Predicting the effects of nanoparticles on compressive strength of ash-based geopolymers by gene expression programming

In the present work, the effect of SiO₂ and Al₂O₃ nanoparticles on compressive strength of ash-based geopolymers with different mixtures of rice husk ash, fly ash, nanoalumina and nanosilica has been predicted by gene expression programming. The models were constructed by 12 input parameters, namely the water curing time, the rice husk ash content, the fly ash content, the water glass content, NaOH content, the water content, the aggregate content, SiO₂ nanoparticle content, Al₂O₃ nanoparticle content, oven curing temperature, oven curing time and test trial number. The value for the output layer was the compressive strength. According to the input parameters in gene expression programming models, the data were trained and tested, and the effects of SiO₂ and Al₂O₃ nanoparticles on compressive strength of the specimens were predicted with a tiny error. The results indicate that gene expression programming model is a powerful tool for predicting the effect of nanoparticles on compressive strength of the geopolymers in the considered range.     

RETRACTED ARTICLE: Prediction compressive strength of Portland cement-based geopolymers by artificial neural networks

In the present study, compressive strength results of geopolymers produced by ordinary Portland cement (OPC) as aluminosilicate source have been modeled by artificial neural networks. Six main factors including NaOH concentration, water glass to NaOH weight ratio, alkali activator to cement weight ratio, oven curing temperature, oven curing time and water curing regime each at 4 levels were considered for designing. A total of 32 experiments were conducted according to the L32 array proposed by the method. The neural network models were constructed by 10 input parameters including NaOH concentration, water glass to NaOH weight ratio, alkali activator to cement weight ratio, oven curing temperature, oven curing time, water curing regime, water glass content, NaOH content, Portland cement content and test trial number. The value for the output layer was the compressive strength. According to the input parameters in feed-forward back-propagation algorithm, the constructed networks were trained, validated and tested. The results indicate that artificial neural networks model is a powerful tool for predicting the compressive strength of the geopolymers in the considered range.

     

Artificial neural networks application to predict the compressive damage of lightweight geopolymer

In this work, compressive strength of lightweight geopolymers produced by fine fly ash and rice husk–bark ash together with palm oil clinker (POC) aggregates has been investigated experimentally and modeled based on artificial neural networks. Different specimens made from a mixture of fine fly ash and rice husk–bark ash with and without POC were subjected to compressive strength tests at 2, 7, and 28 days of curing. A model based on artificial neural networks for predicting the compressive strength of the specimens has been presented. To build the model, training and testing using experimental results from 144 specimens were conducted. The data used in the multilayer feed-forward neural networks models are arranged in a format of six input parameters that cover the quantity of fine POC particles, the quantity of coarse POC particles, the quantity of FA + RHBA mixture, the ratio of alkali activator to ashes mixture, the age of curing and the test trial number. According to these input parameters, in the neural networks model, the compressive strength of each specimen was predicted. The training and testing results in the neural networks model have shown a strong potential for predicting the compressive strength of the geopolymer specimens in the considered range.     

ANFIS-based prediction of the compressive strength of geopolymers with seeded fly ash and rice husk–bark ash

In the present work, compressive strength of geopolymers made from seeded fly ash and rice husk–bark ash has been predicted by adaptive network-based fuzzy inference systems (ANFIS). Different specimens, made from a mixture of fly ash and rice husk–bark ash in fine and coarse forms and a mixture of water glass and NaOH mixture as alkali activator, were subjected to compressive strength tests at 7 and 28 days of curing. The curing regimes were different: one set of the specimens were cured in water at room temperature until 7 and 28 days and the other sets were oven-cured for 36 h at the range of 40–90°C and then cured at room temperature until 7 and 28 days. A model based on ANFIS for predicting the compressive strength of the specimens has been presented. To build the model, training and testing using experimental results from 120 specimens were conducted. The used data as the inputs of ANFIS models are arranged in a format of six parameters that cover the percentage of fine fly ash in the ashes mixture, the percentage of coarse fly ash in the ashes mixture, the percentage of fine rice husk–bark ash in the ashes mixture, the percentage of coarse rice husk–bark ash in the ashes mixture, the temperature of curing, and the time of water curing. According to these input parameters in the ANFIS models, the compressive strength of each specimen was predicted. The training and testing results in ANFIS models showed a strong potential for predicting the compressive strength of the geopolymeric specimens.     

