Prediction of recycled coarse aggregate concrete mechanical properties using multiple linear regression and artificial neural network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Suhas Vijay Patil ◽  
K. Balakrishna Rao ◽  
Gopinatha Nayak
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Coarse Aggregate ◽  
Recycled Aggregate ◽  
Training Phase ◽  
Content Type ◽  
Recycled Coarse Aggregate ◽  
Artificial Neural

Purpose Recycling construction waste is a promising way towards sustainable development in construction. Recycled aggregate (RA) is obtained from demolished concrete structures, laboratory crushed concrete, concrete waste at a ready mix concrete plant and the concrete made from RA is known as RA concrete. The purpose of this study is to apply multiple linear regressions (MLRs) and artificial neural network (ANN) to predict the mechanical properties, such as compressive strength (CS), flexural strength (FS) and split tensile strength (STS) of concrete at the age of 28 days curing made completely from the recycled coarse aggregate (RCA). Design/methodology/approach MLR and ANN are used to develop a prediction model. The model was developed in the training phase by using data from a previously published research study and a developed model was further tested by obtaining data from laboratory experiments. Findings ANN shows more accuracy than MLR with an R2-value of more than 0.8 in the training phase and 0.9 in a testing phase. The high R2-value indicates strong relation between the actual and predicted values of mechanical properties of RCA concrete. These models will help construction professionals to save their time and cost in predicting the mechanical properties of RCA concrete at 28 days of curing. Originality/value ANN with rectified linear unit transfer function and backpropagation algorithm for training is used to develop a prediction model. The outcome of this study is the prediction model for CS, FS and STS of concrete at 28 days of curing.

Identification of mechanical properties of adhesive joints using ANN

2017 ◽  
Vol 89 (6) ◽  
pp. 928-935
Author(s):  
Kamran Pazand ◽  
A.S. Nobari
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Natural Frequencies ◽  
Adhesive Joints ◽  
Nonlinear Frequency ◽  
Content Type ◽  
Linear And Nonlinear ◽  
Artificial Neural ◽  
Effective Mechanical Properties

Purpose This paper aims to present a new approach to the fast determination of the effective, dynamic, mechanical properties of an adhesive for linear and nonlinear regions of the adhesive response, for both healthy and damaged states of the bond. Design/methodology/approach The proposed approach is based on the measurement of the linear and nonlinear frequency response function (FRF) of adhesive-bonded structure and using artificial neural network identification technique. For this purpose, linear and nonlinear FRFs are measured for several single-lap joint specimens that are fabricated in healthy and damaged configurations of the bond. The measured FRFs of healthy and damaged specimens are then used to identify the natural frequencies of the specimens. The experimental natural frequencies, in turn, would be used to train artificial neural network (ANN) which would be able to predict the effective Young’s and shear moduli and damping of adhesive in healthy and damaged specimens, for any given excitation level and frequency, within the training domain. Findings Simultaneous identification of the effective mechanical properties of adhesive for linear and nonlinear response regions, as well as healthy and damages states of the adhesive bond. Practical implications The introduced method is effective to model the assembled structures with the viscoelastic adhesive joints, for linear and nonlinear regions. Originality/value A fast methodology, using ANN, for identification the effective mechanical properties of adhesives, compared to other methods for both linear and nonlinear regions.

Study on Performance of Recycled Aggregate Concrete

2011 ◽  
Vol 477 ◽  
pp. 280-289 ◽  
Author(s):  
Shao Wei Yao ◽  
Zhen Guo Gao ◽  
Chang Rui Wang
Keyword(s):  
Mechanical Properties ◽  
Coarse Aggregate ◽  
Physical And Mechanical Properties ◽  
Recycled Aggregate Concrete ◽  
Recycled Aggregate ◽  
Chemical Solution ◽  
Aggregate Concrete ◽  
Recycled Coarse Aggregate ◽  
Natural Aggregate ◽  
Strength And Durability

The properties of recycled coarse aggregate and the slump, the physical and mechanical properties and durability of recycled aggregate concrete were studied through tests. The results indicate that the slump, compressive strength and durability of concrete with recycled aggregate are lower than that of concrete with natural aggregate when recycled coarse aggregate fully absorbs water. However, the slump can be similar to that of concrete with natural aggregate. The properties of recycled aggregate concrete can be improved by strengthening the recycled coarse aggregate, and it is also found that the recycled coarse aggregate strengthened by grinding is superior to that soaked by chemical solution.

