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All Journal Jurnal Rekayasa Infrastruktur
Adji Putra Abriantoro
Universitas 17 Agustus 1945 Jakarta
Civil Engineering, Building, Construction & Architecture Engineering Transportation

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Eksperimental Substitusi Serbuk Kaca Pada Binder Untuk Meningkatkan Kuat Tekan Beton Self-Compacting Concrete (SCC) Pada Umur 1 Hari Adji Putra Abriantoro; Sri Endah Susilowati
Jurnal Rekayasa Infrastruktur Vol. 9 No. 1 (2023): Jurnal Rekayasa Infrastruktur
Publisher : Program Studi Teknik Sipil Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/jri.v9i1.218

Abstract

Beton SCC adalah jenis beton inovatif yang dapat memadat secara mandiri. SCC memiliki manfaat lingkungan, sosial, dan ekonomi yang lebih baik dibandingkan beton konvensional. Penggunaan limbah kaca sebagai pengganti semen dalam SCC telah menunjukkan aktivitas pozzolanik dan meningkatkan sifat beton. Beberapa penelitian menguji penggantian sebagian semen dengan serbuk kaca, tetapi penelitian ini bertujuan untuk menentukan persentase optimal berdasarkan hasil eksperimen.Penelitian ini menggunakan 12 sampel dengan tiga sampel untuk setiap variasi. Serbuk kaca dengan variasi 0%, 5%, 10%, dan 15% digunakan. Variabel yang dikendalikan adalah penggunaan serbuk kaca sebagai subtitusi binder, sedangkan variabel yang diukur adalah kuat tekan beton pada usia 1 hari dan flowability. Dalam penelitian ini, variasi penggantian serbuk kaca sebesar 0%, 5%, 10%, dan 15% dari berat binder digunakan. Hasilnya menunjukkan bahwa penggantian 10% menghasilkan kuat tekan beton SCC yang lebih tinggi pada umur 1 hari. Namun, penggantian 15% mengakibatkan penurunan kuat tekan. Evaluasi lebih lanjut diperlukan untuk memahami kinerja jangka panjang beton SCC dengan penggantian serbuk kaca. Penggunaan serbuk kaca sebagai substitusi juga meningkatkan flowability beton SCC secara proporsional dengan persentase penggantian. Meskipun demikian, aspek lain seperti sifat mekanik dan keberlanjutan juga harus dipertimbangkan.  
Optimasi Mix Design Beton Melalui Teknologi Machine Learning Adji Putra Abriantoro; J Rajes Khana
Jurnal Rekayasa Infrastruktur Vol. 9 No. 2 (2023): Jurnal Rekayasa Infrastruktur
Publisher : Program Studi Teknik Sipil Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/jri.v9i2.228

Abstract

The concrete mix design is a crucial step in ensuring the quality of concrete used in construction projects. Traditional mix design methods rely on trial and error, which can be time-consuming and escalate construction costs. In recent years, machine learning technology has been developed to predict concrete properties and optimize mix designs for high-quality concrete. This study aims to explore the application of machine learning in high-quality concrete mix design, considering theoretical conditions, methods, and related research. The focus of this research is to test the use of machine learning techniques in predicting concrete properties and optimizing mix designs for high-quality concrete. The theory and concepts of machine learning will be applied to concrete mix design datasets and relevant properties. The methods employed will include data pre-processing, feature selection, and model training and evaluation. The performance of the machine learning model will be compared with traditional mix design methods to determine its effectiveness. Furthermore, the results and benefits of this study will demonstrate the potential advantages of using machine learning in determining high-performance concrete mix designs. By accurately predicting concrete properties and optimizing mix designs, construction projects can be completed more efficiently and with higher quality. This technology also has the potential to reduce costs associated with trial and error methods and minimize the environmental impact of concrete production. The success of this study will pave the way for further research and development in the field of machine learning for high-performance concrete mix designs.
Co-Authors J Rajes Khana Sri Endah Susilowati