A Machine Learning-Assisted Numerical Predictor for Compressive Strength of Geopolymer Concrete Based on Experimental Data and Sensitivity Analysis (original) (raw)
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Civil Engineering and Architecture, 2024
Geopolymer concrete is an environmentally friendly alternative to traditional Portland cement concrete. This research investigates the use of Artificial Neural Networks (ANN) to predict the compressive and tensile strengths of such concrete. A strict materials selection was applied by assessing the use of fly ash class-F and Ground Granulated Blast Furnace Slag (GGBS) as geopolymer source materials. The ANN model performed exceptionally well with 75 different concrete mix combinations, generating an extremely low Mean Squared Error (MSE) of 2.9x10-5 , suggesting a scant 2% variation between predictions and targets. The study demonstrates a strong agreement between the ANN predictions and the experimental values across a wide range of concrete strengths (10 to 80 MPa), guaranteeing a complete dataset. Regression analysis demonstrates the model's dependability, with correlation coefficients (R) of 0.993, 0.819, and 0.956 for the training, testing, and validation datasets, respectively. A constant R-value of 0.932 across all datasets adds to the ANN model's accuracy. The model's dependability in predicting geopolymer concrete strengths was confirmed by predicting a new dataset extracted from the literature, which yielded high agreement with a maximum error of 3%.
Prediction of Geopolymer Concrete Compressive Strength Using Novel Machine Learning Algorithms
Polymers
The innovation of geopolymer concrete (GPC) plays a vital role not only in reducing the environmental threat but also as an exceptional material for sustainable development. The application of supervised machine learning (ML) algorithms to forecast the mechanical properties of concrete also has a significant role in developing the innovative environment in the field of civil engineering. This study was based on the use of the artificial neural network (ANN), boosting, and AdaBoost ML approaches, based on the python coding to predict the compressive strength (CS) of high calcium fly-ash-based GPC. The performance comparison of both the employed techniques in terms of prediction reveals that the ensemble ML approaches, AdaBoost, and boosting were more effective than the individual ML technique (ANN). The boosting indicates the highest value of R2 equals 0.96, and AdaBoost gives 0.93, while the ANN model was less accurate, indicating the coefficient of determination value equals 0.87. ...
E3S Web of Conferences
Fly ash-based geopolymer concrete is studied in this research work for its compressive strength, life cycle and environmental impact assessment contribution to the construction environment. This is in line with the United Nations’ sustainable development goals SDG9 and SDG11. However, the focus of this research paper is on the sustainability of geopolymer concrete and its overall environmental impact. The metaheuristic machine learning approaches have been deployed to predict the compressive strength (CS) of the GPC based on environmental impact considerations of the concrete constituent materials, which included fly ash, sodium silicate, sodium hydroxide, fine and coarse aggregates. The metaheuristic techniques include the k-Nearest Neighbour (kNN), support vector regression (SVR), and random forest regression (RFR), where all are optimized with the particle swarm (PSO). These metaheuristic techniques have been modified for this research work with new codes to enhance innovation in...
Artificial Intelligence Approaches for Prediction of Compressive Strength of Geopolymer Concrete
Materials
Geopolymer concrete (GPC) has been used as a partial replacement of Portland cement concrete (PCC) in various construction applications. In this paper, two artificial intelligence approaches, namely adaptive neuro fuzzy inference (ANFIS) and artificial neural network (ANN), were used to predict the compressive strength of GPC, where coarse and fine waste steel slag were used as aggregates. The prepared mixtures contained fly ash, sodium hydroxide in solid state, sodium silicate solution, coarse and fine steel slag aggregates as well as water, in which four variables (fly ash, sodium hydroxide, sodium silicate solution, and water) were used as input parameters for modeling. A total number of 210 samples were prepared with target-specified compressive strength at standard age of 28 days of 25, 35, and 45 MPa. Such values were obtained and used as targets for the two AI prediction tools. Evaluation of the model’s performance was achieved via criteria such as mean absolute error (MAE), ...
Prediction of Compressive Strength of Geopolymer Concrete using Artificial Neural Network
Journal of Engineering Research and Application, 2022
Geopolymer concrete could be an alternative to ordinary Portland cement concrete. It is sustainable, ecofriendly, durable, and economic concrete. Machine learning methods could an alternatives to determines strength without destruction tests conduction of the mix samples. In this study, to analyse the experimental investigation and prediction of compressive strength using artificial neural network. In experimental analysis, M2 mix got optimum point in the compressive strength among all mixes. ANN model is used to predict the compressive strength of geopolymer concrete. ANN model prediction has negligible errors. So, it can be stated that machine learning methods are capable to predicts the strength of concrete accurately.
