Search results for: compressive strength prediction

Authors: P. van Tonder, U. Bagdadi, B. M. D. Lario, Z. Masina, T. R. Motshwari

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This paper analyses how concrete reinforcing steel bars corrode and how it can be minimised through the use of various protection methods against corrosion, such as metal-based paint, alloying, cathodic protection and electroplating. Samples of carbon steel bars were protected, using these four methods. Tests performed on the samples included durability, electrical resistivity and bond strength. Durability results indicated relatively low corrosion rates for alloying, cathodic protection, electroplating and metal-based paint. The resistivity results indicate all samples experienced a downward trend, despite erratic fluctuations in the data, indicating an inverse relationship between electrical resistivity and corrosion rate. The results indicated lowered bond strengths when the reinforced concrete was cured in seawater compared to being cured in normal water. It also showed that higher design compressive strengths lead to higher bond strengths which can be used to compensate for the loss of bond strength due to corrosion in a real-world application. In terms of implications, all protection methods have the potential to be effective at resisting corrosion in real-world applications, especially the alloying, cathodic protection and electroplating methods. The metal-based paint underperformed by comparison, most likely due to the nature of paint in general which can fade and chip away, revealing the steel samples and exposing them to corrosion. For alloying, stainless steel is the suggested material of choice, where Y-bars are highly recommended as smooth bars have a much-lowered bond strength. Cathodic protection performed the best of all in protecting the sample from corrosion, however, its real-world application would require significant evaluation into the feasibility of such a method.

Keywords: protection methods, corrosion, concrete, reinforcing steel bars

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Authors: Bin Li, Mei Liu

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Arrival time information is a crucial component of advanced public transport system (APTS). The advertisement of arrival time at stops can help reduce the waiting time and anxiety of passengers, and improve the quality of service. In this research, an experiment was conducted to compare the performance on prediction accuracy and precision between the link-based and the path-based historical travel time based model with the automatic vehicle location (AVL) data collected from an actual bus route. The research results show that the path-based model is superior to the link-based model, and achieves the best improvement on peak hours.

Keywords: bus transit, arrival time prediction, link-based, path-based

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Authors: B. G. Ayantunji, B. Musa, H. Mai-Unguwa, L. A. Sunmonu, A. S. Adewumi, L. Sa'ad, A. Kado

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For achieving reliable and efficient communication system, both terrestrial and satellite communication, surface refractivity is critical in planning and design of radio links. This study analyzed the impact of atmospheric parameters on Ultra High Frequency (UHF) signal strength over Gusau, North West, Nigeria. The analysis exploited meteorological data measured simultaneously with UHF signal strength for the month of June 2017 using a Davis Vantage Pro2 automatic weather station and UHF signal strength measuring devices respectively. The instruments were situated at the premise of Federal University, Gusau (6° 78' N, 12° 13' E). The refractivity values were computed using ITU-R model. The result shows that the refractivity value attained the highest value of 366.28 at 2200hr and a minimum value of 350.66 at 2100hr local time. The correlation between signal strength and refractivity is 0.350; Humidity is 0.532 and a negative correlation of -0.515 for temperature.

Keywords: refractivity, UHF (ultra high frequency) signal strength, free space, automatic weather station

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Authors: Humaira Athar, Brajeshwar Singh

