Applying Back-Propagation Neural Network for Estimating the Slump of Concrete

2013 ◽  
Vol 651 ◽  
pp. 986-989
Author(s):  
Chin Ming Kao ◽  
Li Chen ◽  
Chang Huan Kou ◽  
Shih Wei Ma
Keyword(s):  
Neural Network ◽  
High Performance ◽  
High Performance Concrete ◽  
Linear Regression Analysis ◽  
Back Propagation ◽  
Nonlinear Function ◽  
Multiple Linear Regression Analysis ◽  
Back Propagation Neural Network ◽  
Fine Aggregate ◽  
Furnace Slag

This paper proposes the back-propagation neural network (BPN) and applies it to estimate the slump of high-performance concrete (HPC). It is known that HPC is a highly complex material whose behaviour is difficult to model, especially for slump. To estimate the slump, it is a nonlinear function of the content of all concrete ingredients, including cement, fly ash, blast furnace slag, water, superplasticizer, and coarse and fine aggregate. Therefore, slump estimation is set as a function of the content of these seven concrete ingredients and additional four important ratios. The results show that BPN obtains a more accurate mathematical equation through learning procedures which outperforms the traditional multiple linear regression analysis (RA), with lower estimating errors for predicting the HPC slump.

Modeling Slump Flow of High-Performance Concrete Using a Back-Propagation Network

2010 ◽  
Vol 20-23 ◽  
pp. 838-842 ◽  
Author(s):  
Wen Huan Chien ◽  
Li Chen ◽  
Chih Chiang Wei ◽  
Hsun Hsin Hsu ◽  
Tai Sheng Wang
Keyword(s):  
Blast Furnace Slag ◽  
High Performance ◽  
High Performance Concrete ◽  
Back Propagation ◽  
Fine Aggregate ◽  
Flow Estimation ◽  
Slump Flow ◽  
Complex Relationships ◽  
Propagation Network ◽  
Furnace Slag

This paper proposes a back-propagated network (BPN) and applies it to estimate the slump flow of high-performance concrete (HPC). HPC is a highly complex material whose behavior is difficult to model, especially slump flow. Slump flow estimation is a function of the content of all concrete ingredients, including cement, fly ash, blast furnace slag, water, superplasticizer, and coarse and fine aggregate. BPN is a well-known method, used to automatically discover the complex relationships among nonlinear systems. The results show that BPN predicts the slump flow of HPC with satisfyied estimating errors.

scholarly journals A Pattern Recognition Model of Python Programming using Artificial Neural Network via NeMo

2020 ◽  
Vol 8 (5) ◽  
pp. 109-113
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Programming Languages ◽  
Programming Language ◽  
High Performance ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Network Simulator ◽  
Source Codes ◽  
Python Programming

Background/Objectives: In the field of software development, the diversity of programming languages increases dramatically with the increase in their complexity. This leads both programmers and researchers to develop and investigate automated tools to distinguish these programming languages. Different efforts were conducted to achieve this task using keywords of source codes of these programming languages. Therefore, instead of using keywords classification for recognition, this work is conducted to investigate the ability to detect the pattern of a programming language characteristic by using NeMo(High-performance spiking neural network simulator) of neural network and testing the ability of this toolkit to provide detailed analyzable results. Methods/Statistical analysis: the method of achieving these objectives is by using a back propagation neural network via NeMo based on pattern recognition methodology. Findings: The results show that the NeMo neural network of pattern recognition can identify and recognize the pattern of python programming language with high accuracy. It also shows the ability of the NeMo toolkit to represent the analyzable results through a percentage of certainty. Improvements/Applications: it can be noticed from the results the ability of NeMo simulator to provide beneficial platform for studying and analyzing the complexity of the backpropagation neural network model.

COMPARISON OF MULTIPLE LINEAR REGRESSION AND ARTIFICIAL NEURAL NETWORKS IN PREDICTING OCTANOL/WATER PARTITION COEFFICIENT OF A VARIETY OF ORGANIC MOLECULES

2003 ◽  
Vol 02 (03) ◽  
pp. 335-344 ◽  
Author(s):  
SEIFOLLAH JALILI ◽  
MOHSEN TAFAZZOLI ◽  
MEHDI JALALI-HERAVI
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Organic Compounds ◽  
Partition Coefficients ◽  
Linear Regression Analysis ◽  
Back Propagation ◽  
Multiple Linear Regression Analysis ◽  
Back Propagation Neural Network ◽  
Log P ◽  
Data Set

For estimating log P values of a group of organic compounds, a back-propagation neural network with a 9–6–1 architecture was developed with optimal learning rate (ε) and momentum (μ) of 0.24 and 0.82, respectively. A collection of 131 organic compounds was chosen as data set that consists of normal hydrocarbons, alcohols, ethers, amines, ketones, acids, benzene derivatives, phenols, and aldehydes. The data set was divided into a training set consisting of 118 molecules and a prediction set consisting of 18 molecules. The most important properties that affect the partition coefficients of organic compounds (surface/volume, dipole moment, and those which are related to electrostatic potentials such as the sum of charges on the carbon atoms) were used as descriptors. These descriptors were obtained using AM1 semiempirical MO method for the gas phase geometries. The descriptors were selected via developing a multiple linear regression analysis. The ANN calculated values of partition coefficients (log Ps) for molecules of the training and prediction sets are in good agreement with the experimental values.

