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Developing interpretable machine learning-Shapley additive explanations model for unconfined compressive strength of cohesive soils stabilized with geopolymer

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  • Anh Quan Ngo
  • Linh Quy Nguyen
  • Van Quan Tran

Abstract

This paper seeks to develop an interpretable Machine Learning (ML) model for predicting the unconfined compressive strength (UCS) of cohesive soils stabilized with geopolymer at 28 days. Four models including Random Forest (RF), Artificial Neuron Network (ANN), Extreme Gradient Boosting (XGB), and Gradient Boosting (GB) are built. The database consists of 282 samples collected from the literature with three different types of cohesive soil stabilized with three geopolymer categories including Slag-based geopolymer cement, alkali-activated fly ash geopolymer and slag/fly ash-based geopolymer cement. The optimal model is selected by comparing their performances with each other. The values of hyperparameters are tuned by Particle Swarm Optimization (PSO) algorithm and K-Fold Cross Validation. Statistical indicators show the superior performance of the ANN model with three metrics performance such as coefficient of determination R2 = 0.9808, Root Mean Square Error RMSE = 0.8808 MPa and Mean Absolute Error MAE = 0.6344 MPa. In addition, a sensitivity analysis was performed to determine the influence of different input parameters on the UCS of cohesive soils stabilized with geopolymer. The order of feature effect can be ordered in descending order using the Shapley additive explanations (SHAP) value as follows: Ground granulated blast slag content (GGBFS) > Liquid limit (LL) > Alkali/Binder ratio (A/B) > Molarity (M) > Fly ash content (FA) > Na/Al > Si/Al. The ANN model can obtain the best accuracy using these seven inputs. LL has a negative correlation with the growth of unconfined compressive strength, whereas GGBFS has a positive correlation.

Suggested Citation

  • Anh Quan Ngo & Linh Quy Nguyen & Van Quan Tran, 2023. "Developing interpretable machine learning-Shapley additive explanations model for unconfined compressive strength of cohesive soils stabilized with geopolymer," PLOS ONE, Public Library of Science, vol. 18(6), pages 1-22, June.
    • Handle: RePEc:plo:pone00:0286950
    DOI: 10.1371/journal.pone.0286950
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    References listed on IDEAS

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    1. Tuan Anh Pham & Van Quan Tran, 2022. "Developing random forest hybridization models for estimating the axial bearing capacity of pile," PLOS ONE, Public Library of Science, vol. 17(3), pages 1-23, March.
    2. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    3. Husein Ali Zeini & Duaa Al-Jeznawi & Hamza Imran & Luís Filipe Almeida Bernardo & Zainab Al-Khafaji & Krzysztof Adam Ostrowski, 2023. "Random Forest Algorithm for the Strength Prediction of Geopolymer Stabilized Clayey Soil," Sustainability, MDPI, vol. 15(2), pages 1-15, January.
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