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<p>Artificial intelligence and machine learning are increasingly applied in civil engineering to predict material performance and optimize mix design. This study compares three ensemble learning approaches, Boosting, Bagging, and Stacking, for predicting the slump and compressive strength (CS) of eco-friendly mortars produced in the laboratory by partially replacing cement with glass powder (GP) and reinforcing with flax fibers (FF) and polypropylene fibers (PPF). Seven input variables were considered: the amounts of cement, GP, water, water-to-binder ratio (W/B), FF, PPF, and the superplasticizer dosage. A database of 580 experimental mixtures was created and used to train six predictive models: XGBoost and LightGBM (Boosting), Random Forest and Extra Trees (Bagging), and two hybrid Stacking models. Model performance was assessed using statistical metrics (R<sup>2</sup>, RMSE, MAE), and SHAP (Shapley Additive Explanations) analysis was conducted to enhance interpretability and assess the influence of each input variable. This research is among the first to model both fresh (spread) and hardened (CS) properties of sustainable mortars with dual reinforcement using natural and synthetic fibers. The study highlights the critical role of W/B and SP dosage in workability and demonstrates the beneficial effect of GP in densifying the matrix and improving strength. The addition of FF and PPF enhanced CS by improving crack control, although higher fiber contents led to reduced workability. Overall, the study delivers both an original dataset and innovative mortar formulations, while demonstrating that ensemble ML techniques—particularly Stacking—offer superior predictive capability and practical support for eco-mortar mix design. This study is limited to short-term properties and a specific dataset; future work will expand the database and include durability assessments for broader applicability.</p>

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Machine learning models with SHAP for performance prediction of eco-friendly fiber reinforced mortars with glass waste

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月20日

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  • Samra Rahmani,
  • Ahmed Abderraouf Belkadi,
  • Yacine Achour,
  • Eyad Alsuhaibani,
  • Amirouche Berkouche,
  • Abdellah Douadi,
  • Adrian Chajec

摘要

Artificial intelligence and machine learning are increasingly applied in civil engineering to predict material performance and optimize mix design. This study compares three ensemble learning approaches, Boosting, Bagging, and Stacking, for predicting the slump and compressive strength (CS) of eco-friendly mortars produced in the laboratory by partially replacing cement with glass powder (GP) and reinforcing with flax fibers (FF) and polypropylene fibers (PPF). Seven input variables were considered: the amounts of cement, GP, water, water-to-binder ratio (W/B), FF, PPF, and the superplasticizer dosage. A database of 580 experimental mixtures was created and used to train six predictive models: XGBoost and LightGBM (Boosting), Random Forest and Extra Trees (Bagging), and two hybrid Stacking models. Model performance was assessed using statistical metrics (R2, RMSE, MAE), and SHAP (Shapley Additive Explanations) analysis was conducted to enhance interpretability and assess the influence of each input variable. This research is among the first to model both fresh (spread) and hardened (CS) properties of sustainable mortars with dual reinforcement using natural and synthetic fibers. The study highlights the critical role of W/B and SP dosage in workability and demonstrates the beneficial effect of GP in densifying the matrix and improving strength. The addition of FF and PPF enhanced CS by improving crack control, although higher fiber contents led to reduced workability. Overall, the study delivers both an original dataset and innovative mortar formulations, while demonstrating that ensemble ML techniques—particularly Stacking—offer superior predictive capability and practical support for eco-mortar mix design. This study is limited to short-term properties and a specific dataset; future work will expand the database and include durability assessments for broader applicability.