scholarly journals Performance evaluation of mechanical properties of self-compacting concrete using artificial neural network

2020 ◽  
Vol 9 (1) ◽  
pp. 104
Author(s):  
Arabi N.S.Al qadi ◽  
Madhar Haddad
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Mineral Admixtures ◽  
Test Results ◽  
Self Compacting Concrete ◽  
Predictive Capacity ◽  
Aggre Gate ◽  
The Neural Network ◽  
Hardened Properties ◽  
Local Materials

This experimental study was undertaken to investigate the effects of using local materials (cement, fly ash, super-plasticizer, coarse aggre-gate, and sand) on the mechanical properties of Self-Compacting Concrete (SCC). For this purpose, a total of 31 mixtures of SCC were prepared by the neural network design methods. Furthermore, based on the experimental results, the neural network model-based clear for-mulations were developed to predict the mechanical properties of SCC. The test results have shown that mineral admixtures were very effective on hardened properties of SCC. In addition, it was found that the developed model by using neural network appeared to have a high predictive capacity of hardened properties of SCC with respect to regression and experimental.  

A Model on the Correlation between Composition and Mechanical Properties of Mg-Al-Zn Alloys by Using Artificial Neural Network

2005 ◽  
Vol 488-489 ◽  
pp. 793-796 ◽  
Author(s):  
Hai Ding Liu ◽  
Ai Tao Tang ◽  
Fu Sheng Pan ◽  
Ru Lin Zuo ◽  
Ling Yun Wang
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Magnesium Alloys ◽  
Tensile Yield Strength ◽  
Data Set ◽  
The Neural Network ◽  
Artificial Neural ◽  
Prediction Of Mechanical Properties ◽  
Artificial Neural Network Ann

A model was developed for the analysis and prediction of correlation between composition and mechanical properties of Mg-Al-Zn (AZ) magnesium alloys by applying artificial neural network (ANN). The input parameters of the neural network (NN) are alloy composition. The outputs of the NN model are important mechanical properties, including ultimate tensile strength, tensile yield strength and elongation. The model is based on multilayer feedforward neural network. The NN was trained with comprehensive data set collected from domestic and foreign literature. A very good performance of the neural network was achieved. The model can be used for the simulation and prediction of mechanical properties of AZ system magnesium alloys as functions of composition.

Design and Realization of Experimental Autonomous Driving System Based on Neural Network Control

2012 ◽  
Vol 241-244 ◽  
pp. 1953-1958
Author(s):  
Qing Fu Kong ◽  
Fan Ming Zeng ◽  
Jie Chang Wu ◽  
Jia Ming Wu
Keyword(s):  
Neural Network ◽  
Autonomous Driving ◽  
Network Control ◽  
Neural Network Control ◽  
Test Results ◽  
Driving System ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Autonomous Driving System

Intelligent vehicle is an attractive solution to the traffic problems caused by automobiles. An experimental autonomous driving system based on a slot car set is designed and realized in the paper. With the application of a wireless camera equipped on the slot car, the track information is acquired and sent to the controlling computer. A backpropogation (BP) neural network controller is built to imitate the way of player’s thinking. After being trained, the neural network controller can give the proper voltage instructions to the direct current (DC) motor of the slot car according to different track conditions. Test results prove that the development of the autonomous driving system is successful.

The Effect of Polypropylene Fiber on the Mechanical Properties of Self-Compacting Concrete

2010 ◽  
Vol 168-170 ◽  
pp. 1325-1329
Author(s):  
Ye Ran Zhu ◽  
Jun Cai ◽  
Dong Wang ◽  
Guo Hong Huang
Keyword(s):  
Mechanical Properties ◽  
Tensile Strength ◽  
Compressive Strength ◽  
Polypropylene Fiber ◽  
Flexural Properties ◽  
Test Results ◽  
Self Compacting Concrete ◽  
Bending Tests ◽  
Flexural Toughness ◽  
Four Point Bending

This paper investigates the mechanical properties (compressive strength, splitting tensile strength and flexural toughness) of polypropylene fiber reinforced self-compacting concrete (PFRSCC). The effect of the incorporation of polypropylene fiber on the mechanical properties of PFRSCC is determined. Four point bending tests on beam specimens were performed to evaluate the flexural properties of PFRSCC. Test results indicate that flexural toughness and ductility are remarkably improved by the addition of polypropylene fiber.

BEHAVIOR OF M 50 GRADE SELF-COMPACTING CONCRETE DEVELOPED USING PORTLAND SLAG CEMENT AND METAKAOLIN

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Sravya Nalla ◽  
Janardhana Maganti ◽  
Dinakar Pasla
Keyword(s):  
Compressive Strength ◽  
Mineral Admixtures ◽  
Partial Replacement ◽  
Test Results ◽  
Slag Cement ◽  
Self Compacting Concrete ◽  
Destructive Testing ◽  
Compressive Strength Test ◽  
Portland Slag Cement ◽  
Strength And Durability

Self-compacting concrete (SCC) is a revolutionary development in concrete construction. The addition of mineral admixtures like metakaolin, which is a highly reactive pozzolana to the SCC mixes, gives it superior strength and durability. The present work is an effort to study the behavior of M50 grade SCC by partial replacement of Portland Slag Cement (PSC) with metakaolin. Its strength and durability aspects are comparable with a controlled concrete (without replacement of cement). In the present work, a new mix design methodology based on the efficiency of metakaolin is adopted. The optimum percentage replacement of cement with metakaolin is obtained based on compressive strength test results. The influence of metakaolin on the workability, compressive strength, splitting tensile strength and flexural strength of SCC and its behavior when subjected to elevated temperature was investigated through evaluation against controlled concrete and non-destructive testing. From the test results, it was observed that incorporation of metakaolin at an optimum dosage satisfied all the fresh properties of SCC and improved both the strength and durability performance of SCC compared to controlled concrete.