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Generally, the brightness of the indoor environment of buildings is entirely maintained by the artificial lighting which has consumed a large amount of resources. It is reported that lighting consumes about 19% of the total generated electricity which accounts for about 30-40% of total energy consumption. One possible way is to reduce the lighting energy by exploiting sunlight either through the use of suitable devices or energy efficient materials like translucent concrete. Translucent concrete is one such architectural concrete which allows the passage of natural light as well as artificial light through it. Several attempts have been made on different aspects of translucent concrete such as light guiding materials (glass fibers, plastic fibers, cylinder etc.), concrete mix design and manufacturing methods for use as building elements. Concerns are, however, raised on various related issues such as poor compatibility between the optical fibers and cement paste, unaesthetic appearance due to disturbance occurred in the arrangement of fibers during vibration and high shrinkage in flowable concrete due to its high water/cement ratio. Need is felt to develop translucent concrete to meet the requirement of structural safety as OPC concrete with the maximized saving in energy towards the power of illumination and thermal load in buildings. Translucent concrete was produced using pre-treated plastic optical fibers (POF, 2mm dia.) and high slump white concrete. The concrete mix was proportioned in the ratio of 1:1.9:2.1 with a w/c ratio of 0.40. The POF was varied from 0.8-9 vol.%. The mechanical properties and light transmission of this concrete were determined. Thermal conductivity of samples was measured by a transient plate source technique. Daylight illumination was measured by a lux grid method as per BIS:SP-41. It was found that the compressive strength of translucent concrete increased with decreasing optical fiber content. An increase of ~28% in the compressive strength of concrete was noticed when fiber was pre-treated. FE-SEM images showed little-debonded zone between the fibers and cement paste which was well supported with pull-out bond strength test results (~187% improvement over untreated). The light transmission of concrete was in the range of 3-7% depending on fiber spacing (5-20 mm). The average daylight illuminance (~75 lux) was nearly equivalent to the criteria specified for illumination for circulation (80 lux). The thermal conductivity of translucent concrete was reduced by 28-40% with respect to plain concrete. The thermal load calculated by heat conduction equation was ~16% more than the plain concrete. Based on Design-Builder software, the total annual illumination energy load of a room using one side translucent concrete was 162.36 kW compared with the energy load of 249.75 kW for a room without concrete. The calculated energy saving on an account of the power of illumination was ~25%. A marginal improvement towards thermal comfort was also noticed. It is concluded that the translucent concrete has the advantages of the existing concrete (load bearing) with translucency and insulation characteristics. It saves a significant amount of energy by providing natural daylight instead of artificial power consumption of illumination.

Keywords: energy saving, light transmission, microstructure, plastic optical fibers, translucent concrete

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Authors: Rim Zgueb, Noureddine Yacoubi

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The objective of this study was to investigate the thermal property of magnesium oxychloride cement (MOC) using glass powder as a substitute. Glass powder by proportion 0%, 5%, 10%, 15% and 20% of cement’s weight was added to specimens. At the end of a drying time of 28 days, thermal properties, compressive strength and bulk density of samples were determined. Thermal property is measured by Photothermal Deflection Technique by comparing the experimental of normalized amplitude and the phase curves of the photothermal signal to the corresponding theoretical ones. The findings indicate that incorporation of glass powder decreases the thermal properties of MOC.

Keywords: magnesium oxychloride cement (MOC), phototharmal deflection technique, thermal properties, Ddensity

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Authors: Sohyoung Won, Heebal Kim, Dajeong Lim

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Genomic prediction is an effective way to measure the abilities of livestock for breeding based on genomic estimated breeding values, statistically predicted values from genotype data using best linear unbiased prediction (BLUP). Using haplotypes, clusters of linked single nucleotide polymorphisms (SNPs), as markers instead of individual SNPs can improve the reliability of genomic prediction since the probability of a quantitative trait loci to be in strong linkage disequilibrium (LD) with markers is higher. To efficiently use haplotypes in genomic prediction, finding optimal ways to define haplotypes is needed. In this study, 770K SNP chip data was collected from Hanwoo (Korean cattle) population consisted of 2506 cattle. Haplotypes were first defined in three different ways using 770K SNP chip data: haplotypes were defined based on 1) length of haplotypes (bp), 2) the number of SNPs, and 3) k-medoids clustering by LD. To compare the methods in parallel, haplotypes defined by all methods were set to have comparable sizes; in each method, haplotypes defined to have an average number of 5, 10, 20 or 50 SNPs were tested respectively. A modified GBLUP method using haplotype alleles as predictor variables was implemented for testing the prediction reliability of each haplotype set. Also, conventional genomic BLUP (GBLUP) method, which uses individual SNPs were tested to evaluate the performance of the haplotype sets on genomic prediction. Carcass weight was used as the phenotype for testing. As a result, using haplotypes defined by all three methods showed increased reliability compared to conventional GBLUP. There were not many differences in the reliability between different haplotype defining methods. The reliability of genomic prediction was highest when the average number of SNPs per haplotype was 20 in all three methods, implying that haplotypes including around 20 SNPs can be optimal to use as markers for genomic prediction. When the number of alleles generated by each haplotype defining methods was compared, clustering by LD generated the least number of alleles. Using haplotype alleles for genomic prediction showed better performance, suggesting improved accuracy in genomic selection. The number of predictor variables was decreased when the LD-based method was used while all three haplotype defining methods showed similar performances. This suggests that defining haplotypes based on LD can reduce computational costs and allows efficient prediction. Finding optimal ways to define haplotypes and using the haplotype alleles as markers can provide improved performance and efficiency in genomic prediction.

