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Dergiler / Bitlis Eren Üniversitesi Fen Bilimleri Dergisi / 2026 / Volume: 15 Issue: 2

Prediction And Analysis Of Concrete Compressive Strength By Machine Learning Methods

Remzi Gürfidan(ISPARTA UYGULAMALI BİLİMLER ÜNİVERSİTESİ)0000-0002-4899-2219
Kemal Erten(ISPARTA UNIVERSITY OF APPLIED SCIENCES)0000-0001-5181-4109
Sayfa
—
Yayın Tarihi
2026-06-30
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The results highlight that boosting-based machine learning models not only achieve high accuracy but also provide interpretable and robust predictions when evaluated through comprehensive error and explainability analyses.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Concrete compressive strength (CCS) is a critical parameter directly affecting the load-bearing capacity, durability, and overall safety of engineering structures.
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Temel Bulgular & Veri:In this study, thirteen different machine learning algorithms were applied to a well-established dataset (1030 samples, 8 input parameters) to estimate concrete compressive strength.
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Sonuç & Katkı:The results highlight that boosting-based machine learning models not only achieve high accuracy but also provide interpretable and robust predictions when evaluated through comprehensive error and explainability analyses.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:1030 samples
⚙️Değişkenler:Concrete Compressive Strength · Machine Learning · Water-to-Binder Ratio

Abstract

Concrete compressive strength (CCS) is a critical parameter directly affecting the load-bearing capacity, durability, and overall safety of engineering structures. Traditional experimental approaches for determining CCS are time-consuming and costly, making predictive models an attractive alternative. In this study, thirteen different machine learning algorithms were applied to a well-established dataset (1030 samples, 8 input parameters) to estimate concrete compressive strength. Unlike many previous studies using the Yeh dataset that primarily emphasize prediction accuracy of individual models, this work presents a systematic multi-model comparison within a unified hyperparameter optimization framework. In addition to conventional performance metrics, permutation importance and SHAP-based explainability analyses are jointly employed, and detailed error evaluations are conducted across curing age and water-to-binder ratio subgroups to enhance engineering interpretability. Among the models tested, the CatBoost algorithm demonstrated the highest predictive performance (R² = 0.9469, RMSE = 3.70), followed closely by XGBoost, Gradient Boosting, and a stacking ensemble model. The results highlight that boosting-based machine learning models not only achieve high accuracy but also provide interpretable and robust predictions when evaluated through comprehensive error and explainability analyses.

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Bu Makaleye Atıf Yap

Gürfidan, R., Erten, K. (2026). Prediction And Analysis Of Concrete Compressive Strength By Machine Learning Methods. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, Volume: 15 Issue: 2. https://doi.org/10.17798/bitlisfen.1816694

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