PREDICTION OF THE COMPRESSIVE STRENGTH OF ENVIRONMENTALLY FRIENDLY CONCRETE USING ARTIFICIAL NEURAL NETWORK

The paper evaluated the possibility of using artificial neural network models for predicting the compressive strength (Fc) of concretes with  the addition of recycled concrete aggregate (RCA). The artificial neural network (ANN) approaches were used for three variable processes modeling (cement content in the range of 250 to 400 kg/m3, percentage of recycled concrete aggregate from 25% to 100% and the ratios of water contents  0.45 to 0.6). The results indicate that the compressive strength of recycled concrete at 3, 7 and 28 days is strongly influenced by the cement content, %RCA and the ratios of water contents. It is found that the compressive strength at 3, 7 and 28 days decreases when increasing RCA from 25% to 100%. The obtained MLP and RBF networks are characterized by satisfactory capacity for prediction of the compressive strength of concretes with recycled concrete aggregate (RCA) addition. The results in statistical terms; correlation coefficient (R) reveals that the both ANN approaches are powerful tools for the prediction of the compressive strength.&nbsp

Material Cost Prediction for Jewelry Production Using Deep Learning Technique

Production cost management is a key factor to increase industrial competitiveness. The precious metals and the gemstones comprise 65% of the jewelry material cost. Managing raw material cost is a challenging task especially when the price highly fluctuates. In this article, deep learning models were proposed to predict the prices of main raw materials of jewelry which are silver, gold, and diamond. These models are designed for Thai jewelry manufactures which are mostly small businesses. Therefore, our models only consider historical price data. This is because small businesses usually do not have access to other relevant data, i.e. oil prices and other economic data. The proposed precious metal price model can provide prediction with RMSE of 0.00765 which is comparable to other models in literature while requires less data and offers a simpler model. Also, the proposed diamond price model can provide RMSE of 0.0181 which is 42.41% improvement from the model normally used by jewelry manufacturers. In addition to the raw material price prediction model, a quantization method of diamond 4C grade is proposed and validated statistically and visually. This quantization method could be used in a diamond analysis

Predicting The Strength Properties of Self Healing Concrete Using Artificial Neural Network

An extensive simulation program is used in this study to discover the best ANN model for predicting the compressive strength of concrete with respect to the percentage of mineral admixture and percentage of crystalline admixture. To accomplish this, an experimental database of 100 samples is compiled from the literature and utilized to find the best ANN architecture. The main aim of this paper was to predict the strength properties of self-healing concrete (SHC) with crystalline admixture and different mineral admixtures using an artificial neural network (ANN). The samples, 100 in Number, with different mixes, were analyzed after 28 days of curing of the samples. ANN was fed with the experimental data containing four input parameters: mineral admixture (MA), percentage of mineral admixture (PMA), Percentage of crystalline admixture (PCA), and type of exposure (TE). Correspondingly, strength (Fc) was the output parameter. The experimental data showed a good correlation with the values predicted by ANN. In conclusion, ANN could be used to accurately evaluate SHC strength characteristics

Case-based reasoning approach to estimating the strength of sustainable concrete

Continuing from previous studies of sustainable concrete containing environmentally friendly materials and existing modeling approach to predicting concrete properties, this study developed an estimation methodology to predicting the strength of sustainable concrete using an advanced case-based reasoning approach. It was conducted in two steps: (i) establishment of a case database and (ii) development of an advanced case-based reasoning model. Through the experimental studies, a total of 144 observations for concrete compressive strength and tensile strength were established to develop the estimation model. As a result, the prediction accuracy of the A-CBR model (i.e., 95.214% for compressive strength and 92.448% for tensile strength) performed superior to other conventional methodologies (e.g., basic case-based reasoning and artificial neural network models). The developed methodology provides an alternative approach in predicting concrete properties and could be further extended to the future research area in durability of sustainable concrete