Keywords: best linear unbiased predictor, genomic prediction, haplotype, linkage disequilibrium

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Authors: Bikis Muhammed, Sehra Sedigh Sarvestani, Ali R. Hurson, Lasanthi Gamage

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Vehicular traffic events have overly complex spatial correlations and temporal interdependencies and are also influenced by environmental events such as weather conditions. To capture these spatial and temporal interdependencies and make more realistic vehicular traffic predictions, graph neural networks (GNN) based traffic prediction models have been extensively utilized due to their capability of capturing non-Euclidean spatial correlation very effectively. However, most of the already existing GNN-based traffic prediction models have some limitations during learning complex and dynamic spatial and temporal patterns due to the following missing factors. First, most GNN-based traffic prediction models have used static distance or sometimes haversine distance mechanisms between spatially separated traffic observations to estimate spatial correlation. Secondly, most GNN-based traffic prediction models have not incorporated environmental events that have a major impact on the normal traffic states. Finally, most of the GNN-based models did not use an attention mechanism to focus on only important traffic observations. The objective of this paper is to study and make real-time vehicular traffic predictions while incorporating the effect of weather conditions. To fill the previously mentioned gaps, our prediction model uses a real-time driving distance between sensors to build a distance matrix or spatial adjacency matrix and capture spatial correlation. In addition, our prediction model considers the effect of six types of weather conditions and has an attention mechanism in both spatial and temporal data aggregation. Our prediction model efficiently captures the spatial and temporal correlation between traffic events, and it relies on the graph attention network (GAT) and Bidirectional bidirectional long short-term memory (Bi-LSTM) plus attention layers and is called GAT-BILSTMA.

Keywords: deep learning, real time prediction, GAT, Bi-LSTM, attention

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Authors: Mohammad Zavid Parvez, Manoranjan Paul

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Epilepsy is a common neurological disorders characterized by sudden recurrent seizures. Electroencephalogram (EEG) is widely used to diagnose possible epileptic seizure. Many research works have been devoted to predict epileptic seizure by analyzing EEG signal. Seizure prediction by analyzing EEG signals are challenging task due to variations of brain signals of different patients. In this paper, we propose a new approach for feature extraction based on phase correlation in EEG signals. In phase correlation, we calculate relative change between two consecutive segments of an EEG signal and then combine the changes with neighboring signals to extract features. These features are then used to classify preictal/ictal and interictal EEG signals for seizure prediction. Experiment results show that the proposed method carries good prediction rate with greater consistence for the benchmark data set in different brain locations compared to the existing state-of-the-art methods.

Keywords: EEG, epilepsy, phase correlation, seizure

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Authors: Faramarz Ashenai Ghasemi, Ismail Ghasemi, Sajad Daneshpayeh

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In this study, tensile strength and elastic modulus of nanocomposites based on polypropylene/ linear low density polyethylene/ nano titanium dioxide (PP/LLDPE/TiO2) were studied. The samples were produced using a co-rotating twin screw extruder including 0, 2, 4 Wt .% of nano particles, and 20, 40, 60 Wt.% of LLDPE. The styrene-ethylene-butylene-styrene (SEBS) was used as comptabiliser. Tensile strength and elastic modulus were evaluated. The results showed that modulus was increased by 7% with addition of nano particles in comparison to PP/LLDPE. In addition, tensile strength was decreased.

Keywords: PP/LLDPE/TiO2, nanocomposites, elastic modulus, tensile strength

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Authors: Sweta Das

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This case report is a 15-year-old male who presented with painless unilateral vision loss from left optic nerve compression due to juvenile nasopharyngeal angiofibroma. JNA is a rare, benign neoplasm that causes intracranial and intraorbital bone destruction and extends aggressively into surrounding soft tissues. It accounts for <1% of all head and neck tumors, is predominantly found in pediatric males and tends to affect indigenous population disproportionately. The most common presenting symptom for JNA is epistaxis and nasal obstruction. However, it can invade orbit, chiasm and pituitary gland, causing loss of vision and field. Visual acuity and function near normalized following surgical excision. Optometry plays an important role in the diagnosis and co-management of JNA with optic nerve compression by closely monitoring afferent optic nerve function and structure, and extraocular motility. Visual function and acuity in patients with short-term compressive neuropathy may drastically improve following surgical resection as this case demonstrates.

