Predicting the Compressive Strength of PVC-Confined Concrete via Machine Learning

Yıl 2025, Cilt: 15 Sayı: 2, 568 - 580, 01.06.2025

Ahmet Emin Kurtoğlu

https://doi.org/10.21597/jist.1584930

Öz

Polyvinyl Chloride (PVC) is a promising sustainable alternative to traditional materials for confining concrete in structural applications due to its corrosion resistance, durability, and cost-effectiveness. The present research is focused on the axial compressive strength of PVC-confined concrete short columns with machine learning models for superior predictive accuracy. A database gathered from FEA simulations was utilized to train the Artificial Neural Network (ANN) and Support Vector Machine (SVM) models, in which the performance of each model was compared with an available empirical formula. The ANN and SVM models could achieve a high predictive accuracy with R² values close to 1.0 and smaller RMSE values than those by traditional empirical approaches. Results have shown that machine-learning models succeed in capturing complex interactions among the parameters, including PVC thickness, column diameter, and concrete compressive strength, providing a versatile and powerful method for strength prediction. These models offer construction engineers a rapid, cost-effective tool for predicting PVC-confined concrete column strengths without extensive physical testing, potentially accelerating the adoption of sustainable materials in structural design. By reducing experimental costs and design time, the approach demonstrates significant practical value for innovative construction technologies.

Anahtar Kelimeler

Confined concrete , Compressive strength , PVC , Machine learning , Finite element analysis

PVC İle Sargılanmış Betonun Eksenel Basınç Dayanımının Makine Öğrenmesi İle Tahmini

Öz

Polivinil Klorür (PVC), korozyon direnci, dayanıklılığı ve maliyet etkinliği sayesinde betonun yapı uygulamalarında sargılanması için geleneksel malzemelere bir alternatif olma potansiyeline sahip sürdürülebilir bir seçenektir. Bu araştırma, PVC ile sargılanmış beton kısa kolonların eksenel basınç dayanımı üzerine yoğunlaşmakta ve yüksek tahmin kapasiteli makine öğrenimi modelleri kullanılmaktadır. Sonlu Elemanlar Analizi (FEA) simülasyonlarından elde edilen bir veri tabanı kullanılarak, Yapay Sinir Ağı (YSA) ve Destek Vektör Makinesi (DVM) modelleri eğitilmiştir ve her modelin performansı mevcut bir ampirik modelle karşılaştırılmıştır. YSA ve DVM modelleri, 1.0’a yakın R² değerleri ve geleneksel ampirik yaklaşımlara kıyasla daha düşük RMSE değerleri ile yüksek tahmin doğruluğu elde edebilmiştir. Sonuçlar, makine öğrenimi modellerinin, PVC kalınlığı, kolon çapı ve beton basınç dayanımı gibi parametreler arasındaki karmaşık etkileşimleri başarılı bir şekilde yakalayarak dayanım tahmini için esnek ve güçlü bir yöntem sağladığını göstermiştir. Bu modeller, inşaat mühendislerine kapsamlı fiziksel testlere gerek kalmadan PVC ile sargılanmış beton kolon mukavemetlerini tahmin etmek için hızlı ve uygun maliyetli bir araç sunarak yapısal tasarımda sürdürülebilir malzemelerin benimsenmesini potansiyel olarak hızlandırmaktadır. Bu yaklaşım, deneysel maliyetleri ve tasarım süresini azaltarak yenilikçi inşaat teknolojileri için önemli bir pratik değer ortaya koymaktadır.

Anahtar Kelimeler

Sargılanmış beton , Basınç dayanımı , PVC , Makine öğrenmesi , Sonlu Elemanlar Analizi

Kaynakça

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