a rectangular narrow opening, with the long sides in the horizontal direction, can save up to 40% of structural strength of the deep beam.

Keywords: Deep Beams, Finite Element, Opening, Reinforced Concrete.

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Authors: Hyun-Woo Cho

Abstract:

Detection of incipient abnormal events is important to improve safety and reliability of machine operations and reduce losses caused by failures. Improper set-ups or aligning of parts often leads to severe problems in many machines. The construction of prediction models for predicting faulty conditions is quite essential in making decisions on when to perform machine maintenance. This paper presents a multivariate calibration monitoring approach based on the statistical analysis of machine measurement data. The calibration model is used to predict two faulty conditions from historical reference data. This approach utilizes genetic algorithms (GA) based variable selection, and we evaluate the predictive performance of several prediction methods using real data. The results shows that the calibration model based on supervised probabilistic principal component analysis (SPPCA) 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: Prediction, operation monitoring, on-line data, nonlinear statistical methods, empirical model.

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Authors: A. Masi, A. Digrisolo, G. Santarsiero

Abstract:

Concrete strength evaluated from compression tests on cores is affected by several factors causing differences from the in-situ strength at the location from which the core specimen was extracted. Among the factors, there is the damage possibly occurring during the drilling phase that generally leads to underestimate the actual in-situ strength. In order to quantify this effect, in this study two wide datasets have been examined, including: (i) about 500 core specimens extracted from Reinforced Concrete existing structures, and (ii) about 600 cube specimens taken during the construction of new structures in the framework of routine acceptance control. The two experimental datasets have been compared in terms of compression strength and specific weight values, accounting for the main factors affecting a concrete property, that is type and amount of cement, aggregates' grading, type and maximum size of aggregates, water/cement ratio, placing and curing modality, concrete age. The results show that the magnitude of the strength reduction due to drilling damage is strongly affected by the actual properties of concrete, being inversely proportional to its strength. Therefore, the application of a single value of the correction coefficient, as generally suggested in the technical literature and in structural codes, appears inappropriate. A set of values of the drilling damage coefficient is suggested as a function of the strength obtained from compressive tests on cores.

Keywords: RC Buildings, Assessment, In-situ concrete strength, Core testing, Drilling damage.

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Authors: Abdoullah Namdar, Fadzil Mat Yahaya

Abstract:

Many synthetic additives have been using for improve cement mortar and concrete characteristics, but natural additive is a friendly environment option. The quantity of (2% and 4%) seashell powder has been replaced in cement mortar, and compared with plain cement mortar in early age of 7 days. The strain gauges have been installed on beams and cube, for monitoring fluctuation of flexural and compressive strength. Main objective of this paper is to study effect of linear static force on flexural and compressive strength of modified cement mortar. The results have been indicated that the replacement of appropriate proportion of seashell powder enhances cement mortar mechanical properties. The replacement of 2% seashell causes improvement of deflection, time to failure and maximum load to failure on concrete beam and cube, the same occurs for compressive modulus elasticity. Increase replacement of seashell to 4% reduces all flexural strength, compressive strength and strain of cement mortar.

Keywords: Compressive strength, flexural strength, compressive modulus elasticity, time to failure, deflection.

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Authors: I. Barsoum, F. Khan

Abstract:

the current study presents a modeling framework to determine the torsion strength of an induction hardened splined shaft by considering geometry and material aspects with the aim to optimize the static torsion strength by selection of spline geometry and hardness depth. Six different spline geometries and seven different hardness profiles including non-hardened and throughhardened shafts have been considered. The results reveal that the torque that causes initial yielding of the induction hardened splined shaft is strongly dependent on the hardness depth and the geometry of the spline teeth. Guidelines for selection of the appropriate hardness depth and spline geometry are given such that an optimum static torsion strength of the component can be achieved.

Keywords: Static strength, splined shaft, torsion, induction hardening, hardness profile, finite element, optimization, design.

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Authors: K. Sreelakshmi, P. Ramakanthkumar

Abstract:

Predicting short term wind speed is essential in order to prevent systems in-action from the effects of strong winds. It also helps in using wind energy as an alternative source of energy, mainly for Electrical power generation. Wind speed prediction has applications in Military and civilian fields for air traffic control, rocket launch, ship navigation etc. The wind speed in near future depends on the values of other meteorological variables, such as atmospheric pressure, moisture content, humidity, rainfall etc. The values of these parameters are obtained from a nearest weather station and are used to train various forms of neural networks. The trained model of neural networks is validated using a similar set of data. The model is then used to predict the wind speed, using the same meteorological information. This paper reports an Artificial Neural Network model for short term wind speed prediction, which uses back propagation algorithm.

Keywords: Short term wind speed prediction, Neural networks, Back propagation.