Keywords: orbital mass, painless monocular vision loss, compressive optic neuropathy, pediatric tumor

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Authors: S. Yousefi Oderji, B. Chen, M. A. Yazdi, J. Yang

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This study investigates the influence of water-binder ratio, mineral admixtures (silica fume and ground granulated blast furnace slag), and copper coated steel fiber on fluidity diameter, compressive and flexural strengths of reactive powder concrete (RPC). The test results show that the binary combination of silica fume and blast-furnace slag provided a positive influence on the mechanical properties of RPC. Although the addition of fibers reduced the workability, results indicated a higher mechanical strength in the inclusion of fibers.

Keywords: RPC, steel fiber, fluidity, mechanical properties

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Authors: Pallavi Mane, Nikita Mankar, Shraddha Mazire, Rasika Pashankar

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Content sharing sites are very useful in sharing information and images. However, with the increasing demand of content sharing sites privacy and security concern have also increased. There is need to develop a tool for controlling user access to their shared content. Therefore, we are developing an Adaptive Privacy Policy Prediction (A3P) system which is helpful for users to create privacy settings for their images. We propose the two-level framework which assigns the best available privacy policy for the users images according to users available histories on the site.

Keywords: online information services, prediction, security and protection, web based services

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Authors: Sam Khozama, Ali M. Mayya

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Breast cancer is one of the most common types in women. Early prediction of breast cancer helps physicians detect cancer in its early stages. Big cancer data needs a very powerful tool to analyze and extract predictions. Machine learning and deep learning are two of the most efficient tools for predicting cancer based on textual data. In this study, we developed a fusion model of two machine learning and deep learning models. To obtain the final prediction, Long-Short Term Memory (LSTM) and ensemble learning with hyper parameters optimization are used, and score-level fusion is used. Experiments are done on the Breast Cancer Surveillance Consortium (BCSC) dataset after balancing and grouping the class categories. Five different training scenarios are used, and the tests show that the designed fusion model improved the performance by 3.3% compared to the individual models.

Keywords: machine learning, deep learning, cancer prediction, breast cancer, LSTM, fusion

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Authors: Mohsen Abotalebi Esfahani, Amin Rahimi

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Maintenance and management of route and roads infrastructure is one of the most important and the most fundamental principles of the countries. Several methods have been under investigation as preventive proceedings for the maintenance of asphalt pavements for many years. Using a mixture of salt, sand and gravel is the most common method of deicing, which could have numerous harmful consequences. Icy or snow-covered road is one of the major reasons of accidents in rainy seasons, which causes substantial damages such as loss of time and energy, environmental pollution, destruction of buildings, traffic congestion and rising possibility of accidents. Regarding this, every year the government incurred enormous costs to secure traverses. In this study, asphalt pavements have been cured, in terms of compressive strength, tensile strength and resilient modulus of asphalt samples, under the influence of Magnesium Chloride, Calcium Chloride, Sodium Chloride, Urea and pure water; and showed that de-icing with the calcium chloride solution and urea have the minimum negative effect and de-icing with pure water has most negative effect on laboratory specimens. Hence some simple techniques and new equipment and less use of sand and salt, can reduce significantly the risks and harmful effects of excessive use of salt, sand and gravel and at the same time use the safer roads.

Keywords: maintenance, sodium chloride, icyroad, calcium chloride

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Authors: Nagini Sabbineni, Rajini T. V. Kanth, B. V. Kiranmayee

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India’s economy primarily depends on agriculture yield growth and their allied agro industry products. The agriculture yield prediction is the toughest task for agricultural departments across the globe. The agriculture yield depends on various factors. Particularly countries like India, majority of agriculture growth depends on rain water, which is highly unpredictable. Agriculture growth depends on different parameters, namely Water, Nitrogen, Weather, Soil characteristics, Crop rotation, Soil moisture, Surface temperature and Rain water etc. In our paper, lot of Explorative Data Analysis is done and various predictive models were designed. Further various regression models like Linear, Multiple Linear, Non-linear models are tested for the effective prediction or the forecast of the agriculture yield for various crops in Andhra Pradesh and Telangana states.

