Application of Advanced Machine Learning Approaches to Predict the Compressive Strength of Concrete Containing Supplementary Cementitious Materials

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This study aimed to expand the knowledge on the application of the most common industrial byproduct, i.e., fly ash, as a supplementary cementitious material. The characteristics of cement-based composites containing fly ash as supplementary cementitious material were discussed. This research evaluated the mechanical, durability, and microstructural properties of FA-based concrete. Additionally, the various factors affecting the aforementioned properties are discussed, as well as the limitations associated with the use of FA in concrete. The addition of fly ash as supplementary cementitious material has a favorable impact on the material characteristics along with the environmental benefits; however, there is an optimum level of its inclusion (up to 20%) beyond which FA has a deleterious influence on the composite’s performance. The evaluation of the literature identified potential solutions to the constraints and directed future research toward the application of FA in higher amounts. The delayed early strength development is one of the key downsides of FA use in cementitious composites. This can be overcome by chemical activation (alkali/sulphate) and the addition of nanomaterials, allowing for high-volume use of FA. By utilizing FA as an SCM, sustainable development may promote by lowering CO2 emissions, conserving natural resources, managing waste effectively, reducing environmental pollution, and low hydration heat.

Section: Introductionmentioning

confidence: 99%

Fly Ash Application as Supplementary Cementitious Material: A Review

Zhou

Ahmad

et al. 2022

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“…In the meantime, the rapid surge towards the use of various ML techniques for the prediction of numerous properties of materials plays a vital role for researchers in the field of engineering [ 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 ]. Especially the prediction of mechanical properties of different types of concrete, as concrete is a material that requires experimental efforts, time, and cost to achieve the desired strength [ 7 , 60 , 62 , 65 , 66 , 67 , 68 , 69 , 70 , 71 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning Approaches to Predict the Strength Property of Geopolymer Concrete

Cao

Fang

et al. 2022

Geopolymer concrete (GPC) based on fly ash (FA) is being studied as a possible alternative solution with a lower environmental impact than Portland cement mixtures. However, the accuracy of the strength prediction still needs to be improved. This study was based on the investigation of various types of machine learning (ML) approaches to predict the compressive strength (C-S) of GPC. The support vector machine (SVM), multilayer perceptron (MLP), and XGBoost (XGB) techniques have been employed to check the difference between the experimental and predicted results of the C-S for the GPC. The coefficient of determination (R2) was used to measure how accurate the results were, which usually ranged from 0 to 1. The results show that the XGB was a more accurate model, indicating an R2 value of 0.98, as opposed to SVM (0.91) and MLP (0.88). The statistical checks and k-fold cross-validation (CV) also confirm the high precision level of the XGB model. The lesser values of the errors for the XGB approach, such as mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE), were noted as 1.49 MPa, 3.16 MPa, and 1.78 MPa, respectively. These lesser values of the errors also indicate the high precision of the XGB model. Moreover, the sensitivity analysis was also conducted to evaluate the parameter’s contribution towards the anticipation of C-S of GPC. The use of ML techniques for the prediction of material properties will not only reduce the effort of experimental work in the laboratory but also minimize the cast and time for the researchers.

“…One of the necessary components of concrete is its binder, i.e., cement. There are threats to the environment caused by the process of cement production, including its high energy demand and the emission of numerous gases [6][7][8]. In order to overcome these threats, one option is to utilize additional materials that have binding properties, such as supplementary cementitious materials (SCMs), including silica fume, fly ash, blast furnace slag (BFS), etc., in place of cement, either during cement production or while manufacturing concrete [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…So far, there has been limited research in this sector that employs ensemble learning to predict concrete parameters. Adaptive Boosting (AdaBoost) and random Forest (RF) are ensemble learning techniques that can improve prediction accuracy by combining numerous regression tree predictions and voting on the final outcome [6,33]. Ahmad et al [6] performed standalone and EML techniques to predict the C-S of concrete and compared their accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Adaptive Boosting (AdaBoost) and random Forest (RF) are ensemble learning techniques that can improve prediction accuracy by combining numerous regression tree predictions and voting on the final outcome [6,33]. Ahmad et al [6] performed standalone and EML techniques to predict the C-S of concrete and compared their accuracy. It was determined that EML techniques predicted the outcomes with a higher level of accuracy than the standalone technique.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Computation of High-Performance Concrete Compressive Strength Using Standalone and Ensembled Machine Learning Techniques

Ahmad

et al. 2021

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The current trend in modern research revolves around novel techniques that can predict the characteristics of materials without consuming time, effort, and experimental costs. The adaptation of machine learning techniques to compute the various properties of materials is gaining more attention. This study aims to use both standalone and ensemble machine learning techniques to forecast the 28-day compressive strength of high-performance concrete. One standalone technique (support vector regression (SVR)) and two ensemble techniques (AdaBoost and random forest) were applied for this purpose. To validate the performance of each technique, coefficient of determination (R2), statistical, and k-fold cross-validation checks were used. Additionally, the contribution of input parameters towards the prediction of results was determined by applying sensitivity analysis. It was proven that all the techniques employed showed improved performance in predicting the outcomes. The random forest model was the most accurate, with an R2 value of 0.93, compared to the support vector regression and AdaBoost models, with R2 values of 0.83 and 0.90, respectively. In addition, statistical and k-fold cross-validation checks validated the random forest model as the best performer based on lower error values. However, the prediction performance of the support vector regression and AdaBoost models was also within an acceptable range. This shows that novel machine learning techniques can be used to predict the mechanical properties of high-performance concrete.