On the Multiple Linear Regression and Artificial Neural Networks for Strength Prediction of Soil-Based Controlled Low-Strength Material

2014 ◽  
Vol 597 ◽  
pp. 349-352 ◽  
Author(s):  
Li Jeng Huang ◽  
Yeong Nain Sheen ◽  
Duc Hien Le
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Prediction Models ◽  
Laboratory Data ◽  
Pulse Velocity ◽  
Strength Prediction ◽  
Strength Material ◽  
Artificial Neural ◽  
Low Strength ◽  
Controlled Low Strength Material

This paper presents two approaches, multiple linear regression (MLR) and artificial neural network (ANN), to develop predictive models for unconfined compressive strength of soil-based controlled low-strength material (CLSM). Our obtained laboratory data conducting on the soil-based CLSM were employed for analysis. Two strength prediction models were proposed: (1) strength is assumed to be a function of mix proportion and curing period; and (2) it is estimated from measured ultrasonic pulse velocity combined with effect of mixture parameters and curing ages. In each model, three predicted formulas were developed; one from MLR and two from ANN. It was showed that all the proposed equations have a well-predicted capacity.

scholarly journals Prediction of Blended Yarn Evenness and Tensile Properties by Using Artificial Neural Network and Multiple Linear Regression

2016 ◽  
Vol 16 (2) ◽  
pp. 43-50 ◽  
Author(s):  
Samander Ali Malik ◽  
Assad Farooq ◽  
Thomas Gereke ◽  
Chokri Cherif
Keyword(s):  
Neural Networks ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Tensile Properties ◽  
Regression Models ◽  
Prediction Models ◽  
Research Work ◽  
Absolute Error ◽  
Knowledge Domain ◽  
Artificial Neural

Abstract The present research work was carried out to develop the prediction models for blended ring spun yarn evenness and tensile parameters using artificial neural networks (ANNs) and multiple linear regression (MLR). Polyester/cotton blend ratio, twist multiplier, back roller hardness and break draft ratio were used as input parameters to predict yarn evenness in terms of CVm% and yarn tensile properties in terms of tenacity and elongation. Feed forward neural networks with Bayesian regularisation support were successfully trained and tested using the available experimental data. The coefficients of determination of ANN and regression models indicate that there is a strong correlation between the measured and predicted yarn characteristics with an acceptable mean absolute error values. The comparative analysis of two modelling techniques shows that the ANNs perform better than the MLR models. The relative importance of input variables was determined using rank analysis through input saliency test on optimised ANN models and standardised coefficients of regression models. These models are suitable for yarn manufacturers and can be used within the investigated knowledge domain.

scholarly journals Development of artificial neural network and multiple linear regression models in the prediction process of the hot mix asphalt properties

2017 ◽  
Vol 44 (12) ◽  
pp. 994-1004 ◽  
Author(s):  
Ivica Androjić ◽  
Ivan Marović
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Regression Models ◽  
Prediction Models ◽  
Hot Mix Asphalt ◽  
Linear Regression Models ◽  
Artificial Neural ◽  
Multiple Linear Regression Models

The oscillation of asphalt mix composition on a daily basis significantly affects the achieved properties of the asphalt during production, thus resulting in conducting expensive laboratory tests to determine existing properties and predicting the future results. To decrease the amount of such tests, a development of artificial neural network and multiple linear regression models in the prediction process of predetermined dependent variables air void and soluble binder content is presented. The input data were obtained from a single laboratory and consists of testing 386 mixes of hot mix asphalt (HMA). It was found that it is possible and desirable to apply such models in the prediction process of the HMA properties. The final aim of the research was to compare results of the prediction models on an independent dataset and analyze them through the boundary conditions of technical regulations and the standard EN 13108-21.

Towards an Adaptive Artificial Neural Network based Postprocessor

2021 ◽  
Author(s):  
Paul J. Roebber
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Network Architecture ◽  
Prediction Models ◽  
Weather Prediction ◽  
Training Data ◽  
Dynamical Model ◽  
Artificial Neural