Keywords: agriculture yield growth, agriculture yield prediction, explorative data analysis, predictive models, regression models

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Authors: Ahmad Saleem

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Ethereum is the second largest crypto currency in blockchain ecosystem. Along with standard transactions, it supports smart contracts and NFT’s. Current research trends are focused on analyzing the overall structure of the network its growth and behavior. Ethereum addresses are anonymous and can be created on fly. The nature of Ethereum network and addresses make it hard to predict their behavior. The activity period of an ethereum address is not much analyzed. Using machine learning we can make early prediction about the disposability of the address. In this paper we analyzed the lifetime of the addresses. We also identified and predicted the disposable addresses using machine learning models and compared the results.

Keywords: blockchain, Ethereum, cryptocurrency, prediction

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Authors: Youssouf Benmeriem

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Shear strength of sandy soils has been considered as the important parameter to study the stability of different civil engineering structures when subjected to monotonic, cyclic and earthquake loading conditions. The proposed research investigated the effect of grading characteristics on the shear strength and mechanical behavior of granular classes of sands mixed with silt in loose and dense states (Dr = 15% and 90%). The laboratory investigation aimed at understanding the extent or degree at which shear strength of sand-silt mixture soil is affected by its gradation under static loading conditions. For the purpose of clarifying and evaluating the shear strength characteristics of sandy soils, a series of Casagrande shear box tests were carried out on different reconstituted samples of sand-silt mixtures with various gradations. The soil samples were tested under different normal stresses (100, 200 and 300 kPa). The results from this laboratory investigation were used to develop insight into the shear strength response of sand and sand-silt mixtures under monotonic loading conditions. The analysis of the obtained data revealed that the grading characteristics (D10, D50, Cu, ESR, and MGSR) have significant influence on the shear strength response. It was found that shear strength can be correlated to the grading characteristics for the sand-silt mixture. The effective size ratio (ESR) and mean grain size ratio (MGSR) appear as pertinent parameters to predict the shear strength response of the sand-silt mixtures for soil gradation under study.

Keywords: grading characteristics, granular classes of sands, mechanical behavior, sand-silt, shear strength

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Authors: Mohammed A. Sakr, Tarek M. Khalifa, Walid N. Mansour

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External bonding of fiber reinforced polymer (FRP) plates to reinforced concrete (RC) beams is an effective technique for flexural strengthening. This paper presents an analytical parametric study on the behavior of RC continuous beams flexurally strengthened with externally bonded FRP plates on the upper and lower fibers, conducted using simple uniaxial nonlinear finite element model (UNFEM). UNFEM is able to estimate the load-carrying capacity, different failure modes and the interfacial stresses of RC continuous beams flexurally strengthened with externally bonded FRP plates on the upper and lower fibers. The study investigated the effect of five key parameters on the behavior and moment redistribution of FRP-reinforced continuous beams. The investigated parameters were the length of the FRP plate, the width and the thickness of the FRP plate, the ratio between the area of the FRP plate to the concrete area, the cohesive shear strength of the adhesive layer, and the concrete compressive strength. The investigation resulted in a number of important conclusions reflecting the effects of the studied parameters on the behavior of RC continuous beams flexurally strengthened with externally bonded FRP plates.

Keywords: continuous beams, parametric study, finite element, fiber reinforced polymer

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

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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, though 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: Oluwaponmile David Alao

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Due to an increase in the death rate as a result of heart attack. There is need to develop a system that can be useful in the diagnosis of the disease at the medical centre. This system will help in preventing misdiagnosis that may occur from the medical practitioner or the physicians. In this research work, heart disease dataset obtained from UCI repository has been used to develop an intelligent prediction diagnosis system. The system is modeled on a feedforwad neural network and trained with back propagation neural network. A recognition rate of 86% is obtained from the testing of the network.

