scholarly journals Modeling Compressive Strength of Eco-Friendly Volcanic Ash Mortar using Artificial Neural Networking

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2009
Author(s):  
Muhammad Nasir Amin ◽  
Muhammad Faisal Javed ◽  
Kaffayatullah Khan ◽  
Faisal I. Shalabi ◽  
Muhammad Ghulam Qadir
Keyword(s):  
Compressive Strength ◽  
Volcanic Ash ◽  
Failure Surface ◽  
Curing Temperature ◽  
Error Statistics ◽  
Statistical Measures ◽  
Concrete Failure ◽  
Neuro Fuzzy ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Forecasting the compressive strength of concrete is a complex task owing to the interactions among concrete ingredients. In addition, an important characteristic of the concrete failure surface is its six-fold symmetry. In this study, an artificial neural network (ANN) and adaptive neuro fuzzy interface system (ANFIS) were employed to model the compressive strength of natural volcanic ash mortar (VAM) by using the six-fold symmetry of concrete failure. The modeling was correlated with four parameters. To train and test the projected models, data for more than 150 samples were collected from the literature. Furthermore, mortar samples with varying proportions of volcanic ash were prepared in the laboratory and tested, and the results were used to validate the models. The performance of the developed models was assessed using numerous statistical measures. The results show that both the ANN and ANFIS models accurately predict the compressive strength of VAM with R-square above 0.9 and lower error statistics. The permutation feature analysis confirmed that the age of specimens affects the strength of VAM the most, followed by the water-to-cement ratio, curing temperature, and percentage of volcanic ash.

scholarly journals Machine Learning Approaches for Prediction of the Compressive Strength of Alkali Activated Termite Mound Soil

2021 ◽  
Vol 11 (11) ◽  
pp. 4754
Author(s):  
Assia Aboubakar Mahamat ◽  
Moussa Mahamat Boukar ◽  
Nurudeen Mahmud Ibrahim ◽  
Tido Tiwa Stanislas ◽  
Numfor Linda Bih ◽  
...  
Keyword(s):  
Compressive Strength ◽  
Construction Materials ◽  
Curing Temperature ◽  
Sustainable Construction ◽  
Support Vector ◽  
Learning Approaches ◽  
Artificial Neural Network Ann ◽  
Curing Regime ◽  
Alkali Activated

Earth-based materials have shown promise in the development of ecofriendly and sustainable construction materials. However, their unconventional usage in the construction field makes the estimation of their properties difficult and inaccurate. Often, the determination of their properties is conducted based on a conventional materials procedure. Hence, there is inaccuracy in understanding the properties of the unconventional materials. To obtain more accurate properties, a support vector machine (SVM), artificial neural network (ANN) and linear regression (LR) were used to predict the compressive strength of the alkali-activated termite soil. In this study, factors such as activator concentration, Si/Al, initial curing temperature, water absorption, weight and curing regime were used as input parameters due to their significant effect in the compressive strength. The experimental results depict that SVM outperforms ANN and LR in terms of R2 score and root mean square error (RMSE).

scholarly journals Learned Prediction of Compressive Strength of GGBFS Concrete Using Hybrid Artificial Neural Network Models

Materials ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 3708 ◽  
Author(s):  
In-Ji Han ◽  
Tian-Feng Yuan ◽  
Jin-Young Lee ◽  
Young-Soo Yoon ◽  
Joong-Hoon Kim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Experimental Studies ◽  
Back Propagation ◽  
Network Models ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Artificial Neural Network Models

A new hybrid intelligent model was developed for estimating the compressive strength (CS) of ground granulated blast furnace slag (GGBFS) concrete, and the synergistic benefits of the hybrid algorithm as compared with a single algorithm were verified. While using the collected 269 data from previous experimental studies, artificial neural network (ANN) models with three different learning algorithms namely back-propagation (BP), particle swarm optimization (PSO), and new hybrid PSO-BP algorithms, were constructed and the performance of the models was evaluated with regard to the prediction accuracy, efficiency, and stability through a threefold procedure. It was found that the PSO-BP neural network model was superior to the simple ANNs that were trained by a single algorithm and it is suitable for predicting the CS of GGBFS concrete.

scholarly journals Influence of Silica Modulus and Curing Temperature on the Strength of Alkali-Activated Volcanic Ash and Limestone Powder Mortar

Materials ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5204
Author(s):  
Adeshina Adewale Adewumi ◽  
Mohd Azreen Mohd Ariffin ◽  
Mohammed Maslehuddin ◽  
Moruf Olalekan Yusuf ◽  
Mohammad Ismail ◽  
...  
Keyword(s):  
Compressive Strength ◽  
Low Temperatures ◽  
Room Temperature ◽  
Volcanic Ash ◽  
Curing Temperature ◽  
Limestone Powder ◽  
Strength Development ◽  
Mass Ratio ◽  
Ambient Room Temperature ◽  
Alkali Activated

This present study evaluates the effect of silica modulus (Ms) and curing temperature on strengths and the microstructures of binary blended alkali-activated volcanic ash and limestone powder mortar. Mortar samples were prepared using mass ratio of combined Na2SiO3(aq)/10 M NaOH(aq) of 0.5 to 1.5 at an interval of 0.25, corresponding to Ms of 0.52, 0.72, 0.89, 1.05 and 1.18, respectively, and sole 10 M NaOH(aq). Samples were then subjected to ambient room temperature, and the oven-cured temperature was maintained from 45 to 90 °C at an interval of 15 °C for 24 h. The maximum achievable 28-day strength was 27 MPa at Ms value of 0.89 cured at 75 °C. Samples synthesised with the sole 10 M NaOH(aq) activator resulted in a binder with a low 28-day compressive strength (15 MPa) compared to combined usage of Na2SiO3(aq)/10 M NaOH(aq) activators. Results further revealed that curing at low temperatures (25 °C to 45 °C) does not favour strength development, whereas higher curing temperature positively enhanced strength development. More than 70% of the 28-day compressive strength could be achieved within 12 h of curing with the usage of combined Na2SiO3(aq)/10 M NaOH(aq). XRD, FTIR and SEM + EDX characterisations revealed that activation with combined Na2SiO3(aq)/10 M NaOH(aq) leads to the formation of anorthite (CaAl2Si2O8), gehlenite (CaO.Al2O3.SiO2) and albite (NaAlSi3O8) that improve the amorphosity, homogeneity and microstructural density of the binder compared to that of samples synthesised with sole 10 M NaOH(aq).

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