Artificial neural networks to prediction total specific pore volume of geopolymers produced from waste ashes

In the present work, total specific pore volume of inorganic polymers (geopolymers) made from seeded fly ash and rice husk bark ash has been predicted by artificial neural networks. Different specimens were subjected to porosimetry tests at 7 and 28 days of curing. One set of the specimens were cured at room temperature until reaching to 7 and 28 days, and the other sets were oven-cured for 36 h at the range of 40–90°C and then cured at room temperature until 7 and 28 days. To build the neural network models, training and testing using experimental results from 120 specimens were conducted. According to these input parameters, in the neural networks models, the pore volume of each specimen was predicted. The training and testing results in the neural networks model have shown a strong potential for predicting the total specific pore volume of the geopolymer specimens in the examined range.     

Fuzzy logic-based prediction of compressive strength of lightweight geopolymers

In the present work, compressive strength of lightweight inorganic polymers (geopolymers) produced by fine fly ash and rice husk–bark ash together with palm oil clinker (POC) aggregates has been investigated experimentally and modeled based on fuzzy logic. To build the model, training, validating and testing were conducted using experimental results from 144 specimens. The used data in the ANFIS models were arranged in a format of six input parameters that cover the quantity of fine POC particles, the quantity of coarse POC particles, the quantity of FA + RHBA mixture, the ratio of alkali activator to ashes mixture, the age of curing and the test trial number. According to these input parameters, in the model, the compressive strength of each specimen was predicted. The training, validating and testing results in the model have shown a strong potential for predicting the compressive strength of the geopolymer specimens in the considered range.     

Utilizing ANFIS for prediction water absorption of lightweight geopolymers produced from waste materials

In the present work, water absorption of lightweight geopolymers produced by fine fly ash and rice husk–bark ash together with palm oil clinker (POC) aggregates has been investigated experimentally and modeled by adaptive network-based fuzzy inference systems (ANFIS). Different specimens made from a mixture of fine fly ash and rice husk–bark ash with and without POC were subjected to water absorption tests at 2, 7, and 28 days of curing. The specimens were oven cured for 36 h at 80 °C and then cured at room temperature until 2, 7, and 28 days. The results showed that high amount of POC particles improve the percentage of water absorption at the early age of curing. In addition, the ratio of “the percentage of water absorption” to “weight” of the POC-contained specimens at all ages of curing was much higher than that of POC-free specimens, which make them suitable for lightweight applications. To build the model, training, validating, and testing using experimental results from 144 specimens were conducted. The used data in the ANFIS models are arranged in a format of six input parameters that cover the quantity of fine POC particles, the quantity of coarse POC particles, the quantity of FA + RHBA mixture, the ratio of alkali activator to ashes mixture, the age of curing, and the test trial number. According to these input parameters, the water absorption of each specimen was predicted. The training, validating, and testing results in the ANFIS models showed a strong potential for predicting the water absorption of the geopolymer specimens.     

Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network

This study applies multiple regression analysis and an artificial neural network in estimating the compressive strength of concrete that contains various amounts of blast furnace slag and fly ash, based on the properties of the additives (blast furnace slag and fly ash in this case) and values obtained by non-destructive testing rebound number and ultrasonic pulse velocity for 28 different concrete mixtures (M_control and M₁–M₂₇) at different curing times (3, 7, 28, 90, and 180 days). The results obtained using the two methods are then compared and discussed. The results reveal that although multiple regression analysis was more accurate than artificial neural network in predicting the compressive strength using values obtained from non-destructive testing, the artificial neural network models performed better than did multiple regression analysis models. The application of an artificial neural network to the prediction of the compressive strength in admixture concrete of various curing times shows great potential in terms of inverse problems, and it is suitable for calculating nonlinear functional relationships, for which classical methods cannot be applied.     