Keywords: heart disease, artificial neural network, diagnosis, prediction system

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Authors: Mohammadjavad Yaghoubi, Arul Arulrajah, Mahdi M. Disfani, Suksun Horpibulsuk, Myint W. Bo, Stephen P. Darmawan

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This paper investigates the changes in the strength development of a high water content soft soil stabilised with alkaline activation of fly ash (FA) to use in deep soil mixing (DSM) technology. The content of FA was 20% by dry mass of soil, and the alkaline activator was sodium silicate (Na2SiO3). Samples were cured for 3, 7, 14, 28 and 56 days to evaluate the effect of curing time on strength development. To study the effect of adding slag (S) to the mixture on the strength development, 5% S was replaced with FA. In addition, the effect of the initial unit weight of samples on strength development was studied by preparing specimens with two different static compaction stresses. This was to replicate the field conditions where during implementing the DSM technique, the pressure on the soil while being mixed, increases with depth. Unconfined compression strength (UCS), scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) tests were conducted on the specimens. The results show that adding S to the FA based geopolymer activated by Na2SiO3 decreases the strength. Furthermore, samples prepared at a higher unit weight demonstrate greater strengths. Moreover, samples prepared at lower unit weight reached their final strength at about 14 days of curing, whereas the strength development continues to 56 days for specimens prepared at a higher unit weight.

Keywords: alkaline activation, curing time, fly ash, geopolymer, slag

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Authors: JingWei Yu, Hong Yang Yu

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At present, the increasingly serious air pollution in various cities of China has made people pay more attention to the air quality index(hereinafter referred to as AQI) of their living areas. To face this situation, it is of great significance to predict air pollution in heavily polluted areas. In this paper, based on the time series model of LSTM, a spatiotemporal prediction model of PM2.5 concentration in Mianyang, Sichuan Province, is established. The model fully considers the temporal variability and spatial distribution characteristics of PM2.5 concentration. The spatial correlation of air quality at different locations is based on the Air quality status of other nearby monitoring stations, including AQI and meteorological data to predict the air quality of a monitoring station. The experimental results show that the method has good prediction accuracy that the fitting degree with the actual measured data reaches more than 0.7, which can be applied to the modeling and prediction of the spatial and temporal distribution of regional PM2.5 concentration.

Keywords: LSTM, PM2.5, neural networks, spatio-temporal prediction

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Authors: Sadaf Sahar, Usman Qamar, Sadaf Ayaz

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In the software development lifecycle, the quality prediction techniques hold a prime importance in order to minimize future design errors and expensive maintenance. There are many techniques proposed by various researchers, but with the increasing complexity of the software lifecycle model, it is crucial to develop a flexible system which can cater for the factors which in result have an impact on the quality of the end product. These factors include properties of the software development process and the product along with its operation conditions. In this paper, a neural network (perceptron) based software quality prediction technique is proposed. Using this technique, the stakeholders can predict the quality of the resulting software during the early phases of the lifecycle saving time and resources on future elimination of design errors and costly maintenance. This technique can be brought into practical use using successful training.

Keywords: software quality, fuzzy logic, perception, prediction

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Authors: M. Sedlmajer, J. Zach, J. Hroudova, P. Rovnaníkova

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There are several possibilities of reducing the required amount of cement in concrete production. Natural zeolite is one of the raw materials which can partly substitute Portland cement. The effort to reduce the amount of Portland cement used in concrete production is brings both economical as well as ecological benefits. The paper presents the properties of concrete containing natural zeolite as an active admixture in the concrete which partly substitutes Portland cement. The properties discussed here bring information about the basic mechanical properties and frost resistance of concrete containing zeolite. The properties of concretes with the admixture of zeolite are compared with a reference concrete with no content of zeolite. The properties of the individual concretes are observed for 360 days.

Keywords: concrete, zeolite, compressive strength, modulus of elasticity, durability

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Authors: S. B. V. S. P. Sastry, V. V. S. Kesava Rao

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In this paper, x-ray impact of Taguchi method and design of experiment philosophy to project relationship between various factors leading to output yield strength of rebar is studied. In bar mill of an integrated steel plant, there are two production lines called as line 1 and line 2. The metallic properties e.g. yield strength of finished product of the same material is varying for a particular grade material when rolled simultaneously in both the lines. A study has been carried out to set the process parameters at optimal level for obtaining equal value of yield strength simultaneously for both lines.

