Predictive Modeling of Compression Strength of Waste PET/SCM Blended Cementitious Grout Using Gene Expression Programming

• Main Authors: Khan, K., Jalal, F.E., Iqbal, M., Khan, M.I., Amin, M.N., Al-Faiad, M.A.
• Format: Article
• Published: MDPI 2022
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129098772&doi=10.3390%2fma15093077&partnerID=40&md5=3cce8eddd5a457ece76a4e9baae1a05e; http://eprints.utp.edu.my/33113/
• Institution: Universiti Teknologi Petronas

The central aim of this study is to evaluate the effect of polyethylene terephthalate (PET) alongside two supplementary cementitious materials (SCMs)�i.e., fly ash (FA) and silica fume (SF)�on the 28-day compressive strength (CS28d ) of cementitious grouts by using. For the gene expression programming (GEP) approach, a total of 156 samples were prepared in the laboratory using variable percentages of PET and SCM (0�10, each). To achieve the best hyper parameter setting of the optimized GEP model, 10 trials were undertaken by varying the genetic parameters while observing the models� performance in terms of statistical indices, i.e., correlation coefficient (R), root mean squared error (RMSE), mean absolute error (MAE), comparison of regression slopes, and predicted to experimental ratios (�). Sensitivity analysis and parametric study were performed on the best GEP model (obtained at; chromosomes = 50, head size = 9, and genes = 3) to evaluate the effect of contributing input parameters. The sensitivity analysis showed that: CS7d (30.47) > CS1d (28.89) > SCM (18.88) > Flow (18.53) > PET (3.23). The finally selected GEP model exhibited optimal statistical indices (R = 0.977 and 0.975, RMSE = 2.423 and 2.531, MAE = 1.918 and 2.055) for training and validation datasets, respectively. The role of PET/SCM has no negative influence on the CS28d of cementitious grouts, which renders the PET a suitable alternative toward achieving sustainable and green concrete. Hence, the simple mathematical expression of GEP is efficacious, which leads to saving time and reducing labor costs of testing in civil engineering projects. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.