Machine Learning and RSM for Strength Forecasting in Sustainable SCGC

Abstract

This research focuses on the predictive modeling of flexural (Ff) and splitting tensile (Ft) strengths in Self-Compacting Geopolymer Concrete (SCGC) to support sustainable mix design optimization. A curated dataset comprising 544 experimental records was utilized to train and evaluate eight supervised machine learning (ML) algorithms. These included Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Random Forests, Gradient Boosting, CN2 Rule Induction, Naïve Bayes, Decision Trees, and Stochastic Gradient Descent. The predictive performance of each model was assessed using multiple statistical metrics, such as RMSE, R², and accuracy percentage. Among the models, SVM and KNN achieved the highest precision, with R² values of 0.99 and RMSE as low as 0.10 MPa. Additionally, statistical techniques were applied to identify influential input variables, confirming the dominant role of binder constituents in determining tensile-related strength. The models demonstrated strong generalization on unseen data and minimal sensitivity to activator dosage or curing age. These results validate the effectiveness of ML-driven tools for SCGC prediction and offer a scalable framework for integrating data analytics into sustainable concrete design and performance optimization.

ABSTRAK: Kajian ini memfokuskan kepada pemodelan ramalan bagi kekuatan lenturan (Ff) dan tegangan belahan (Ft) dalam Konkrit Geopolimer Pemadat Kendiri (SCGC) bagi menyokong pengoptimuman reka bentuk campuran mampan. Satu set data terpilih yang merangkumi 544 rekod eksperimen telah digunakan bagi melatih dan menilai lapan algoritma pembelajaran mesin (ML) terselia. Algoritma tersebut termasuk Mesin Sokongan Vektor (SVM), K-Nearest Neighbors (KNN), Rawak Forests, Gradient Boosting, CN2 Rule Induction, Naïve Bayes, Pokok Keputusan, dan Stochastic Gradient Descent. Prestasi ramalan setiap model dinilai menggunakan pelbagai metrik statistik seperti RMSE, R², dan peratusan ketepatan. Antara model tersebut, SVM dan KNN mencapai ketepatan tertinggi dengan nilai R² sebanyak 0.99 dan RMSE serendah 0.10 MPa. Tambahan, teknik statistik turut digunakan bagi mengenal pasti pemboleh ubah input berpengaruh, sekali gus mengesahkan peranan dominan konstituen pengikat dalam menentukan kekuatan berkaitan tegangan. Model yang dibangunkan menunjukkan keupayaan generalisasi yang kukuh terhadap data baharu serta kepekaan minimum terhadap dos pengaktif atau umur pengerasan. Dapatan ini mengesahkan keberkesanan alat berasaskan ML bagi meramal SCGC dan menawarkan kerangka boleh skala bagi mengintegrasikan analitik data ke dalam reka bentuk konkrit mampan serta pengoptimuman prestasi.