Prediction of Compressive Strength of Fly Ash-Based Geopolymer Concrete Based on Machine Learning

SHI Xufeng; DING Danjing; SONG Huiping; WU Haibin; AN Quan

doi:10.3724/j.gyjzG24102302

Volume 55 Issue 6

Jun. 2025

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SHI Xufeng, DING Danjing, SONG Huiping, WU Haibin, AN Quan. Prediction of Compressive Strength of Fly Ash-Based Geopolymer Concrete Based on Machine Learning[J]. INDUSTRIAL CONSTRUCTION, 2025, 55(6): 278-287. doi: 10.3724/j.gyjzG24102302

Citation:

SHI Xufeng, DING Danjing, SONG Huiping, WU Haibin, AN Quan. Prediction of Compressive Strength of Fly Ash-Based Geopolymer Concrete Based on Machine Learning[J]. INDUSTRIAL CONSTRUCTION, 2025, 55(6): 278-287. doi: 10.3724/j.gyjzG24102302

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Prediction of Compressive Strength of Fly Ash-Based Geopolymer Concrete Based on Machine Learning

doi: 10.3724/j.gyjzG24102302

SHI Xufeng^1,2,3,
DING Danjing^1,2,3,
SONG Huiping^{1,2,3
,
,},
WU Haibin^1,2,3,
AN Quan^1,2,3

1. Institute of Resources and Environmental Engineering, Shanxi University, Taiyuan 030006, China;
2. Yellow River Basin Resource Efficiency and Carbon Reduction Engineering Research Center of Ministry of Education, Taiyuan 030006, China;
3. Shanxi Yellow River Laboratory, Taiyuan 030006, China

Received Date: 2024-10-23

Abstract

Abstract

Machine learning technology is used to predict the compressive strength of fly ash-based geopolymer concrete. A total of 112 sets of data were collected， and 11 variables such as fly ash content and composition were selected as inputs and compressive strength was output variable， which were divided into training sets and test sets according to the proportion of 70% and 30% for analysis. A single machine learning algorithm （SVM） and two integrated machine learning algorithms （RF， Adaboost） were used to construct the prediction model. The performance of the models was evaluated using the training set and the test set， and the results showed that all models showed strong predictive ability on both sets. Among them， RF model had the best performance， its test set R²=0.91，Δ_MES=14.76，Δ_RMSE=3.84，Δ_MAE=2.89. The SHAP value algorithm was used to determine that fly ash content， Al₂O₃ and SiO₂ content in fly ash， NaOH molar concentration and water content were the five most important factors affecting the compressive strength of concrete. When the fly ash dosage was greater than 400 kg/m³， the SiO₂ content in fly ash was less than 60%， and the NaOH concentration was relatively high， all these factors contributed to the enhancement of concrete strength. When the Al₂O₃ content in fly ash was greater than 15%， the concrete strength increased with the increase of Al₂O₃ content. Excessive water addition would increase the porosity of concrete， thereby reduced its compressive strength.
- fly ash,
- geopolymer concrete,
- compressive strength,
- machine learning,
- interpretability

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References(34)

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