Fly ash is a by-product almost found in coal power plants; it is available worldwide. According to the hazardous impacts of cement on the environment, fly ash is known to be a suitable replacement for cement in concrete. A lot of carbon dioxide (CO2) is released during cement manufacturing. The investigations estimate that about 8 – 10% of the total CO2 emissions are maintained by cement production. Since fly ash has nearly the same chemical compounds as cement, it can be utilized as a suitable alternative to cement in concrete (green concrete). The current study analyzes the effect of the quantity of the two main components of fly ash, CaO, and SiO2, on the compressive strength of concrete modified with different fly ash content for various mix proportions. For this purpose, various concrete samples modified with fly ash were collected from the literature (236 datasets), analyzed, and modeled using four different models; Full-quadratic (FQ), Nonlinear regression (NLR), Multi-linear regression (MLR), and Artificial neural network (ANN) model to predict the compressive strength of concrete with different geometry and size of the specimens. The accuracy of the models was evaluated using correlation coefficient (R2), Mean absolute error (MAE), Root mean squared error (RMSE), Scatter Index (SI), a-20 index, and Objective function (OBJ). According to the modeling results, increasing SiO2 (%) increased the compressive strength, while increasing CaO (%) increased compressive strength only when the cement replacement with fly ash was between 52 and –100%. Based on R2, RMSE, and MAE, the ANN model was the most effective and accurate on predicting the compressive strength of concrete in different strength ranges. According to the sensitivity analysis, curing time is the most critical characteristic for predicting the compression strength of concrete using this database. The primary objective of this work is to explore and evaluate various machine-learning models for predicting compressive resistance. The study emphasizes these models' development, comparison, and performance assessment, highlighting their potential to predict compressive strength accurately. The research primarily uses machine learning, leveraging algorithms and techniques to build predictive models. The focus is on harnessing the power of data-driven approaches to improve the accuracy and reliability of compressive strength predictions. © 2023 Elsevier Ltd
A neural network approach for predicting hardened property of Geopolymer concrete
International Journal of GEOMATE, 2020
This paper presents the application of an Artificial Neural Network (ANN) approach to predict the 28-day compression strength of Geopolymer concrete (GPC) from the input ingredients. A total of 190 test samples collected from previously published were employed for training and validating the ANN model. Additionally, a test project was also implemented to collect the experimental data for verifying the prediction ability of the ANN model. Different learning algorithms were investigated to obtain the optimal algorithm for the GPC data. Results from the study revealed that the ANN model using the "trainlm" learning algorithm provided the best prediction results. The average prediction error about 8 MPa was found for the unseen data set. Besides, the effects of changing input variables to the output of the model were also explored by conducting the sensitivity analysis. It was shown that the 28-day GPC compression strength was more sensitive to the change of coarse aggregate (CoAg) and sodium silicate (Na 2 SiO 3) variables.
Machine learning-based model for predicting concrete compressive strength
International Journal of GEOMATE, 2021
This study aims at applying a machine learning-based model to establish the relationship between different input variables to the 28-day compressive strength of normal and High-Performance Concrete (HPC). An Artificial Neural Network (ANN) model was trained, validated, and tested using a comprehensive database consisted of 361 records gathered from the previously circulated source. Various models with different learning algorithms and neuron numbers in the hidden layer were examined to attain the best performance model. The examination results revealed that the ANN model using the "trainlm" learning algorithm delivered the best prediction outcomes with the overall coefficient of determination (R 2) of 0.9277. The influence of input parameters on the output was also examined by performing the sensitivity analysis. It was observed that the compressive strength of concrete at 28 days was more responsive to the changes in the cement parameter (CM) and the amount of water (WT). In contrast, the 28-day concrete compressive strength was found less sensitive to the variation of the fly ash (FL) parameter.
2019
Estimating the compressive strength of concrete before fabricating, it has been one of the most important challenges because designing a mixture proportion by experimental methods needs expert workers, consumes energy, and wastes materials. Therefore, in this study, the influences of materials and the age of samples on the compressive strength of fly ash concrete are investigated, and a novel method for predicting the compressive strength is presented. To this end, water cycle algorithm and genetic algorithm are utilized, and their outcomes are compared with the classical regression models. Various performance indicators are used to gauge the accuracy of the models. By analyzing the results, it is comprehended that the water cycle algorithm is the most accurate model according to all performance indicators. Besides, the outcomes of water cycle algorithm and genetic algorithm are by far better than those of classical methods. The mean absolute error of water cycle algorithm, genetic ...