Ensemble of M5 Model Tree Based Modelling of Sodium Adsorption Ratio

This work reports the results of four ensemble approaches with the M5 model tree as the base regression model to anticipate Sodium Adsorption Ratio (SAR). Ensemble methods that combine the output of multiple regression models have been found to be more accurate than any of the individual models making up the ensemble. In this study additive boosting, bagging, rotation forest and random subspace methods are used. The dataset, which consisted of 488 samples with nine input parameters were obtained from the Barandoozchay River in West Azerbaijan province, Iran. Three evaluation criteria: correlation coefficient, root mean square error and mean absolute error were used to judge the accuracy of different ensemble models. In addition to the use of M5 model tree to predict the SAR values, a wrapper-based variable selection approach using a M5 model tree as the learning algorithm and a genetic algorithm, was also used to select useful input variables. The encouraging performance motivates the use of this technique to predict SAR values

COMPARING VARIOUS MACHINE LEARNING METHODS FOR PREDICTION OF PATIENT REVISIT INTENTION: A CASE STUDY

Many techniques have been proposed for analysis of costumer intention, from surveys to statistical models. During the last few years, different machine learning approaches have successfully been applied to costumer-centric decision-making problems. In this study, we conduct a comparative assessment of the performance of ten widely used machine learning methods, (i.e., logistic regression, multilayer perceptron, support vector machines,  IBk linear NN search, KStar, locally weighted learning, decisionstump, C4.5., randomtree and  reduced error pruning tree) for the aim of suggesting appropriate machine learning techniques in the context of patient revisit intention prediction problem. Experimental results reveal that the C4.5 decision tree demonstrates to be the best predictive model since it has the highest overall average accuracy and a very low percentage error on both Type I and Type II errors, closely followed by the locally weighted learning and decisionstump, whereas the logistic regression and the IBk linear NN search algorithms appear to be the worst in terms of average accuracy and type II error. Besides the randomtree and the IBk linear NN search algorithms appear to be the worst in terms of type I error

High performance concrete compressive strength forecasting using ensemble models based on discrete wavelet transform

This paper investigates the use of wavelet ensemble models for high performance concrete (HPC) compressive strength forecasting. More specifically, we incorporate bagging and gradient boosting methods in building artificial neural networks (ANN) ensembles (bagged artificial neural networks (BANN) and gradient boosted artificial neural networks (GBANN)), first. Coefficient of determination (R-2), mean absolute error (MAE) and the root mean squared error (RMSE) statics are used for performance evaluation of proposed predictive models. Empirical results show that ensemble models (R-BANN(2)=0.9278, R-GBANN(2)=0.9270) are superior to a conventional ANN model (R-ANN(2)=0.9088). Then, we use the coupling of discrete wavelet transform (DWT) and ANN ensembles for enhancing the prediction accuracy. The study concludes that DWT is an effective tool for increasing the accuracy of the ANN ensembles (R-WBANN(2)=0.9397. R-WGBANN(2)=0.9528). (C) 2012 Elsevier Ltd. All rights reserved

Machine Learning Prediction of Mechanical and Durability Properties of Recycled Aggregates Concrete

Whilst recycled aggregate (RA) can alleviate the environmental footprint of concrete production and the landfilling of colossal amounts of demolition waste, there need for robust predictive tools for its effects on mechanical and durability properties. In this thesis, state-of-the-art machine learning (ML) models were deployed to predict properties of recycled aggregate concrete (RAC). A systematic review was performed to analyze pertinent ML techniques previously applied in the concrete technology field. Accordingly, three different ML methods were selected to determine the compressive strength of RAC and perform mixture proportioning optimization. Furthermore, a gradient boosting regression tree was used to study the effects of RA and several types of binders on the carbonation depth of RAC. The ML models developed in this study demonstrated robust performance to predict diverse properties of RAC

Machine Learning Prediction of Shear Capacity of Steel Fiber Reinforced Concrete

The use of steel fibers for concrete reinforcement has been growing in recent years owing to the improved shear strength and post-cracking toughness imparted by fiber inclusion. Yet, there is still lack of design provisions for steel fiber-reinforced concrete (SFRC) in building codes. This is mainly due to the complex shear transfer mechanism in SFRC. Existing empirical equations for SFRC shear strength have been developed with relatively limited data examples, making their accuracy restricted to specific ranges. To overcome this drawback, the present study suggests novel machine learning models based on artificial neural network (ANN) and genetic programming (GP) to predict the shear strength of SFRC beams with great accuracy. Different statistical metrics were employed to assess the reliability of the proposed models. The suggested models have been benchmarked against various soft-computing models and existing empirical equations. Sensitivity analysis has also been conducted to identify the most influential parameters to the SFRC shear strength