Efektif Kendiliğinden Yerleşen Hafif Beton Dayanımı Tahmini için Farklı Makine Öğrenmesi Algoritmalarının Karşılıklı Değerlendirilmesi

Yıl 2025, Cilt: 37 Sayı: 1, 251 - 261, 27.03.2025

Hakan Güler , Mustafa Ulaş , Merve Açıkgenç Ulaş

https://doi.org/10.35234/fumbd.1552422

Öz

İnşaat mühendisliği literatüründe, özel beton türlerinin karışım tasarımını modellemek ve bu amaçla betonun bazı özelliklerini tahmin etmek için makine öğrenmesi yöntemleri sıklıkla kullanılmaktadır. Geleneksel karışım tasarımı hesapları, istenilen özellikte betonun üretilebilmesi için bir deneme yanılma süreci gerektirmektedir. Bu süreçte zaman, işgücü ve malzeme kayıpları yaşanır. Makine öğrenmesi yöntemleri kullanılarak deneme yanılma karışımlarının sayısı azaltılabileceğinden, gerek duyulan özelliklere sahip betonun üretilebilmesi kolaylaşabilir. Burada söz konusu malzeme Kendiliğinden Yerleşen Hafif Beton (KYHB) gibi özel bir tür beton ise karışım tasarımının pratik hale getirilmesi ile üretim sürecinin hızlandırılması daha büyük önem kazanır. Hem kendiliğinden yerleşen özellikte hem de hafif agrega içeren bu beton türünün istenilen özelliklerde üretilebilmesi süreci geleneksel betona göre daha zordur. Bu çalışmada, KYHB’nin basınç dayanımını tahmin etmek için dört farklı makine öğrenmesi algoritması karşılaştırılmalı değerlendirilmiştir. Çalışmanın amacı, KYHB bileşiminden basınç dayanımının tahmin edilmesidir. Ek olarak makine öğrenmesi algoritmalarının beton performansını tahmin etmedeki başarısını kanıtlamak ve böylece yöntemin öncelikle beton karışım hesabı için kullanımını artırmaktır. Bu amaçla, KYHB karışımının bazı deneysel özelliklerini ve bileşimini içeren bir veri seti ile Lineer Regresyon, Gauss Süreç Regresyonu, Quadratik ve Kernel Destek Vektör Makinası yöntemleri kullanılarak 4 farklı tahmin modeli geliştirilmiştir. Geliştirilen modellerin performansı, deneysel sonuçlar ile model çıktısı arasındaki doğrusal korelasyon oranı ve hata miktarları açısından değerlendirilmiştir. Gauss Süreç Regresyonu modeli ise bu çalışmada en başarılı makine öğrenmesi metodu olmuştur.

Anahtar Kelimeler

Kendiliğinden Yerleşen Hafif Beton, Lineer Regresyon, Gauss Süreç Regresyonu, Destek Vektör Makineleri

Mutual Evaluation of Different Machine Learning Algorithms for Effective Self-Compacting Lightweight Concrete Strength Prediction

Öz

Machine Learning (ML) methods are frequently used to model the mix design and to predict some properties of concrete. Traditional mix design calculations require a trial-and-error process to produce concrete with the desired properties. In this process, time, labor, and material losses are experienced. By using ML methods, the number of trial-and-error mixtures can be reduced, making it easier to produce concrete with the required properties. As a special type of concrete, Self-Compacting Lightweight Concrete (SCLWC), it becomes more important to speed up the production process by making the mix design practical. SCLWC, which contains both self-compactibility and lightweight aggregates, is more difficult to produce with the desired properties than conventional concrete. In this study, four different ML algorithms were comparatively evaluated to predict the compressive strength of SCLWC. The aim of the study is to predict the compressive strength from the composition of SCLWCC. In addition, to prove the success of ML in predicting concrete performance and thus increase the use of the method primarily for concrete mix calculation. For this purpose, 4 different prediction models were developed using Linear Regression, Gaussian Process Regression, Quadratik and Kernel Support Vector Machine methods with a data set containing some experimental properties and composition of SCLWC mix. The performance of the developed models was evaluated in terms of the linear correlation ratio between the experimental results and the model output and the amount of error. The Gaussian Process Regression model was the most successful ML method in this study.

Anahtar Kelimeler

Self-compacting Lightweight Concrete, Linear Regression, Gaussian Process Regression, Support Vector Machines

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