Evaluation of Artificial Intelligence Methods to Estimate the Compressive Strength of Geopolymers

Zou, Yong; Zheng, Chao; Alzahrani, Abdullah M.; Ahmad, Waqas; Ahmad, Ayaz; Mohamed, Abdeliazim Mustafa; Khallaf, Rana; Elattar, Samia

doi:10.3390/gels8050271

Cited by 51 publications

(12 citation statements)

References 57 publications

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“…The coefficient of determination (R 2 ) for an equation shows the accuracy of the model in predicting the results. A higher R 2 value near 1 suggests a higher accuracy [135]. The resultant relation has an R 2 of 0.91, which indicates a good agreement between experimental and predicted results.…”

Section: Compressive Strength (Cs)mentioning

confidence: 60%

Nano-Silica-Modified Concrete: A Bibliographic Analysis and Comprehensive Review of Material Properties

et al. 2022

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Several review studies have been performed on nano-silica-modified concrete, but this study adopted a new method based on scientometric analysis for the keywords’ assessment in the current research area. A scientometric analysis can deal with vast bibliometric data using a software tool to evaluate the diverse features of the literature. Typical review studies are limited in their ability to comprehensively and accurately link divergent areas of the literature. Based on the analysis of keywords, this study highlighted and described the most significant segments in the research of nano-silica-modified concrete. The challenges associated with using nano-silica were identified, and future research is directed. Moreover, prediction models were developed using data from the literature for the strength estimation of nano-silica-modified concrete. It was noted that the application of nano-silica in cement-based composites is beneficial when used up to an optimal dosage of 2–3% due to high pozzolanic reactivity and a filler effect, whereas a higher dosage of nano-silica has a detrimental influence due to the increased porosity and microcracking caused by the agglomeration of nano-silica particles. The mechanical strength might enhance by 20–25% when NS is incorporated in the optimal amount. The prediction models developed for predicting the strength of nano-silica-modified concrete exhibited good agreement with experimental data due to lower error values. This type of analysis may be used to estimate the essential properties of a material, therefore saving time and money on experimental tests. It is recommended to investigate cost-effective methods for the dispersion of nano-silica in higher concentrations in cement mixes; further in-depth studies are required to develop more accurate prediction models to predict nano-silica-modified concrete properties.

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Section: Compressive Strength (Cs)mentioning

confidence: 60%

Nano-Silica-Modified Concrete: A Bibliographic Analysis and Comprehensive Review of Material Properties

et al. 2022

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“…The ML methodology, in contrast to prior regression methods, delivers very precise results [ 54 , 55 ]. The discovery of artificial-intelligence algorithms such as genetic engineering programming (GEP), support vector machine (SVM), artificial neural network (ANN), and ensemble approaches has enabled researchers to address tough problems [ 56 , 57 , 58 , 59 , 60 , 61 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Mechanical Properties of Fly-Ash/Slag-Based Geopolymer Concrete Using Ensemble and Non-Ensemble Machine-Learning Techniques

et al. 2022

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The emission of greenhouse gases and natural-resource depletion caused by the production of ordinary Portland cement (OPC) have a detrimental effect on the environment. Thus, an alternative means is required to produce eco-friendly concrete such as geopolymer concrete (GPC). However, GPC has a complex cementitious matrix and an ambiguous mix design. Aside from that, the composition and proportions of materials utilized may have an impact on the compressive strength. Similarly, the use of robust and efficient machine-learning (ML) approaches is now required to forecast the strength of such a composite cementitious matrix. As a result, this study anticipated the compressive strength of GPC with waste resources using ensemble and non-ensemble ML algorithms. This was accomplished through the use of Anaconda (Python). To build a strong ensemble learner by integrating weak learners, adaptive boosting, random forest (RF), and ensemble learner bagging were employed. Furthermore, ensemble learners were utilized on non-ensemble or weak learners, such as decision trees (DT) and support vector machines (SVM) via regression. The data encompassed 156 statistical samples in which nine variables, namely superplasticizer (kg/m3), fly ash (kg/m3), ground granulated blast-furnace slag (GGBS), temperature (°C), coarse and fine aggregate (kg/m3), sodium silicate (Na2SiO3), and sodium hydroxide (NaOH), were chosen to anticipate the results. Exploring it in depth, twenty sub-models with ensemble boosting and bagging approaches were trained, and tuning was performed to achieve the highest possible coefficient of determination (R2). Moreover, cross K-Fold validation analysis and statistical checks were performed via indicators for the evaluation of the models. The result revealed that ensemble approaches yielded robust performance compared to non-ensemble algorithms. Generally, an ensemble learner with the RF and bagging approach on a DT yielded robust performance by achieving a better R2 as 0.93, and with the lowest statistical errors. The communal model in artificial-intelligence analysis, on average, improved the accuracy of the model.