Keywords: bar mill, design of experiment, taguchi, yield strength

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Authors: Gonzalez Carlos, Martinez Fransisco

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There are various types of analysis that allow us to involve seismic phenomena that cause strong requirements for structures that are designed by society; one of them is a probabilistic analysis which works from prediction equations that have been created based on metadata seismic compiled in different regions. These equations form models that are used to describe the 5% damped pseudo spectra response for the various zones considering some easily known input parameters. The biggest problem for the creation of these models requires data with great robust statistics that support the results, and there are several places where this type of information is not available, for which the use of alternative methodologies helps to achieve adjustments to different models of seismic prediction.

Keywords: GMPM, 5% damped pseudo-response spectra, models of seismic prediction, PSHA

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Authors: Shervin Alaei, Reza Moradi

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Trading in financial markets is subject to risks due to their high volatilities. Here, using an LSTM neural network, and by doing some risk-based feature engineering tasks, we developed a method that can accurately predict trends of the Tehran stock exchange market index from a few days ago. Our test results have shown that the proposed method with an average prediction accuracy of more than 94% is superior to the other common machine learning algorithms. To the best of our knowledge, this is the first work incorporating deep learning and risk factors to accurately predict market trends.

Keywords: deep learning, LSTM, trend prediction, risk management, artificial neural networks

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Authors: Shiva Kumar, G. S. Vijay, Srinivas Pai P., Shrinivasa Rao B. R.

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In the present study RBF neural networks were used for predicting the performance and emission parameters of a biodiesel engine. Engine experiments were carried out in a 4 stroke diesel engine using blends of diesel and Honge methyl ester as the fuel. Performance parameters like BTE, BSEC, Tech and emissions from the engine were measured. These experimental results were used for ANN modeling. RBF center initialization was done by random selection and by using Clustered techniques. Network was trained by using fixed and varying widths for the RBF units. It was observed that RBF results were having a good agreement with the experimental results. Networks trained by using clustering technique gave better results than using random selection of centers in terms of reduced MRE and increased prediction accuracy. The average MRE for the performance parameters was 3.25% with the prediction accuracy of 98% and for emissions it was 10.4% with a prediction accuracy of 80%.

Keywords: radial basis function networks, emissions, performance parameters, fuzzy c means

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Authors: Muhammad Zia ul Haq, Ong Kuan Boon, Jeffrey Low Fook Lee, Bendri Bin Dasril, Amna Iqbal, Muhammad Saleem

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This study is bond of two purpose, first is to establish the anthropometric profile of Malaysian cricket batsmen and second, to find the variances among the anthropometric characteristics of ten under-16 years, eight under-19 years and eight senior teams batsmen. The anthropometric variables were measured as 8 skinfolds, 12 circumferences, 06 lengths and 05 breadths, stature, sitting height, arm span, body mass, hand grip strength and leg strength. The batsmen of under-19 and under-16 found similar in skinfolds, sum of skinfolds, circumferences and breadth measurements but significantly lesser than the senior team batsmen. Senior and Under-19 batsmen were almost found similar in segmental lengths, heights and arm span but significantly higher than the under-16 batsmen. Breadth measurements the under-19 found higher than the senior and u-16 batsmen. The hand grips strength of the senior batsmen significantly high than the uder-19 and under-16 players and both groups were similar and no significant difference were found in leg strength of all three groups batsmen. Leg strength were found significant correlation with wrist, hip, thigh, and calf girth and handgrip strength. The hand grip strength were found correlated with all variables except biceps, mid-thigh skinfold, segmental length, humerus breadth. It is concluded from the present study that the girth segments and hand grip strength are the predictors of good performance in cricket batting.

Keywords: cricket batting, batsmen, anthropometry, body segments, hand grip strength

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Authors: Tahar Ayadat

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The undrained shear strength is an essential parameter for determining the consistency and the ultimate bearing capacity of a clay layer. The undrained shear strength can be determined by field tests such as the in situ vane test or in laboratory, including hand vane test, triaxial, simple compression test, and the consistency penetrometer (i.e. Swedish cone). However, the field vane test and the Swedish cone are the most commonly used tests by geotechnical experts. In this technical note, a comparison between the shear strength results obtained by the in situ vane test and the cone penetration test (Swedish cone) was conducted. A correlation between the results of these two tests, concerning the undrained shear strength of the Champlain sea clay, has been developed. Moreover, some applications of the proposed correlation on some geotechnical problems have been included, such as the determination of the consistency and the bearing capacity of a clay layer.

Keywords: correlation, shear strength, clay, vane test, Swedish cone

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