MAKİNE ÖĞRENMESİ ALGORİTMALARI KULLANILARAK BETON BASINÇ DAYANIMININ ÖZDİRENÇ YÖNTEMİNDEN TAHMİNİ

Öz

Betonun tek eksenli basınç dayanımının doğru biçimde belirlenmesi, yapı güvenliği ve performans değerlendirmesi açısından kritik öneme sahiptir. Geleneksel dayanım belirleme yöntemleri tahribatlı deneylere dayandığından özellikle mevcut yapılarda uygulanmaları sınırlıdır. Bu nedenle doğru akım özdirenç yöntemine dayalı tahribatsız test (NDT) yöntemleri ile makine öğrenmesi algoritmalarının birlikte kullanımı önemli bir alternatif oluşturmaktadır. Bu çalışmada betonun basınç dayanımı, elektrik özdirenç, su/çimento oranı (w/c) ve donatı çapı verileri kullanılarak RF (Rastgele Orman) ve XGBoost (Aşırı Gradyan Artırma) algoritmaları ile tahmin edilmiştir. Toplam 334 beton numunesinden elde edilen deneysel veriler kullanılmıştır. Oluşturulan model ile basınç dayanımı arasında orta düzeyde pozitif ilişkiler bulunmuştur (R=0.77-0.79). RF modeli, GridSearchCV kullanılarak yapılan hiperparametre optimizasyonu ile en iyi sonucu üretmiştir. Model performansı R, RMSE ve MAE ile değerlendirilmiştir. Elde edilen sonuçlara göre RF modeli R=0.79, RMSE=12.79 ve MAE= 8.39 değeri ile orta düzeyde tahmin başarısı göstermiştir. Bulgular, elektrik özdirenç, su/çimento oranı ve donatı çapı ile beton dayanımı arasında orta düzeyde bir model kurulabileceğini göstermektedir.

Anahtar Kelimeler

• Beton Dayanımı
• Özdirenç
• Makine Öğrenmesi
• Random Forest
• XGBoost

Etik Beyan

Çıkar Çatışması (Conflict of Interest) Yazar tarafından herhangi bir çıkar çatışması beyan edilmemiştir.

ESTIMATION OF CONCRETE COMPRESSIVE STRENGTH FROM RESISTIVITY METHOD USING MACHINE LEARNING ALGORITHMS

Öz

Accurate determination of the uniaxial compressive strength of concrete is critical for structural safety and performance evaluation. Traditional strength determination methods rely on destructive testing, limiting their application, especially in existing structures. Therefore, the combined use of non-destructive testing (NDT) methods based on direct current resistivity method and machine learning algorithms constitutes a significant alternative. In this study, the compressive strength of concrete was estimated using RF (Random Forest) and XGBoost (Extreme Gradient Boosting) algorithms with electrical resistivity, water/cement ratio, and reinforcement diameter data. Experimental data obtained from a total of 334 concrete samples were used. A moderately positive correlation was found between the model developed and compressive strength (R=0.77-0.79). The RF model produced the best result with hyperparameter optimization using GridSearchCV. Model performance was evaluated using R, RMSE and MAE. According to the results obtained, the RF model showed a moderate level of prediction success with R=0.79, RMSE=12.79, and MAE=8.39. The findings indicate that a moderate model can be established between electrical resistivity, water/cement ratio, reinforcement diameter and concrete strength.

Anahtar Kelimeler

• Concrete Strength
• Resistivity
• Machine Learning
• Random Forest
• XGBoost

Etik Beyan

No conflict of interest was declared by the author

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