AbstractWe introduce an adaptive form of postprocessor where algorithm structures are neural networks where the number of hidden nodes and the network training features evolve. Key potential advantages of this system are the flexible, nonlinear mapping capabilities of neural networks and, through backpropagation, the ability to rapidly establish capable predictors in an algorithm population. The system can be implemented after one initial training process and future changes to postprocessor inputs (new observations, new inputs or model upgrades) are incorporated as they become available. As in prior work, the implementation in the form of a predator-prey ecosystem allows for the ready construction of ensembles. Computational requirements are minimal, and the use of a moving data window means that data storage requirements are constrained.The system adds predictive skill to a demonstration dynamical model representing the hemispheric circulation, with skill competitive with or exceeding that obtainable from multiple linear regression and standard artificial neural networks constructed under typical operational limitations. The system incorporates new information rapidly and the dependence of the approach on the training data size is similar to multiple linear regression. A loss of performance occurs relative to a fixed neural network architecture in which only the weights are adjusted after training, but this loss is compensated for by gains from the ensemble predictions. While the demonstration dynamical model is complex, current numerical weather prediction models are considerably more so, and thus a future step will be to apply this technique to operational weather forecast data.

scholarly journals Predicting Particulate Matter (PM2.5) in Malaysia using Multiple Linear Regression and Artificial Neural Network

2021 ◽  
Vol 2084 (1) ◽  
pp. 012010
Author(s):  
Norafefah Mohamad Sobri ◽  
Wan Fairos Wan Yaacob ◽  
Nor Azima Ismail ◽  
Mohd Azry Abdul Malik ◽  
Raudhah Ab. Rahman ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Trend Analysis ◽  
Quality Index ◽  
Prediction Models ◽  
Ann Model ◽  
Particulate Size ◽  
Artificial Neural ◽  
Better Than

Abstract Air pollution is a well-known issue for all countries, including Malaysia. It has been stated that particulate matter that less than 2.5mm known as PM2.5 has a greater effect on health as the smaller particulate size can penetrate deep into the respiratory system and affect the cardiovascular system significantly. Therefore, it is necessary to estimate the concentration of PM2.5 for haze precautions. This study characterizes the pattern of PM2.5 concentrations involving seven stations including Alor Setar, Shah Alam, Pasir Gudang, Ipoh, Kuantan, Kuala Terengganu and Miri with seven indicator parameters (Carbon Monoxide, Ozone, Sulphur Dioxide, Nitrogen Dioxide, Humidity, Temperature and Wind Speed). PM2.5 concentrations were predicted for each station using Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN). Descriptive and trend analysis using Mann-Kandell Trend analysis was used to describe the haze characteristics and identify significant trends in the haze selected locations in Malaysia. MLR and ANN were fitted for the data. The performance of both prediction models was compared based on R2 and Mean Square Error (MSE). The results show ANN performed better than MLR with a high value of coefficient determination (R2) and low error measure. The ANN model was used to predict the occurrence of haze for the next day in the Air Quality Index (API).

scholarly journals Predictive modelling of soils’ hydraulic conductivity using artificial neural network and multiple linear regression

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Charles Gbenga Williams ◽  
Oluwapelumi O. Ojuri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydraulic Conductivity ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Coarse Grained ◽  
Soil Types ◽  
Wide Range ◽  
Artificial Neural ◽  
Input Variables

AbstractAs a result of heterogeneity nature of soils and variation in its hydraulic conductivity over several orders of magnitude for various soil types from fine-grained to coarse-grained soils, predictive methods to estimate hydraulic conductivity of soils from properties considered more easily obtainable have now been given an appropriate consideration. This study evaluates the performance of artificial neural network (ANN) being one of the popular computational intelligence techniques in predicting hydraulic conductivity of wide range of soil types and compared with the traditional multiple linear regression (MLR). ANN and MLR models were developed using six input variables. Results revealed that only three input variables were statistically significant in MLR model development. Performance evaluations of the developed models using determination coefficient and mean square error show that the prediction capability of ANN is far better than MLR. In addition, comparative study with available existing models shows that the developed ANN and MLR in this study performed relatively better.

Properties of lightweight alkali activated controlled Low-Strength material using calcium carbide residue – Fly ash mixture and containing EPS beads

2021 ◽  
Vol 297 ◽  
pp. 123769
Author(s):  
Saofee Dueramae ◽  
Sasipim Sanboonsiri ◽  
Tanvarat Suntadyon ◽  
Bhassakorn Aoudta ◽  
Weerachart Tangchirapat ◽  
...  
Keyword(s):  
Fly Ash ◽  
Calcium Carbide ◽  
Calcium Carbide Residue ◽  
Strength Material ◽  
Low Strength ◽  
Controlled Low Strength Material ◽  
Alkali Activated
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