Computer-aided prediction of the Al2O3 nanoparticles’ effects on tensile strength and percentage of water absorption of concrete specimens

In the present paper, two models based on artificial neural networks and genetic programming for predicting split tensile strength and percentage of water absorption of concretes containing Al₂O₃ nanoparticles have been developed at different ages of curing. For purpose of building these models, training and testing using experimental results for 144 specimens produced with 16 different mixture proportions were conducted. The data used in the multilayer feed-forward neural networks models and input variables of genetic programming models are arranged in a format of eight input parameters that cover the cement content, nanoparticle content, aggregate type, water content, the amount of superplasticizer, the type of curing medium, Age of curing and number of testing try. According to these input parameters, in the neural networks and genetic programming models, the split tensile strength and percentage of water absorption values of concretes containing Al₂O₃ nanoparticles were predicted. The training and testing results in the neural network and genetic programming models have shown that every two models have strong potential for predicting the split tensile strength and percentage of water absorption values of concretes containing Al₂O₃ nanoparticles. It has been found that NN and GEP models will be valid within the ranges of variables. In neural networks model, as the training and testing ended when minimum error norm of network gained, the best results were obtained, and in genetic programming model, when 4 gens was selected to construct the model, the best results were acquired. Although neural network have predicted better results, genetic programming is able to predict reasonable values with a simpler method rather than neural network.     

Application of adaptive neuro-fuzzy technique and regression models to predict the compressive strength of geopolymer composites

This article introduces an adaptive network-based fuzzy inference system (ANFIS) model and two linear and nonlinear regression models to predict the compressive strength of geopolymer composites. Geopolymers are highly complex materials which involve many variables which make modeling its properties very difficult. There is no systematic approach in the mix design for geopolymers. The amounts of silica modulus, Na₂O content, w/b ratios, and curing time have a great influence on the compressive strength. In this study, by developing and comparing parametric linear and nonlinear regressions and ANFIS models, we dealt with predicting the compressive strength of geopolymer composites for possible use in mix-design framework considering the mentioned complexities. ANFIS model developed by generalized bell-shaped membership function was recognized the best approach, and the prediction results of linear and nonlinear regression models as empirical methods showed the weakness of these models comparing ANFIS model.

     

Modeling the compressive strength of geopolymeric binders by gene expression programming-GEP

GEP has been employed in this work to model the compressive strength of different types of geopolymers through six different schemes. The differences between the models were in their linking functions, number of genes, chromosomes and head sizes. The curing time, Ca(OH)₂ content, the amount of superplasticizer, NaOH concentration, mold type, aluminosilicate source and H₂O/Na₂O molar ratio were the seven input parameters considered in the construction of the models to evaluate the compressive strength of geopolymers. A total number of 399 input-target pairs were collected from the literature, randomly divided into 299 and 100 sets and were trained and tested, respectively. The best performance model had 6 genes, 14 head size, 40 chromosomes and multiplication as linking function. This was shown by the absolute fraction of variance, the absolute percentage error and the root mean square error. These were of 0.9556, 2.4601 and 3.4716 for training phase, respectively and 0.9483, 2.8456 and 3.7959 for testing phase, respectively. However, another model with 7 genes, 12 head size, 30 chromosomes and addition as linking function showed suitable results with the absolute fraction of variance, the absolute percentage error and the root mean square of 0.9547, 2.5665 and 3.4360 for training phase, respectively and 0.9466, 2.8020 and 3.8047 for testing phase, respectively. These models showed that gene expression programming has a strong potential for predicting the compressive strength of different types of geopolymers in the considered range.     

Applying a meta-heuristic algorithm to predict and optimize compressive strength of concrete samples

The successful use of fly ash (FA) and silica fume (SF) materials has been reported in the design of concrete samples in the literature. Due to the benefits of using these materials, they can be utilized in many industrial applications. However, the proper use of them in the right mixes is one of the important factors with respect to the strength and weight of concrete. Therefore, this paper develops relationships based on meta-heuristic (MH) algorithms (artificial bee colony technique) to evaluate the compressive strength of concrete specimens using laboratory experiments. A database comprising silica fume replacement ratio, fly ash replacement ratio, total cementitious material, water content coarse aggregate, high-rate water-reducing agent, fine aggregate, and age of samples, as model inputs, was used to evaluate and predict the compressive strength of concrete samples. Developed models of the MH technique created relationships between the mentioned parameters. In the new models, the influence of each parameter on the compressive strength was determined. Finally, using the developed model, optimum conditions for compressive strength of concrete samples were presented. This paper demonstrated that the MH algorithms are able to develop relationships that can serve as good substitutes for empirical models.

