Predicting Autogenous Shrinkage of Concrete Including Superabsorbent Polymers and Other Cementitious Ingredients Using Convolution-based Algorithms

Authors

  • Mohammad Sadegh Barkhordari
    Affiliation
    Department of Civil Engineering, Amirkabir University of Technology (Tehran Polytechnic), Hafez Ave, Valiasr Square, 1591634311 Tehran, Iran
  • Sobhan Ghavaminejad
    Affiliation
    Faculty of Engineering, Pardis Science and Technology Branch, Islamic Azad University, Damavand Ave, 1658174583 Pardis, Iran
  • Mohsen Tehranizadeh
    Affiliation
    Department of Civil Engineering, Amirkabir University of Technology (Tehran Polytechnic), Hafez Ave, Valiasr Square, 1591634311 Tehran, Iran
https://doi.org/10.3311/PPci.23568

Abstract

In this paper, the effectiveness of ensemble convolution-based deep learning models is evaluated for predicting autogenous shrinkage/swelling of cementitious materials. Various ensemble learning techniques are employed, including Simple Average Ensemble, Snapshot Ensemble, and Stacked Generalization, to develop predictive models. The models are trained and evaluated using performance metrics such as Root Mean Squared Error, Coefficient of Determination, Overall Index of model performance, Mean Absolute Error, and 95% Uncertainty. The results show that the integrated stacking model (ISM) outperforms other models in terms of predictive accuracy. Furthermore, the SHapley Additive exPlanation (SHAP) technique was used to interpret the ISM model. The analysis reveals that the most influential factors affecting shrinkage predictions include time, aggregate to cement ratio (A/C), superabsorbent polymer (SAP) content, water to binder ratio, cement content, water to cement ratio, and silica fume content. Also, the ISM model was compared with models developed previously by other researchers, namely, K-Nearest Neighbors (KNN), Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB). With the lowest RMSE and MAE values, the ISM model has exceptional accuracy, demonstrating its capacity to create predictions that closely resemble observed values. Additionally, it has the highest coefficient of determination value, demonstrating its effectiveness in explaining a sizable percentage of the data variance. The Overall Index (OI) statistic shows that the ISM model performs exceptionally well, indicating that it captures more of the underlying information in the data. Additionally, it displays lower 95% confidence intervals, demonstrating greater assurance in its forecasts.

Keywords:

concrete shrinkage, machine learning, autogenous shrinkage prediction, ensemble network

Citation data from Crossref and Scopus

Published Online

2024-06-12

How to Cite

Barkhordari, M. S., Ghavaminejad, S., Tehranizadeh, M. “Predicting Autogenous Shrinkage of Concrete Including Superabsorbent Polymers and Other Cementitious Ingredients Using Convolution-based Algorithms”, Periodica Polytechnica Civil Engineering, 68(4), pp. 1098–1121, 2024. https://doi.org/10.3311/PPci.23568

Issue

Section

Research Article