Korozyona Uğramış Betonarme Kirişler için Kesme Dayanımını Belirlemeye Yönelik Basitleştirilmiş Bir Yaklaşım

Yapı ömrü boyunca yapım ya da kullanım kusuru sayılabilecek korozyon hasarı yapı elemanları için önemli bir parametredir. Korozyon sebebiyle betonarme elemanlarda dayanım kaybı görülmekte bu da yapı performansını etkileyen önemli bir parametre olmaktadır. Donatısı korozyona uğramış kirişlerin kayma mukavemetinin belirlenmesi, yapı elamanında dayanım kaybı, tasarım ve güçlendirme kriterleri açısından önemli olmaktadır. Bu çalışmada yapay zekâ algoritmaları ile betonarme kiriş deneylerinden elde edilen kesme dayanımı değerlerinin deneysel çalışmaya gerek kalmadan belirlenmesi amaçlanmaktadır. Bu kapsamda literatürde gerçekleştirilmiş korozyona uğramış betonarme kiriş deneyleri verileri toparlanmış, deney parametrelerine bağlı olarak kirişlerin nihai kesme dayanımı değerleri tespit edilmiştir. Dayanım tahmini makine öğrenmesi regresyon algoritmalarından XGBoost ve AdaBoost ile gerçekleştirilmiştir. Elde edilen sonuçlar R2, RMSE ve MAE performans metrikleri ile değerlendirilmiş ve yüksek tahmin başarısına ulaşılmıştır. Çalışma göstermektedir ki deneysel verilere bağlı öğrenme gerçekleştirebilen bu sistemler ile üretim parametreleri bilinen ve korozyona uğramış kesme dayanımı değerlerini deneysel ölçümlere ihtiyaç duymadan tahmin etmek mümkündür.

Anahtar Kelimeler:

Korozyonlu kiriş, kesme dayanımı, makine öğrenmesi, XGBoost, AdaBoost

A Simplified Approach to Determine Shear Strength for Corroded RC Beams

Corrosion damage, which can be considered as a construction or usage defect during the life of the structure, is an important parameter for the structural elements. Strength loss is observed in reinforced concrete (RC) elements due to corrosion, which is an important parameter affecting the performance of the building. Determining the shear strength of beams with corroded reinforcement is important in terms of strength loss, design, and reinforcement criteria in the structural element. In this context, the data of the corroded RC beam experimental tests carried out in the literature were collected and the ultimate shear strength values of the beams were determined depending on the test parameters. Strength estimation was performed with machine learning regression algorithms XGBoost and AdaBoost. The results obtained were evaluated with R2, RMSE and MAE performance metrics and high estimation success was achieved. The study shows that with these systems, which can perform learning based on experimental data, it is possible to estimate the shear strength values of corroded beams with known production parameters without the need for experimental measurements.

Keywords:

Corroded beam; shear strength, machine learning; XGBoost, AdaBoost.,

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