scholarly journals The use of the Neural Network in Predicting a Number of Diseases of the Gastrointestinal Tract caused by Parasites

2020 ◽  
pp. 9-19
Author(s):  
U. N. Musevi ◽  
K. S. Pashayeva ◽  
N. T. Abdullayev
Keyword(s):  
Neural Network ◽  
Gastrointestinal Tract ◽  
Functional State ◽  
Disease Prediction ◽  
Network Technology ◽  
Test Results ◽  
The Neural Network ◽  
Neural Network Technology ◽  
Inputs And Outputs

Disorders of the functional state of the gastrointestinal tract associated with the influence of various parasites are considered. The symptoms of diseases caused by parasites and their location in the gastrointestinal tract are given. The possibility of using neural network technology in diagnosing diseases as a result of the influence of various parasites is estimated. The structure of the neural network is given, indicating the set of inputs and outputs, as well as the result of training the network. For the created neural network, test results for the corresponding symptoms and disease prediction results for these symptoms were obtained.

scholarly journals The Effects of Acidic and Basic Pumice on Physico-Mechanical and Durability Properties of Self-Compacting Concretes

2020 ◽  
pp. 21-36
Author(s):  
Hasan Erhan Yücel ◽  
Hatice Öznur Öz ◽  
Muhammet Güneş
Keyword(s):  
Mechanical Properties ◽  
Modulus Of Elasticity ◽  
Correlation Coefficients ◽  
Crushed Stone ◽  
Test Results ◽  
Fresh Properties ◽  
Limit Values ◽  
Durability Properties ◽  
Slump Flow ◽  
Hardened Properties

In this study, properties of self-compacting concretes (SCCs) containing acidic and basic pumice (AP-BP) was investigated. SCCs incorporating AP-BP (SCCAs-SCCBs) were produced with constant slump flow diameter of 720±20 mm and 690±20 mm by adjusting superplasticizer (SP), respectively. Control mixture was designed with totally crushed stone aggregate. SCCAs and SCCBs could be produced up to 100% coarse AP with 20% increments and 60% coarse BP with 10% increments, respectively, to ensure the desired limit values for SCC. Firstly, fresh properties of SCCs were determined. Then, the mechanical and durability properties of SCCs were measured at 28 and 56 days. Test results indicated that workability properties of SCCAs are markedly higher than that of SCCBs. Additionally, mechanical and durability performances of SCCs decreased with increasing of AP and BP. The compressive strengths of SCCs containing 60% AP and BP decreased approximately 28-29% and 22-24%, compared to the control mixture, respectively. Similarly, modulus of elasticity of same mixtures decreased around 35-39% and 17-19%, respectively. However, all results indicated that SCCs produced with AP and BP provided the available limits in the design of SCC. Additionally, SCCBs exhibited higher performance than SCCAs in terms of hardened properties. Moreover, high correlation coefficients (R2>0.89) between the durability and mechanical properties were found for SCCs.

scholarly journals IMPACT OF USING STONE POWDER AND MINERAL ADMIXTURES IN HIGH STRENGTH CONCRETE

2020 ◽  
Vol 4 (5) ◽  
pp. 82-87
Author(s):  
Afzal Basha Syed ◽  
Jayarami Reddy B ◽  
Sashidhar C
Keyword(s):  
Mechanical Properties ◽  
Tensile Strength ◽  
High Strength Concrete ◽  
High Strength ◽  
Mineral Admixtures ◽  
Test Results ◽  
Concrete Technology ◽  
Split Tensile Strength ◽  
Conventional Concrete ◽  
Strength Concrete

In present era, high-strength concrete is progressively utilized in modern concrete technology and particularly in the construction of elevated structures. This examination has been directed to explore the properties of high-strength concrete that was delivered by using stone powder (SP) as an option of extent on sand after being processed. The aim of the research is to study the effect of replacement of sand with stone powder and substitution of cement with mineral admixtures (GGBS & Zeolite) on the mechanical properties of high strength concrete. The test results showed clear improvement in compression and split tensile nature of concrete by using stone powder and mineral admixtures together in concrete. The increment in the magnitude of compressive strength and split tensile strength are comparable with conventional concrete.

scholarly journals Identification of Rice Variety Using Geometric Features and Neural Network

2019 ◽  
Vol 13 (3) ◽  
pp. 301
Author(s):  
Wahyu Srimulyani ◽  
Aina Musdholifah
Keyword(s):  
Neural Network ◽  
Rice Variety ◽  
Physical Characteristics ◽  
Training Data ◽  
Test Results ◽  
Trial And Error ◽  
Rice Varieties ◽  
Segmentation Evaluation ◽  
The Neural Network

 Indonesia has many food varieties, one of which is rice varieties. Each rice variety has physical characteristics that can be recognized through color, texture, and shape. Based on these physical characteristics, rice can be identified using the Neural Network. Research using 12 features has not optimal results. This study proposes the addition of geometry features with Learning Vector Quantization and Backpropagation algorithms that are used separately.The trial uses data from 9 rice varieties taken from several regions in Yogyakarta. The acquisition of rice was carried out using a camera Canon D700 with a kit lens and maximum magnification, 55 mm. Data sharing is carried out for training and testing, and the training data was sharing with the quality of the rice. Preprocessing of data was carried out before feature extraction with the trial and error thresholding process of segmentation. Evaluation is done by comparing the results of the addition of 6 geometry features and before adding geometry features.The test results show that the addition of 6 geometry features gives an increase in the value of accuracy. This is evidenced by the Backpropagation algorithm resulting in increased accuracy of 100% and 5.2% the result of the LVQ algorithm.

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