An assessment of machine learning models for slump flow and examining redundant features

Unlu R.

COMPUTERS AND CONCRETE, cilt.25, sa.6, ss.565-574, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 25 Sayı: 6
  • Basım Tarihi: 2020
  • Doi Numarası: 10.12989/cac.2020.25.6.565
  • Dergi Adı: COMPUTERS AND CONCRETE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Communication Abstracts, Compendex, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.565-574
  • Anahtar Kelimeler: multilayer perceptron regression, regression trees, support vector regression, redundant features, M5P trees, SELF-COMPACTING CONCRETE, MIX DESIGN METHOD, COMPRESSIVE STRENGTH, MINERAL ADDITIVES, NEURAL-NETWORKS, PERFORMANCE, PREDICTION, ADMIXTURES
  • Abdullah Gül Üniversitesi Adresli: Hayır

Özet

Over the years, several machine learning approaches have been proposed and utilized to create a prediction model for the high-performance concrete (HPC) slump flow. Despite HPC is a highly complex material, predicting its pattern is a rather ambitious process. Hence, choosing and applying the correct method remain a crucial task. Like some other problems, prediction of HPC slump flow suffers from abnormal attributes which might both have an influence on prediction accuracy and increases variance. In recent years, different studies are proposed to optimize the prediction accuracy for HPC slump flow. However, more state-of-the-art regression algorithms can be implemented to create a better model. This study focuses on several methods with different mathematical backgrounds to get the best possible results. Four well-known algorithms Support Vector Regression, M5P Trees, Random Forest, and MLPReg are implemented with optimum parameters as base learners. Also, redundant features are examined to better understand both how ingredients influence on prediction models and whether possible to achieve acceptable results with a few components. Based on the findings, the MLPReg algorithm with optimum parameters gives better results than others in terms of commonly used statistical error evaluation metrics. Besides, chosen algorithms can give rather accurate results using just a few attributes of a slump flow dataset.