Abstract
Disposal of plastic waste causes serious environmental problems, including landfills and
water bodies degradation, greenhouse gas emission, soil contamination and so on. This
study investigates the use of recycled plastic aggregate (RPA), as a partial replacement for
conventional coarse aggregates by weight in lightweight concrete production. Concrete
mixtures with different concentrations of RPA at (0, 15, 30, and 45%) were prepared and
cured for 7, 14, 28, and 56 days. Including RPA into concrete reduced both density and
compressive strength as the replacement level increased. Density decreased from 2,347
kg/m³ at 0% RPA to 1,895 kg/m³ at 45% replacement. Similarly, 28 days compressive
strength decreased from 30.43 N/mm² (control) to 19.50 N/mm² at 45% replacement,
reflecting the lower specific gravity and weaker bonding of RPA compared to traditional
coarse aggregate. Additionally, the test results showed that RPA concrete has a low water
absorption rate at 15% replacement, with 2.50% for water absorption and a 0.0235 mm/s1/2
sorptivity value compared to control samples with 2.66% for water absorption and 0.024
mm/s1/2 sorptivity value. However, concrete samples with up to 30% RPA replacement met
the minimum requirements for structural lightweight concrete. This study also used
machine learning models, including artificial neural networks (ANN), k-nearest neighbor (k
NN), and random forest (RF), to predict the durability properties of RPA concrete. Among
these models, the k-NN model showed the best prediction accuracy with an R² value of 1.00,
a mean absolute error (MAE) and a mean square error (MSE) of 0.001 for both the train and
test data. These findings show that the use of treated RPA in concrete not only offers a
sustainable alternative to natural aggregates but also improves the durability of the
resulting structures.
water bodies degradation, greenhouse gas emission, soil contamination and so on. This
study investigates the use of recycled plastic aggregate (RPA), as a partial replacement for
conventional coarse aggregates by weight in lightweight concrete production. Concrete
mixtures with different concentrations of RPA at (0, 15, 30, and 45%) were prepared and
cured for 7, 14, 28, and 56 days. Including RPA into concrete reduced both density and
compressive strength as the replacement level increased. Density decreased from 2,347
kg/m³ at 0% RPA to 1,895 kg/m³ at 45% replacement. Similarly, 28 days compressive
strength decreased from 30.43 N/mm² (control) to 19.50 N/mm² at 45% replacement,
reflecting the lower specific gravity and weaker bonding of RPA compared to traditional
coarse aggregate. Additionally, the test results showed that RPA concrete has a low water
absorption rate at 15% replacement, with 2.50% for water absorption and a 0.0235 mm/s1/2
sorptivity value compared to control samples with 2.66% for water absorption and 0.024
mm/s1/2 sorptivity value. However, concrete samples with up to 30% RPA replacement met
the minimum requirements for structural lightweight concrete. This study also used
machine learning models, including artificial neural networks (ANN), k-nearest neighbor (k
NN), and random forest (RF), to predict the durability properties of RPA concrete. Among
these models, the k-NN model showed the best prediction accuracy with an R² value of 1.00,
a mean absolute error (MAE) and a mean square error (MSE) of 0.001 for both the train and
test data. These findings show that the use of treated RPA in concrete not only offers a
sustainable alternative to natural aggregates but also improves the durability of the
resulting structures.
Keywords
Artificial Neural Network
K-Nearest Neighbor
Random Forest
Recycled plastic aggregate
sorptivity
Water absorption.