Estimating Distribution of Concrete Strength Using Quantile Regression Neural Networks

2014 ◽  
Vol 584-586 ◽  
pp. 1017-1025 ◽  
Author(s):  
I Cheng Yeh
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Quantile Regression ◽  
Blast Furnace Slag ◽  
High Performance ◽  
Coarse Aggregate ◽  
High Performance Concrete ◽  
Concrete Strength ◽  
Fine Aggregate ◽  
Furnace Slag

This paper is aimed at demonstrating the possibilities of adaptingQuantile Regression Neural Network (QRNN) to estimate the distribution ofcompressive strength of high performance concrete (HPC). The databasecontaining 1030 compressive strength data were used to evaluate QRNN. Each dataincludes the amounts of cement, blast furnace slag, fly ash, water,superplasticizer, coarse aggregate, fine aggregate (in kilograms per cubicmeter), the age, and the compressive strength. This study led to the followingconclusions: (1) The Quantile Regression Neural Networks can buildaccurate quantile models and estimate the distribution of compressive strengthof HPC. (2) The various distributions of prediction of compressive strength of HPCshow that the variance of the error is inconstant across observations, whichimply that the prediction is heteroscedastic. (3) The logarithmic normaldistribution may be more appropriate than normal distribution to fit thedistribution of compressive strength of HPC. Since engineers should not assumethat the variance of the error of prediction of compressive strength isconstant, the ability of estimating the distribution of compressive strength ofHPC is an important advantage of QRNN.

scholarly journals Predicting Carbonation Depth of Prestressed Concrete under Different Stress States Using Artificial Neural Network

2009 ◽  
Vol 2009 ◽  
pp. 1-8 ◽  
Author(s):  
Chunhua Lu ◽  
Ronggui Liu
Keyword(s):  
Neural Network ◽  
Stress Level ◽  
Prestressed Concrete ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Fine Aggregate ◽  
Carbonation Depth ◽  
Carbonation Process ◽  
Artificial Neural ◽  
Stress States

Two artificial neural networks (ANN), back-propagation neural network (BPNN) and the radial basis function neural network (RBFNN), are proposed to predict the carbonation depth of prestressed concrete. In order to generate the training and testing data for the ANNs, an accelerated carbonation experiment was carried out, and the influence of stress level of concrete on carbonation process was taken into account especially. Then, based on the experimental results, the BPNN and RBFNN models which all take the stress level of concrete, water-cement ratio, cement-fine aggregate, cement-coarse aggregate ratio and testing age as input parameters were built and all the training and testing work was performed in MATLAB. It can be found that the two ANN models seem to have a high prediction and generalization capability in evaluation of carbonation depth, and the largest absolute percentage errors of BPNN and RBFNN are 10.88% and 8.46%, respectively. The RBFNN model shows a better prediction precision in comparison to BPNN model.

Developing a multi stage predicting system for corporate credit rating in emerging markets

2014 ◽  
Vol 27 (4) ◽  
pp. 475-487 ◽  
Author(s):  
Dana Al-Najjar ◽  
Basil Al-Najjar
Keyword(s):  
Neural Network ◽  
High Performance ◽  
Credit Rating ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Content Type ◽  
Corporate Credit ◽  
Multi Stage ◽  
Financial Characteristics ◽  
Corporate Credit Rating

Purpose – The purpose of this paper is to build a neural network system to predict corporate credit rating in Jordanian non-financial firms, using 19 different financial characteristics such as profitability, leverage ratios, liquidity, bankruptcy, and sales performance. Design/methodology/approach – The study adopts two neural network techniques namely, Kohonen network and Back Propagation Neural Network (BPNN). Our sample includes the manufacturing firms that have provided the required financial information for the period from 2000 to 2007. Findings – BPNN has successfully predicted firms with high performance gaining A rating and the bankrupted firms with D rating for the period from 2005 to 2007. Originality/value – This study is the first study to investigate credit rating in Jordan using Neural Network technique.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

Effect of Copper Slag as a Fine Aggregate on the Durability Characteristics of High-Performance Concrete (HPC) Containing Silica Fume

2020 ◽  
Vol 07 (02) ◽  
pp. 1-10
Author(s):  
Rizwan Ahmad Khan ◽  
Keyword(s):  
Silica Fume ◽  
High Performance ◽  
High Performance Concrete ◽  
Copper Slag ◽  
Chloride Penetration ◽  
Interfacial Transition Zone ◽  
Fine Aggregate ◽  
Fresh Properties ◽  
Fine Aggregates ◽  
Effect Of Copper

This paper investigates the fresh and durability properties of the high-performance concrete by replacing cement with 15% Silica fume and simultaneously replacing fine aggregates with 25%, 50%, 75% and 100% copper slag at w/b ratio of 0.23. Five mixes were analysed and compared with the standard concrete mix. Fresh properties show an increase in the slump with the increase in the quantity of copper slag to the mix. Sorptivity, chloride penetration, UPV and carbonation results were very encouraging at 50% copper slag replacement levels. Microstructure analysis of these mixes shows the emergence of C-S-H gel for nearly all mixes indicating densification of the interfacial transition zone of the concrete.

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