Comparing Machine Learning Regression Models for Early-Age Compressive Strength Prediction of Recycled Aggregate Concrete

Muhammed Ulucan; Güngör Yıldırım; Bilal Alatas; Kürşat Esat Alyamaç

Makale detayları

Comparing Machine Learning Regression Models for Early-Age Compressive Strength Prediction of Recycled Aggregate Concrete

Yazarlar:

Muhammed Ulucan | Güngör Yıldırım | Bilal Alatas | Kürşat Esat Alyamaç

Yayıncı:

Fırat University

Ana Başlıklar:

Yapay Zeka (Diğer) | Yapı Malzemeleri

Dergi:

Fırat Üniversitesi Mühendislik Bilimleri Dergisi Vol.36 Issue 2, pp.563-580

Belge Tipi:

Makale

Yayın Tarihi:

9/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001317060

pISSN:

1308-9072

Anahtar kelimeler:

Özet:

 A branch of artificial intelligence called machine learning is well-positioned as a prediction method that can take into consideration several influencing factors and complex inter-factor connections. Without being specifically trained to do so, these machine learning models have the ability to generalize, predict, and learn from data. Regression theory is a key topic in statistical modelling and machine learning. The main goal of this study is to compare the performance of several popular machine learning regression models for predicting the early-age compressive strength of concretes made from recycled concrete aggregates from a structure that demolished following the Sivrice-Elazig earthquake on January 24, 2020. Early-age concrete compressive strength is even more crucial due to factors like the fact that there are thousands of newly built structures in the aftermath of the earthquake, the quick manufacturing of these structures, and the completion of the project in the lowest amount of time. Determining the early-age concrete strength with high accuracy and in a useful manner is crucial for this reason. Seven different classical machine learning algorithms were employed in this study to achieve all of these goals. Early-age concrete compressive strength values were considered for 1 and 3 days. The relationship between the experimental results and the predicted outcomes of the employed algorithms was investigated, and a thorough comparison of these intelligent regression algorithms was conducted. Within the scope of sustainable development and circular economy goals, it is thought that this article will make significant contributions to the literature in terms of utilizing these waste materials and determining the early-age compressive strengths of the concretes produced with high accuracy.

 A branch of artificial intelligence called machine learning is well-positioned as a prediction method that can take into consideration several influencing factors and complex inter-factor connections. Without being specifically trained to do so, these machine learning models have the ability to generalize, predict, and learn from data. Regression theory is a key topic in statistical modelling and machine learning. The main goal of this study is to compare the performance of several popular machine learning regression models for predicting the early-age compressive strength of concretes made from recycled concrete aggregates from a structure that demolished following the Sivrice-Elazig earthquake on January 24, 2020. Early-age concrete compressive strength is even more crucial due to factors like the fact that there are thousands of newly built structures in the aftermath of the earthquake, the quick manufacturing of these structures, and the completion of the project in the lowest amount of time. Determining the early-age concrete strength with high accuracy and in a useful manner is crucial for this reason. Seven different classical machine learning algorithms were employed in this study to achieve all of these goals. Early-age concrete compressive strength values were considered for 1 and 3 days. The relationship between the experimental results and the predicted outcomes of the employed algorithms was investigated, and a thorough comparison of these intelligent regression algorithms was conducted. Within the scope of sustainable development and circular economy goals, it is thought that this article will make significant contributions to the literature in terms of utilizing these waste materials and determining the early-age compressive strengths of the concretes produced with high accuracy.

 A branch of artificial intelligence called machine learning is well-positioned as a prediction method that can take into consideration several influencing factors and complex inter-factor connections. Without being specifically trained to do so, these machine learning models have the ability to generalize, predict, and learn from data. Regression theory is a key topic in statistical modelling and machine learning. The main goal of this study is to compare the performance of several popular machine learning regression models for predicting the early-age compressive strength of concretes made from recycled concrete aggregates from a structure that demolished following the Sivrice-Elazig earthquake on January 24, 2020. Early-age concrete compressive strength is even more crucial due to factors like the fact that there are thousands of newly built structures in the aftermath of the earthquake, the quick manufacturing of these structures, and the completion of the project in the lowest amount of time. Determining the early-age concrete strength with high accuracy and in a useful manner is crucial for this reason. Seven different classical machine learning algorithms were employed in this study to achieve all of these goals. Early-age concrete compressive strength values were considered for 1 and 3 days. The relationship between the experimental results and the predicted outcomes of the employed algorithms was investigated, and a thorough comparison of these intelligent regression algorithms was conducted. Within the scope of sustainable development and circular economy goals, it is thought that this article will make significant contributions to the literature in terms of utilizing these waste materials and determining the early-age compressive strengths of the concretes produced with high accuracy.

