Machine learning based prediction of compressive and flexural strength of recycled plastic waste aggregate concrete

Yılmaz Y., Nayır S.

Structures, cilt.69, 2024 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 69
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1016/j.istruc.2024.107363
  • Dergi Adı: Structures
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Anahtar Kelimeler: Compressive-flexural strength, Cross-validation, Grid Serach method, Machine learning, Recycled plastic waste aggregate concrete, SHAP analysis
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

In the last 50 years, the use of plastics has increased significantly due to advances in technology, population growth and increasing needs. However, this trend has led to the accumulation of large amounts of plastic waste. Numerous studies have been conducted on the use of recycled plastic materials in concrete production as a means of recycling and environmental protection. This study aims to predict the 28-day cylinder compressive and flexural strength of concretes using Polyethylene Terephthalate (PET), Phenolic Formaldehyde (PF), Polypropylene (PP), Polycarbonate (PC), Polyvinyl Chloride (PCV), High-Impact Polystyrene (HIPS) and High-Density Polyethylene (HDPE) plastic waste as aggregates. In this scope, a comprehensive database was created using experimental results obtained from the literature. Then, this study used six ML models, including Support Vector Regressor (SVR), Random Forest Regressor (RFR), Gradient Boosting Regressor (GBR), XGBoost Regressor (XGR), LightGBM Regressor (LGR), Bagged Decision Trees Regressor (BDTR), to predict the compressive and flexural strength of recycled plastic waste aggregate concrete (RPWAC). From the experimental database, 253 data points with strength between 6.00–70.05 MPa were identified to estimate the compressive strength (CS) and 175 data points with strength between 1.21–7.96 MPa were identified for flexural strength (FS). Cross-validation was used for six ML models and hyperparameter fine-tuning was performed. The results showed that six different ML models predicted the compressive and flexural strengths of RPWAC with high accuracy. The XGR model predicted CS and FS better than the other five different models with R2 values of 0.931 and 0.999, respectively. The three main properties effecting the CS of RPWAC were plastic waste content, water/cement ratio and concrete specific gravity, while the three main properties affecting the FS of RPWAC were cement specific gravity, plastic waste content and cement CS.