scholarly journals PREDICTION OF CONCRETE MIX DESIGN USING DATA MINING TECHNIQUES - A REVIEW

2021 ◽  
Vol 9 (5) ◽  
pp. 324-330
Author(s):  
Rajeeth T.J. ◽  
◽  
Pavan Kumar S.P. ◽  
Swathi B. H. ◽  
◽  
...  
Keyword(s):  
Data Mining ◽  
Mix Design ◽  
Machine Learning Techniques ◽  
Concrete Mix Design ◽  
Mix Proportioning ◽  
Concrete Mix ◽  
Technical Specifications ◽  
Precautionary Measures ◽  
The Right ◽  
Codal Provisions

Concrete mix proportioning is one of the critical process and it involves a lot of precautionary measures to arrive at the right proportions of ingredients like cement, aggregate, water, and admixtures. Even though there are technical specifications that are managed mix proportionating, the procedure is not totally in the realm of science. Due to imprecise codal provisions, impreciseness, and fuzziness involved in the various stages of mix proportioning. This paper reviews the various data mining and machine learning techniques developed by the researchers for making concrete mix design for various codal provisions more realistic and scientific.

scholarly journals Machine Learning Techniques in Concrete Mix Design

Materials ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1256 ◽  
Author(s):  
Patryk Ziolkowski ◽  
Maciej Niedostatkiewicz
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
State Of The Art ◽  
Mix Design ◽  
Machine Learning Techniques ◽  
Concrete Mix Design ◽  
Learning Techniques ◽  
Concrete Mix

Concrete mix design is a complex and multistage process in which we try to find the best composition of ingredients to create good performing concrete. In contemporary literature, as well as in state-of-the-art corporate practice, there are some methods of concrete mix design, from which the most popular are methods derived from The Three Equation Method. One of the most important features of concrete is compressive strength, which determines the concrete class. Predictable compressive strength of concrete is essential for concrete structure utilisation and is the main feature of its safety and durability. Recently, machine learning is gaining significant attention and future predictions for this technology are even more promising. Data mining on large sets of data attracts attention since machine learning algorithms have achieved a level in which they can recognise patterns which are difficult to recognise by human cognitive skills. In our paper, we would like to utilise state-of-the-art achievements in machine learning techniques for concrete mix design. In our research, we prepared an extensive database of concrete recipes with the according destructive laboratory tests, which we used to feed the selected optimal architecture of an artificial neural network. We have translated the architecture of the artificial neural network into a mathematical equation that can be used in practical applications.

Concrete mix design

2017 ◽  
pp. 249-258
Author(s):  
Marios Soutsos ◽  
Peter Domone
Keyword(s):  
Mix Design ◽  
Concrete Mix Design ◽  
Concrete Mix

Concrete mix design

2018 ◽  
pp. 162-168
Author(s):  
Peter Domone ◽  
John Illston
Keyword(s):  
Mix Design ◽  
Concrete Mix Design ◽  
Concrete Mix

scholarly journals Studi Penggunaan Bahan Aditif Dan Variasi Dosis Accelerator Terhadap Perkerasan Beton Yang Dipercepat (Studi Kasus Mutu Beton K-300)

2021 ◽  
Vol 13 (2) ◽  
Author(s):  
Suhaimi Suhaimi ◽  
R. Dedi Iman Kurnia
Keyword(s):  
Mix Design ◽  
Concrete Mix Design ◽  
Chemical Admixture ◽  
Concrete Mix ◽  
Mineral Additive

Penelitian ini bertujuan untuk mengetahui hasil pengujian kuat tekan awal beton kombinasi sikafume (mineral additive) dan variasi dosis penggunaan accelerator (chemical admixture). Penelitian ini menggunakan metode eksperimental. Adapun prosedur penelitian dimulai dengan studi literatur, dilanjutkan dengan persiapan material, pemeriksaan sifat fisis agregat, perencanaan campuran beton (concrete mix design), pembuatan dan perawatan benda uji, serta pengujian benda uji berupa pengujian slump dan pengujian kuat tekan serta analisis data. Berdasarkan hasil dan pembahasan penelitian terkait dengan nilai kuat tekan beton, maupun bentuk atau workability dan waktu perkerasan adalah sebagai berikut: 1) hasil uji kuat tekan beton kombinasi sikafume dengan variasi dosis penggunaan accelerator 1:2 memiliki nilai kuat tekan rata-rata sebesar 26,823 Mpa dan 31,280 Mpa untuk umur pengujian 24 dan 72 jam; 2) hasil uji kuat tekan beton kombinasi sikafume dengan variasi dosis penggunaan accelerator 1:3,5 memiliki nilai kuat tekan rata-rata sebesar 24,913 Mpa dan 30,643 Mpa untuk umur pengujian 24 dan 72 jam; 3) hasil uji kuat tekan beton kombinasi sikafume dengan variasi dosis penggunaan accelerator 1:5 memiliki nilai kuat tekan rata-rata sebesar 23,640 Mpa dan 30,325 Mpa untuk umur pengujian 24 dan 72 jam; 4) nilai slump yang diperoleh pada setiap variasi benda uji lebih besar dari yang direncanakan dalam mix design, yaitu nilai slump yang diperoleh termasuk katagori slump runtuh; dan 5) kombinasi sikafume dan variasi dosis accelerator dapat mempercepat waktu perkerasan dengan nilai kuat tekan rata-rata di atas 24,90 Mpa atau K-300 dalam waktu 24 dan 72 jam

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