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Abstract
Abstract
The optimization of concrete compressive strength is crucial for the development of durable and high-performance concrete structures. Thisㅤstudyㅤinvestigatesㅤthe applicationㅤof Artificial NeuralㅤNetworksㅤ(ANN) using Multi-Layer Perceptron (MLP) regression techniques to optimize the mix design of concrete. By leveraging MLP, a type of ANN known for its ability to model complex non-linear relationships, we aim to predict and enhance the compressiveㅤstrength of concrete based on various mix proportions. The study utilizes a dataset comprising different mix designs, including cement, water, aggregates, and admixtures. The MLP model is trained and validated using this dataset, demonstrating its capability to accurately predict concrete compressive strength. Results indicate that the MLP-based ANN model outperforms traditional mix design methods, providing more precise and reliable predictions. This approach not only enhancesㅤtheㅤunderstandingㅤofㅤtheㅤrelationships between mix components and compressive strength but also offers a robust tool for engineers to optimize concrete mixes effectively. The findings highlight the potential of integrating advanced computational techniques in civil engineering to achieve superior material performance..
Keywords: Optimization, Artificial Neural Networks, Multi-Layer Perceptron, Concrete Mix Design, Construction Materials
Abstrak
Optimasi kuat tekan beton sangat penting untuk pengembangan struktur beton yang tahan lama dan berkinerja tinggi. Studi ini meneliti penerapan Jaringan Syaraf Tiruan (JST) menggunakan teknik regresi Multi-Layer Perceptron (MLP) untuk mengoptimalkan desain campuran beton. Dengan memanfaatkan MLP, jenis JST yang dikenal karena kemampuannya untuk memodelkan hubungan non-linear yang kompleks, kami bertujuan untuk memprediksi dan meningkatkan kuat tekan beton berdasarkan berbagai proporsi campuran. Studi ini menggunakan dataset yang terdiri dari berbagai desain campuran, termasuk semen, air, agregat, dan bahan tambahan. Model MLP dilatih dan divalidasi menggunakan dataset ini, menunjukkan kemampuannya untuk memprediksi kuat tekan beton dengan akurat. Hasil penelitian menunjukkan bahwa model JST berbasis MLP mengungguli metode desain campuran tradisional, memberikan prediksi yang lebih tepat dan andal. Pendekatan ini tidak hanya meningkatkan pemahaman tentang hubungan antara komponen campuran dan kuat tekan, tetapi juga menawarkan alat yang kuat bagi para insinyur untuk mengoptimalkan campuran beton secara efektif. Temuan ini menyoroti potensi integrasi teknik komputasi canggih dalam teknik sipil untuk mencapai kinerja material yang unggul
Kata kunci: Optimasi, Jaringan Syaraf Tiruan, Multi-Layer Perceptron, Desain Campuran Beton, Material Konstruksi
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