Dynamic prediction of Indian stock market: an artificial neural network approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Himanshu Goel ◽  
Narinder Pal Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stock Market ◽  
Institutional Investors ◽  
Time Series Data ◽  
Gradient Algorithm ◽  
Series Data ◽  
Content Type ◽  
Artificial Neural ◽  
Indian Stock Market

Purpose Artificial neural network (ANN) is a powerful technique to forecast the time series data such as the stock market. Therefore, this study aims to predict the Indian stock market closing price using ANNs. Design/methodology/approach The input variables identified from the literature are some macroeconomic variables and a global stock market factor. The study uses an ANN with Scaled Conjugate Gradient Algorithm (SCG) to forecast the Bombay Stock Exchange (BSE) Sensex. Findings The empirical findings reveal that the ANN model is able to achieve 93% accuracy in predicting the BSE Sensex closing prices. Moreover, the results indicate that the Morgan Stanley Capital International world index is the most important variable and the index of industrial production is the least important in predicting Sensex. Research limitations/implications The findings of the study have implications for the investors of all categories such as foreign institutional investors, domestic institutional investors and investment houses. Originality/value The novelty of this study lies in the fact that there are hardly any studies that use ANN to forecast the Indian stock market using macroeconomic indicators.

Bridging Precipitate Microstructure and Mechanical Hardness of Nickel-Based Superalloy Through Artificial Neural Network Model

2020 ◽  
Author(s):  
Yangping Li ◽  
Yangyi Liu ◽  
Sihua Luo ◽  
Zi Wang ◽  
Ke Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Cooling Rate ◽  
Volume Fraction ◽  
Sem Images ◽  
Mechanical Hardness ◽  
Nickel Based Superalloy ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract The attractive mechanical properties of nickel-based superalloys primarily arise from an assembly of γ′ precipitates with desirable size, volume fraction, morphology and spatial distribution. In addition, the solutioning cooling rate after super solvus heat treatment is critical for controlling the features of γ′ precipitates. However, the correlation between these multidimensional parameters and mechanical hardness has not been well established to date. Scanning electron microscope (SEM) images with different γ′ precipitates were investigated in this study, and artificial neural network (ANN) method was used to build a microstructure-mechanical property model. The critical step in this work is to extract different microstructural features from hundreds of SEM images. In order to improve the accuracy of prediction, the cooling rate was also considered as the input. In this work, the methodology was proved to be capable of bridging microstructural features and mechanical properties under the inspiration of material genome spirit.

Improving the electromagnetic compatibility of electronic products by using response surface methodology and artificial neural network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ching-Hsiang Chen ◽  
Chien-Yi Huang ◽  
Yan-Ci Huang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Electromagnetic Compatibility ◽  
Heat Dissipation ◽  
Optimal Parameter ◽  
Content Type ◽  
Early Stages ◽  
Artificial Neural

Purpose The purpose of this study is to use the Taguchi Method for parametric design in the early stages of product development. electromagnetic compatibility (EMC) issues can be considered in the early stages of product design to reduce counter-measure components, product cost and labor consumption increases due to a number of design changes in the R&D cycle and to accelerate the R&D process. Design/methodology/approach The three EMC characteristics, including radiated emission, conducted emission and fast transient impulse immunity of power, are considered response values; control factors are determined with respect to the relevant parameters for printed circuit board and mechanical design of the product and peripheral devices used in conjunction with the product are considered as noise factors. The optimal parameter set is determined by using the principal component gray relational analysis in conjunction with both response surface methodology and artificial neural network. Findings Market specifications and cost of components are considered to propose an optimal parameter design set with the number of grounded screw holes being 14, the size of the shell heat dissipation holes being 3 mm and the arrangement angle of shell heat dissipation holes being 45 degrees, to dispose of 390 O filters on the noise source. Originality/value The optimal parameter set can improve EMC effectively to accommodate the design specifications required by customers and pass test regulations.

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.

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