Machine Learning Prediction of Residual Mechanical Strength of Hybrid-Fiber- Reinforced Self-consolidating Concrete Exposed to Elevated Temperature

Türk K., Kına C., Tanyıldızı H., Nehdi M. L.

FIRE TECHNOLOGY, cilt.59, sa.6, ss.1-47, 2023 (SCI-Expanded)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 59 Sayı: 6
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1007/s10694-023-01457-w
  • Dergi Adı: FIRE TECHNOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, ABI/INFORM, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, Environment Index, ICONDA Bibliographic, INSPEC, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.1-47
  • İnönü Üniversitesi Adresli: Evet

Özet

Establishing the engineering properties of cement-based composites at elevated

temperature requires costly, laborious, and time-consuming experimental work.

Data-driven models can provide a robust and efficient alternative. In this study,

extreme learning machine (ELM), support vector machine (SVM), artificial neural network

(ANN), and decision tree (DT) models were trained to predict the residual compressive,

splitting tensile, and flexural strengths of hybrid fiber-reinforced selfcompacting

concrete (HFR-SCC) exposed to high temperatures. Mixtures including

macro and micro steel fibers, polyvinyl alcohol (PVA), and polypropylene (PP) were

subjected to different temperature levels, leading to an experimental database of 360

specimens. Eleven input parameters including cement, fly ash, water, sand, gravel, fiber

type, water reducer, and temperature were deployed. The residual mechanical strengths

were targeted as output parameters. ANOVA was used to explore the influence of input

parameters. Temperature was found to be the most influential parameter. Dataset consisting

of 114 instances was retrieved from pertinent literature and used along with the

authors’ experimentally generated dataset for residual strength prediction. The experimental

results were compared with predictions of ELM, SVM, ANN, and DT. ELM

achieved superior performance and can offer a robust tool for predicting the residual

mechanical strengths of HFR-SCC upon exposure to high temperature.