 Yapay zekanın makine öğrenimi olarak adlandırılan bir dalı, çeşitli etki faktörlerini ve karmaşık faktörler arası bağlantıları dikkate alabilen bir tahmin yöntemi olarak iyi bir konuma sahiptir. Bu makine öğrenimi modelleri, özel olarak eğitilmeksizin verileri genelleştirme, tahmin etme ve onlardan öğrenme becerisine sahiptir. Regresyon teorisi, istatistiksel modelleme ve makine öğreniminde kilit bir konudur. Bu çalışmanın temel amacı, 24 Ocak 2020deki Sivrice-Elazığ depreminin ardından yıkılan bir binadan elde edilen geri dönüştürülmüş beton agregalarından üretilen betonların erken yaş basınç dayanımını tahmin etmek için birkaç popüler makine öğrenimi regresyon modelinin performansını karşılaştırmaktır. Deprem sonrasında yeni inşa edilen binlerce yapının olması, bu yapıların hızlı bir şekilde imal edilmesi ve projenin en kısa sürede tamamlanması gibi faktörler nedeniyle erken yaş basınç dayanımı daha da büyük önem taşımaktadır. Erken yaş beton dayanımının yüksek doğrulukla ve kullanışlı bir şekilde belirlenmesi bu nedenle çok önemlidir. Bu çalışmada tüm bu hedeflere ulaşmak için yedi farklı klasik makine öğrenimi algoritması kullanılmıştır. Erken yaş basınç dayanımı değerleri 1 ve 3 gün için dikkate alınmıştır. Deneysel sonuçlar ile kullanılan algoritmaların öngördüğü sonuçlar arasındaki ilişki incelenmiş ve bu akıllı regresyon algoritmalarının kapsamlı bir karşılaştırması yapılmıştır. Sürdürülebilir kalkınma ve döngüsel ekonomi hedefleri kapsamında bu atık malzemelerin değerlendirilmesi ve üretilen betonların erken yaş basınç dayanımlarının yüksek doğrulukla belirlenebilmesi açısından makalenin literatüre önemli katkılar sağlayacağı düşünülmektedir.

Makale detayları

Comparing Machine Learning Regression Models for Early-Age Compressive Strength Prediction of Recycled Aggregate Concrete

Yazarlar:

Muhammed Ulucan | Güngör Yıldırım | Bilal Alatas | Kürşat Esat Alyamaç

Yayıncı:

Fırat University

Ana Başlıklar:

Yapay Zeka (Diğer) | Yapı Malzemeleri

Dergi:

Fırat Üniversitesi Mühendislik Bilimleri Dergisi Vol.36 Issue 2, pp.563-580

Belge Tipi:

Makale

Yayın Tarihi:

9/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001317060

pISSN:

1308-9072

Compressive strength

Construction and demolition waste

Machine learning

Recycled aggregate concreteBasınç dayanımı

İnşaat ve yıkıntı atıkları

Makine öğrenme

Geri dönüşüm agregalı beton

Sayı

Makale detayları

Yazarlar: Muhammed Ulucan | Güngör Yıldırım | Bilal Alatas | Kürşat Esat Alyamaç

Yayıncı: Fırat University

Ana Başlıklar: Yapay Zeka (Diğer) | Yapı Malzemeleri

Dergi: Fırat Üniversitesi Mühendislik Bilimleri Dergisi Vol.36 Issue 2, pp.563-580

Belge Tipi: Makale

Yayın Tarihi: 9/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001317060

pISSN: 1308-9072

Sayı

Yazarlar:

Muhammed Ulucan | Güngör Yıldırım | Bilal Alatas | Kürşat Esat Alyamaç

Yayıncı:

Fırat University

Ana Başlıklar:

Yapay Zeka (Diğer) | Yapı Malzemeleri

Dergi:

Fırat Üniversitesi Mühendislik Bilimleri Dergisi Vol.36 Issue 2, pp.563-580

Belge Tipi:

Makale

Yayın Tarihi:

9/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001317060

pISSN:

1308-9072