     

Computer-aided design of the effects of Cr_2O_3 nanoparticles on split tensile strength and water permeability of high strength concrete

In the present paper, two models based on artificial neural networks and genetic programming for predicting split tensile strength and percentage of water absorption of concretes containing Cr2O3 nanoparticles have been developed at different ages of curing. For purpose of building these models, training and testing using experimental results for 144 specimens produced with 16 different mixture proportions were conducted. The data used in the multilayer feed forward neural networks models and input variable...     

An optimized instance based learning algorithm for estimation of compressive strength of concrete

This article proposes an optimized instance-based learning approach for prediction of the compressive strength of high performance concrete based on mix data, such as water to binder ratio, water content, super-plasticizer content, fly ash content, etc. The base algorithm used in this study is the k nearest neighbor algorithm, which is an instance-based machine leaning algorithm. Five different models were developed and analyzed to investigate the effects of the number of neighbors, the distance function and the attribute weights on the performance of the models. For each model a modified version of the differential evolution algorithm was used to find the optimal model parameters. Moreover, two different models based on generalized regression neural network and stepwise regressions were also developed. The performances of the models were evaluated using a set of high strength concrete mix data. The results of this study indicate that the optimized models outperform those derived from the standard k nearest neighbor algorithm, and that the proposed models have a better performance in comparison to generalized regression neural network, stepwise regression and modular neural networks models.     

高性能混凝土强度预测的神经网络-主成分分析

在误差逆传播算法神经网络预测模型数据前处理中,对样本集优化,采用多元统计分析中的主成分分析法,提取影响粉煤灰高性能混凝土抗压强度的主要因素,消除影响因素间的线性相关性。研究结果表明,用该方法处理后的样本数据输入神经网络,提高了预测效率,训练时间减少,预测精度也有一定程度的提高,网络结构得到简化。     

Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks

The paper presents a comparative performance of the models developed to predict 28 days compressive strengths using neural network techniques for data taken from literature (ANN-I) and data developed experimentally for SCC containing bottom ash as partial replacement of fine aggregates (ANN-II). The data used in the models are arranged in the format of six and eight input parameters that cover the contents of cement, sand, coarse aggregate, fly ash as partial replacement of cement, bottom ash as partial replacement of sand, water and water/powder ratio, superplasticizer dosage and an output parameter that is 28-days compressive strength and compressive strengths at 7 days, 28 days, 90 days and 365 days, respectively for ANN-I and ANN-II. The importance of different input parameters is also given for predicting the strengths at various ages using neural network. The model developed from literature data could be easily extended to the experimental data, with bottom ash as partial replacement of sand with some modifications.     

Applying several machine learning approaches for prediction of unconfined compressive strength of stabilized pond ashes

This paper evaluates the potential of five modeling approaches, namely M5 model tree, random forest, artificial neural networks, support vector machines and Gaussian processes, for the prediction of unconfined compressive strength of stabilized pond ashes with lime and lime sludge. The study not only presents five models for the same set of data but also compares the overall performance of them. Dataset used consists of 255 samples acquired from laboratory experiments. Out of the total, 170 randomly chosen samples were used for training and remaining 85 were used for testing the models. Input dataset consists of eight parameters (uniformity coefficient, coefficient of curvature, maximum dry density, optimum moisture content, lime, lime sludge, curing period and 7-day soaked California bearing ratio), while the output is UCS value at 7, 28, 45, 90 and 180 days of curing. Comparisons of results propose that Gaussian processes modeling strategy works well and the overall performance was substantially nearer to the exact agreement line. As a result of GP model, higher value of CC = 0.997 and lower values of RMSE = 23.016 kPa and MAE = 16.455 were obtained for testing the dataset. Sensitivity analysis suggests that lime, lime sludge, curing period and California bearing ratio are the significant parameters for predicting the unconfined compressive strength of stabilized pond ashes. The results confirmed that GP models are in a position to predict the unconfined compressive strength of stabilized pond ashes with an excessive degree of accuracy; however, GP modeling approach proves that this approach is more economical and less difficult in comparison with tedious laboratory work.