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Authors: Heba Z. Fouad, Tarek M. Madkour, Safwan A. Khedr

Abstract:

This paper investigates multiple factors that impact the formation of geopolymers and their compressive strength to be utilized in construction as an environmentally-friendly material. Bentonite and Kaolinite were thermally calcinated at 750 °C to obtain Metabentonite and Metakaolinite with higher reactivity. Both source materials were activated using a solution of sodium hydroxide (NaOH). Thereafter, samples were cured at different temperatures. The samples were analyzed chemically using a host of spectroscopic techniques. The bulk density and compressive strength of the produced geopolymer pastes were studied. Findings indicate that the ratio of NaOH solution to source material affects the compressive strength, being optimal at 0.54. Moreover, controlled heat curing was proven effective to improve compressive strength. The existence of characteristic Fourier Transform Infrared Spectroscopy (FTIR) peaks at approximately 1020 cm-1 and 460 cm-1 which correspond to the asymmetric stretching vibration of Si-O-T and bending vibration of Si-O-Si, hence, confirming the formation of the target geopolymer.

Keywords: alcination of metakaolinite, compressive strength, FTIR analysis, geopolymer, green cement

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Authors: Demet Yavuz

Abstract:

Freeze-thaw cycles are one of the greatest threats to concrete durability. Lately, protection against this threat excites scientists’ attention. Air-entraining admixtures have been widely used to produce freeze-thaw resistant at concretes. The use of air-entraining agents (AEAs) enhances not only freeze-thaw endurance but also the properties of fresh concrete such as segregation, bleeding and flow ability. This paper examines the effects of air-entraining on compressive strength of concrete. Air-entraining is used between 0.05% and 0.4% by weight of cement. One control and four fiber reinforced concrete mixes are prepared and three specimens are tested for each mix. It is concluded from the test results that when air entraining is increased the compressive strength of concrete reduces for all mixes with AEAs.

Keywords: Concrete, air-entraining, compressive strength, mechanical properties.

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Authors: Nurdin

Abstract:

Suppose G(V,E) is a graph, a function f : V \cup E \to \{1, 2, 3, \cdots, k\} is called the total edge(vertex) irregular k-labelling for G such that for each two edges are different having distinct weights. The total edge(vertex) irregularity strength of G, denoted by tes(G)(tvs(G), is the smallest k positive integers such that G has a total edge(vertex) irregular k-labelling. In this paper, we determined the total edge(vertex) irregularity strength of an amalgamation of two isomorphic cycles. The total edge irregularity strength and the total vertex irregularity strength of two isomorphic cycles on n vertices are \lceil (2n+2)/3 \rceil and \lceil 2n/3 \rceil for n \geq 3, respectively.

Keywords: Amalgamation of graphs, irregular labelling, irregularity strength.

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Authors: Fariba Esfandiary, Ghafoor Aghaie, Ali Dolati Mehr

Abstract:

Wheat prediction was carried out using different meteorological variables together with agro meteorological indices in Ardebil district for the years 2004-2005 & 2005–2006. On the basis of correlation coefficients, standard error of estimate as well as relative deviation of predicted yield from actual yield using different statistical models, the best subset of agro meteorological indices were selected including daily minimum temperature (Tmin), accumulated difference of maximum & minimum temperatures (TD), growing degree days (GDD), accumulated water vapor pressure deficit (VPD), sunshine hours (SH) & potential evapotranspiration (PET). Yield prediction was done two months in advance before harvesting time which was coincide with commencement of reproductive stage of wheat (5th of June). It revealed that in the final statistical models, 83% of wheat yield variability was accounted for variation in above agro meteorological indices.

Keywords: Wheat yields prediction, agro meteorological indices, statistical models

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Authors: Shogo Kato, Daisuke Okamoto, Satoko Tsuru, Yoshinori Iizuka, Ryoko Shimono

Abstract:

Healthcare safety has been perceived important. It is essential to prevent troubles in healthcare processes for healthcare safety. Trouble prevention is based on trouble prediction using accumulated knowledge on processes, troubles, and countermeasures. However, information on troubles has not been accumulated in hospitals in the appropriate structure, and it has not been utilized effectively to prevent troubles. In the previous study, however a detailed knowledge acquisition process for trouble prediction was proposed, the knowledgebase for countermeasures was not involved. In this paper, we aim to propose the structure of the knowledgebase for countermeasures, in the knowledge acquisition process for trouble prediction in healthcare process. We first design the structure of countermeasures and propose the knowledge representation form on countermeasures. Then, we evaluate the validity of the proposal, by applying it into an actual hospital.

Keywords: Trouble prevention, knowledge structure, structured knowledge, reusable knowledge.

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Authors: M. Nili, S. Tavasoli, A. R. Yazdandoost

Abstract:

In this paper, an experimental investigation on the effect of Isfahan Ground Granulate Blast Furnace Slag (GGBS) on the compressive strength development of self-consolidating concrete (SCC) and normal concrete (NC) was performed. For this purpose, Portland cement type I was replaced with GGBS in various Portions. For NC and SCC Mixes, 10*10*10 cubic cm specimens were tested in 7, 28 and 91 days. It must be stated that in this research water to cement ratio was 0.44, cement used in cubic meter was 418 Kg/m³ and Superplasticizer (SP) Type III used in SCC based on Poly-Carboxylic acid. The results of experiments have shown that increasing GGBS Percentages in both types of concrete reduce Compressive strength in early ages.

Keywords: Compressive strength, GGBS, normal concrete, self-consolidating concrete.

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