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All Journal MEDIA KOMUNIKASI TEKNIK SIPIL Advance Sustainable Science, Engineering and Technology (ASSET)
Akhmad Firdos Khoiril Khitam
National Taiwan University of Science and Technology
Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Mechanical Performance of Recycled Plastic-Based Paving Blocks with Sand and Fly Ash Fillers Mochammad Qomaruddin; Adi Noor Fakhriyan; Yayan Adi Saputro; Yulita Arni Priastiwi; Fatchur Roehman; Nasyiin Faqih; Arif Hidayat; Akhmad Firdos Khoiril Khitam
Advance Sustainable Science Engineering and Technology Vol. 8 No. 2 (2026): February-April
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i2.2664

Abstract

The use of recycled plastic waste as a binder for paving blocks has been reported in a number of previous studies. However, most of these studies focus on feasibility and do not clearly explain how different plastic types behave mechanically over time. In this study, the compressive strength performance of paving blocks made from polypropylene (PP), polyethylene terephthalate (PET), and high-density polyethylene (HDPE) was experimentally compared using sand or fly ash as fillers. All mixtures were prepared with a fixed composition of 60% plastic and 40% aggregate. Compressive strength testing was carried out at 28 and 56 days using three specimens for each mix. The results show that the HDPE–sand mixture achieved the highest compressive strength at 28 days, reaching 15.7 MPa. Nevertheless, a noticeable reduction in strength was observed at 56 days, particularly in the HDPE-based mixtures. This reduction is mainly associated with polymer shrinkage and the development of interfacial stresses between the plastic binder and the aggregates. Overall, the results indicate that plastic-based paving blocks can meet the requirements of SNI 03–0691–1996 for light-duty applications, although their long-term mechanical stability remains a limitation that needs further attention.
Model Berbasis Kecerdasan Buatan untuk Prediksi Kekuatan Beton Geopolimer Riqi Radian Khasani; Ferry Hermawan; Akhmad Firdos Khoiril Khitam
MEDIA KOMUNIKASI TEKNIK SIPIL Volume 31, Nomor 1 (2025)
Publisher : Department of Civil Engineering, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/mkts.v31i1.70716

Abstract

Geopolymer concrete (GPC) has emerged as a sustainable alternative to conventional concrete, offering reduced carbon emissions and enhanced mechanical properties. However, variability in compressive strength due to material composition poses challenges to its broader adoption. Traditional evaluation methods are often time-consuming and resource-intensive, necessitating the development of precise and efficient predictive tools. This study introduces the optimized least squares moment balanced machine with feature selection (OLSMBM-FS), an advanced AI-based model for accurately predicting GPC compressive strength. The model incorporates backpropagation neural networks (BPNN) for weight assignment, least squares support vector machines (LSSVM) for hyperplane optimization, and the optical microscope algorithm (OMA) for hyperparameter tuning. The study employs a systematic dataset, implementing normalization and feature selection techniques to improve the accuracy and efficiency of the model training process. The OLSMBM-FS was validated using 10-fold cross-validation and demonstrated superior performance compared to other machine learning models. It achieved the lowest RMSE (4.279), MAE (2.291), and MAPE (6.59%), alongside the highest R (0.901) and R² (0.813), confirming its robustness and predictive accuracy. These findings highlight the potential of OLSMBM-FS as a reliable tool for predicting GPC compressive strength, supporting its broader application in sustainable construction practices.
Co-Authors Adi Noor Fakhriyan Arif Hidayat Fatchur Roehman Ferry Hermawan Nasyiin Faqih Qomaruddin, Mochammad Riqi Radian Khasani Yayan Adi Saputro Yulita Arni Priastiwi