     

Prediction of compressive strength of concretes containing metakaolin and silica fume by artificial neural networks

Neural networks have recently been widely used to model some of the human activities in many areas of civil engineering applications. In the present paper, the models in artificial neural networks (ANN) for predicting compressive strength of concretes containing metakaolin and silica fume have been developed at the age of 1, 3, 7, 28, 56, 90 and 180 days. For purpose of building these models, training and testing using the available experimental results for 195 specimens produced with 33 different mixture proportions were gathered from the technical literature. The data used in the multilayer feed forward neural networks models are arranged in a format of eight input parameters that cover the age of specimen, cement, metakaolin (MK), silica fume (SF), water, sand, aggregate and superplasticizer. According to these input parameters, in the multilayer feed forward neural networks models are predicted the compressive strength values of concretes containing metakaolin and silica fume. The training and testing results in the neural network models have shown that neural networks have strong potential for predicting 1, 3, 7, 28, 56, 90 and 180 days compressive strength values of concretes containing metakaolin and silica fume.     

Systematic multiscale models to predict the compressive strength of self-compacting concretes modified with nanosilica at different curing ages

The evolution of nanotechnology brings materials with novel performance and during last year’s much attempt has been established to include nanoparticles especially nano-silica (NS) into the concrete to improve performance and develop concrete with enhanced characteristics. Generally, NS is incorporated into the self-compacting concrete (SCC) aiming to positively influence the fresh, mechanical, microstructure, and durability properties of the composite. The most important mechanical property for all types of concrete composites is compressive strength. Therefore, developing reliable models for predicting the compressive strength of SCC is crucial regarding saving time, energy, and cost-effectiveness. Moreover, it gives valuable information for scheduling the construction work and provides information about the correct time for removing the formwork. In this study, three different models including the linear relationship model (LR), nonlinear model (NLR), and multi-logistic model (MLR) were proposed to predict the compressive strength of SCC mixtures made with or without NS. In this regard, a comprehensive data set that consists of 450 samples were collected and analyzed to develop the models. In the modeling process, the most important variables affecting the compressive strength such as NS content, cement content, water to binder ratio, curing time from 1 to 180 days, superplasticizer content, fine aggregate content, and coarse aggregate content were considered as input variables. Various statistical assessments such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), OBJ value, and the coefficient of determination (R²) were used to evaluate the performance of the proposed models. The results indicated that the MLR model performed better for forecasting the compression strength of SCC mixtures modified with NS compared to other models. The SI and OBJ values of the MLR model were 18.8% and 16.7% lower than the NLR model, indicating the superior performance of the MLR model. Moreover, the sensitivity analysis demonstrated that the curing time is the most affecting variable for forecasting the compressive strength of SCC modified with NS.

     

Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network

In this study, an artificial neural networks study was carried out to predict the compressive strength of ground granulated blast furnace slag concrete. A data set of a laboratory work, in which a total of 45 concretes were produced, was utilized in the ANNs study. The concrete mixture parameters were three different water–cement ratios (0.3, 0.4, and 0.5), three different cement dosages (350, 400, and 450 kg/m³) and four partial slag replacement ratios (20%, 40%, 60%, and 80%). Compressive strengths of moist cured specimens (22 ± 2 °C) were measured at 3, 7, 28, 90, and 360 days. ANN model is constructed, trained and tested using these data. The data used in the ANN model are arranged in a format of six input parameters that cover the cement, ground granulated blast furnace slag, water, hyperplasticizer, aggregate and age of samples and, an output parameter which is compressive strength of concrete. The results showed that ANN can be an alternative approach for the predicting the compressive strength of ground granulated blast furnace slag concrete using concrete ingredients as input parameters.