A Data-Driven Influential Factor Analysis Method for Fly Ash–Based Geopolymer Using Optimized Machine-Learning Algorithms

Ma, Guowei; Cui, Aidi; Huang, Yimiao; Dong, Wei

doi:10.1061/(asce)mt.1943-5533.0004266

Cited by 15 publications

(3 citation statements)

References 44 publications

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“…Some other researchers developed compressive strength predictive models of fly ash based geopolymer concrete (FGPC) using Support Vector Machine (or) Regression (SVM/ SVR) in addition to Random Forest Method, Back propagation neural network, Multilayer perceptron and Extreme gradient boosting algorithm etc. It was concluded that all these models can be effectively employed in the prediction of compressive strength of FGPC [34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Compressive Strength Prediction of Fly Ash Geopolymer Concrete Using Support Vector and Random Forest Regression

Venkateswara Rao,

Harish Kumar,

Satish

2024

J. Phys.: Conf. Ser.

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The mix design of geopolymer concrete includes consideration of several factors and many trail tests. Obviously, to obtain specified target strength different combinations need to be repeatedly adjusted. As a result, this process consumes more time and energy resources. Therefore, from the sustainability of resources it is essential to develop a predictive model, which can show the influence of different combinations on the targeted compressive strength of geopolymer concrete. The current work includes prediction of compressive strength of fly ash based geopolymer concrete using machine leaning algorithms. The input data sets for the modelling are collected from previously conducted research works. Machine leaning algorithms employed are Support Vector Regression (SVR) and Random Forest method (RFM). The input variables include quantity of sodium silicate solution, molarity of sodium hydroxide, ratios of NaOH to Na2SiO3, Alkaline liquid to fly ash and total water content to geopolymer solids. About 75 % of the data collected is used for training and 25 % of the data is used for testing the performance of the model. The accuracy of the model developed is checked using coefficient of determination. It is observed from statical checks that both methods predicted the compressive strength to an acceptable degree of accuracy. The coefficient of determination of for SVM and RFM are found to be 0.82 and 0.81 respectively. Results indicated that the quantities of aggregates are not influencing the compressive strength of geopolymer concrete, on the other hand alkaline liquid to binder and water to geopolymer solids are showing significant impact on the strength of mixes.

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Section: Introductionmentioning

confidence: 99%

Compressive Strength Prediction of Fly Ash Geopolymer Concrete Using Support Vector and Random Forest Regression

Venkateswara Rao,

Harish Kumar,

Satish

2024

J. Phys.: Conf. Ser.

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“…This can be particularly problematic in cases where the algorithm is used to make predictions about future events or trends based on historical data. In order to avoid overfitting, it is important to carefully design and test machine learning algorithms using a variety of datasets and validation techniques [47,48]. However, this can be time-consuming and expensive, which may limit the practicality of using machine learning in certain applications.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, some machine learning algorithms may lack Sustainability 2023, 15, 9738 3 of 37 human oversight, which can lead to unintended consequences or errors if not carefully monitored and controlled by human experts [49,50]. Recent developments in machine learning (ML) for civil Engineering focused on addressing the challenges associated with interpreting ML results and the lack of userfriendly tools [47,48]. Explainable ML models were developed to provide transparency and interpretability, allowing users to understand the reasoning behind the predictions.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Machine Learning Techniques and Applications in Soil Improvement Using Green Materials

et al. 2023

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According to an extensive evaluation of published studies, there is a shortage of research on systematic literature reviews related to machine learning prediction techniques and methodologies in soil improvement using green materials. A literature review suggests that machine learning algorithms are effective at predicting various soil characteristics, including compressive strength, deformations, bearing capacity, California bearing ratio, compaction performance, stress–strain behavior, geotextile pullout strength behavior, and soil classification. The current study aims to comprehensively evaluate recent breakthroughs in machine learning algorithms for soil improvement using a systematic procedure known as PRISMA and meta-analysis. Relevant databases, including Web of Science, ScienceDirect, IEEE, and SCOPUS, were utilized, and the chosen papers were categorized based on: the approach and method employed, year of publication, authors, journals and conferences, research goals, findings and results, and solution and modeling. The review results will advance the understanding of civil and geotechnical designers and practitioners in integrating data for most geotechnical engineering problems. Additionally, the approaches covered in this research will assist geotechnical practitioners in understanding the strengths and weaknesses of artificial intelligence algorithms compared to other traditional mathematical modeling techniques.

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Predicting Mechanical Properties in Geopolymer Mortars, Including Novel Precursor Combinations, Through XGBoost Method

Yilmaz,

Cakmak,

Kurt

et al. 2024

Arab J Sci Eng

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Concrete is the most widely used material in the building industry due to its affordability, durability, and strength. However, considering carbon emissions, it is believed that concrete will be replaced by geopolymers in the future. As numerous parameters significantly affect the strength of geopolymers, the performance of potential algorithms for strength prediction needs to be evaluated for different binders to select an appropriate algorithm. This study employs machine learning approaches to provide the best prediction method for the flexural strength and compressive strength of geopolymers. A new dataset containing 533 compressive strength and 533 flexural strength values of geopolymers with different binders such as waste glass (GW), obsidian (OB), and fly ash was created. The best prediction solution, with R2 = 0.981 for compressive strength and R2 = 0.898 for flexural strength, was obtained from the extreme gradient boosting (XGBoost) algorithm. Additionally, several other machine learning models were employed, including linear regression, k-nearest neighbors, deep neural network, and random forest, with corresponding determination coefficient (R2) values of 0.763, 0.804, 0.93, and 0.96, respectively. These models were trained and evaluated using a dataset encompassing features such as binder types, age, and heat, to forecast the mechanical properties of geopolymers. Among these models, XGBoost demonstrated the highest R2 value, indicating superior performance in predicting both compressive and flexural strengths. The findings of this study provide valuable insights into the selection of appropriate machine learning algorithms for predicting mechanical properties in geopolymers, thus contributing to advancements in sustainable construction materials.

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A Data-Driven Influential Factor Analysis Method for Fly Ash–Based Geopolymer Using Optimized Machine-Learning Algorithms

Cited by 15 publications

References 44 publications

Compressive Strength Prediction of Fly Ash Geopolymer Concrete Using Support Vector and Random Forest Regression

Compressive Strength Prediction of Fly Ash Geopolymer Concrete Using Support Vector and Random Forest Regression

A Systematic Review of Machine Learning Techniques and Applications in Soil Improvement Using Green Materials

Predicting Mechanical Properties in Geopolymer Mortars, Including Novel Precursor Combinations, Through XGBoost Method

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