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“…The goal of a machine learning model is to produce a mathematical expression for output prediction that is accurate and practicable based on a collection of independent parameters. Koza (1992) suggested the GEP as an evolution of the GA based on Darwin’s selection concept [ 47 ]. It is important to note that the main difference between the two techniques is that in GEP, fixed-length binary strings are replaced with non-linear parse trees.…”

Section: Multi-expression Programming (Mep)mentioning

confidence: 99%

“…Typically, a large database is used to represent concrete characteristics. In GEP, just a cross-over genetic operator is used, resulting in the generation of a large population of parse trees, increasing simulation time and requiring a considerable amount of memory [ 47 , 49 , 50 ]. Additionally, because GEP’s non-linear structure functions like gene expression patterns, the algorithm has a hard time proposing a simple mathematical representation for the required attribute.…”

Section: Multi-expression Programming (Mep)mentioning

confidence: 99%

“…Even though numerous ML research on concrete structures have attained great accuracy in their predictions, the applicability of the ML models receives little consideration. Numerous research assesses the feature relevance for tree-based models single decision process, heuristic techniques, or model-agnostic methods [ 47 , 48 ]. However, these approaches are frequently impractical and skewed for EML models, particularly those with a significant bias.…”

Section: Model Developmentmentioning

confidence: 99%

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Forecasting Compressive Strength of RHA Based Concrete Using Multi-Expression Programming

et al. 2022

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Rice husk ash (RHA) is a significant pollutant produced by agricultural sectors that cause a malignant outcome to the environment. To encourage the re-use of RHA, this work used multi expression programming (MEP) to construct an empirical model for forecasting the compressive nature of concrete made with RHA (CRHA) as a cement substitute. Thus, the compressive strength of CRHA was developed comprising of 192 findings from the broad and trustworthy database obtained from literature review. The most significant characteristics, namely the specimen’s age, the percentage of RHA, the amount of cement, superplasticizer, aggregates, and the amount of water, were used as input for the modeling of CRHA. External validation, sensitivity analysis, statistical checks, and Shapley Additive Explanations (SHAP) analysis were used to evaluate the models’ performance. It was discovered that the most significant factors impacting the compressive strength of CRHA are the age of the concrete sample (AS), the amount of cement (C) and the amount of aggregate (A). The findings of this study have the potential to increase the re-use of RHA in the production of green concrete, hence promoting environmental protection and financial gain.

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Evaluation of Artificial Intelligence Methods to Estimate the Compressive Strength of Geopolymers

Cited by 51 publications

References 57 publications

Nano-Silica-Modified Concrete: A Bibliographic Analysis and Comprehensive Review of Material Properties

Nano-Silica-Modified Concrete: A Bibliographic Analysis and Comprehensive Review of Material Properties

Prediction of Mechanical Properties of Fly-Ash/Slag-Based Geopolymer Concrete Using Ensemble and Non-Ensemble Machine-Learning Techniques

Forecasting Compressive Strength of RHA Based Concrete Using Multi-Expression Programming

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