Abstract
Artificial Neural Network (ANN) is a Machine Learning (ML) algorithm which learn by itself and organize its thinking to solve problems. Although the learning process involves many hidden layers (Deep Learning) this algorithm still has weaknesses when faced with high noise data. Concrete mixture design data has a high enough noise caused by many unidentified / measurable aspects such as planning, design, manufacture of test specimens, maintenance, testing, diversity of physical and chemical properties, mixed formulas, mixed design errors, environmental conditions, and testing process. Information needs about the compressive strength of early age concrete (under 28 days) are often needed while the construction process is still ongoing. ANN has been tried to predict the compressive strength of concrete, but the results are less than optimal. This study aims to improve the ANN prediction model using an H2O’s Deep Learning based on a multi-layer feedforward artificial neural network that is trained with stochastic gradient descent using backpropagation. The H2O’s Deep Learning best model is achieved by 2 hidden layers- 50 hidden neurons and ReLU activation function with a RMSE value of 6,801. This Machine Learning model can be used as an alternative/ substitute for conventional mix designs, which are environmentally friendly, economical, and accurate. Future work with regard to the concrete industry, this model can be applied to create an intelligent Batching and Mixing Plants.
Highlights
Soft ComputingOver Hard Computing.International Journal Of LatestTrends In Engineering AndTechnology (Ijltet), Vol 3 Issue 1Hertzmann, A., & Fleet, D, 2012, UnivToronto-Machine Learning AndData Mining, Computer ScienceDepartment, University OfToronto, Version: February 6, Hui, C
This study aims to improve the Artificial Neural Network (ANN) prediction model using an H2O’s Deep Learning based on a multi-layer feedforward artificial neural network that is trained with stochastic gradient descent using backpropagation
Proceedings Of The Ninth International Conference On Machine Learning And Cybernetics
Summary
Penelitian ini merupakan penelitian awal untuk menyusun model prediksi dengan hanya mengandalkan Neural Networks tanpa perlakuan evolusif. 2. Model Prediksi Slump Beton dengan Artificial Neural Networks (2016). Penelitian ini hampir sama dengan penelitian sebelumnya, namun model yang sudah diperoleh diterapkan pada kasus yang berbeda, yakni Slump Beton. 3. Model Prediksi Slump Beton dengan Artificial Neural Networks dan Optimasi Genetic Algorithm (2016). Penelitian ini mencoba pengaruh perlakuan evolusif Genetic Algorithm untuk memprediksi Slump. Model komputasi yang telah diperoleh dari penelitian sebelumnya kemudian diaplikasikan untuk membuat model prediksi kuat tekan beton pada umur 28 hari. Penelitian yang diusulkan ini merupakan tindak lanjut dari penelitian yang keempat, yakni dengan membuat dan menguji coba model prediksi beton pada umur 3, 7, 14, dan 28 hari dengan pendekatan Artificial Neural Networks tipe H2O's Deep Learning. Berdasarkan rumusan masalah yang sudah diuraikan di Pendahuluan, maka untuk memperoleh solusi atas masalah tersebut agar tujuan tercapai disusunlah strategi dan tahapan penelitian seperti berikut ini
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