PEMODELAN PREDIKSI KUAT TEKAN BETON UMUR MUDA MENGGUNAKAN H2O'S DEEP LEARNING

Stefanus Santosa, Suroso Suroso, Marchus Budi Utomo, Martono Martono, Mawardi Mawardi

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.

Keywords


machine learning, H2O’s deep learning, prediction, early age concrete compressive strength

Full Text:

PDF

References


Abolpour, B, Benafsheh Abolpour,

Roozbeh Abolpour, Hossein

Bakhshi, 2013, Estimation Of

Concrete Compressive Strength

By A Fuzzy Logic Model,

Science+Business Media B.V,

Springer. 2013.

Au, W.H., Chan, K.C.C. Yao, X.Y.,

, A Novel Evolutionary

Data Mining Algorithm With

Applications To Churn

Prediction. Ieee Transactions On

Evolutionary Computation, Vol.

, No. 6, December 2003.

Cattral, R., Oppacher, F., Deugo, D.,

, Evolutionary Data Mining

With Automatic Rule

Generalization.

Https://Pdfs.Semanticscholar.Or

g/C068/Ea7807367573f4b5f98c0

fca665e9e F74.Pdf. Diakses

Tanggal 21 Februari 2018.

Chine, Wh. Li Chen. Hsun-Hsin Hsu.

Tai-Seng Wang, 2010,

Modeling Slump Of Concrete

Using The Artificial Neural

Networks, International

Conference On Artificial

Intelligence And Computational

Intelligence, 2010, P.236-239,

Ieee Computer Society.

Chopra, P., Rajendra Kumar Sharma,

Maneek Kumar, 2016, Prediction

Of Compressive Strength Of

Concrete Using Artificial Neural

Network And Genetic

Programming, Advance Material

Scienceanda Engineering Vol

Article Id 7648467,

Hindawi Pub Corporate.

Freitas, A.A., 2003, A Survey Of

Evolutionary Algorithms For

Data Mining And Knowledge

Discovery.

Http://Neuro.Bstu.By/Our/Data-

Mining/Fereitas-Ga.Pdf.

Gupta, P., & Kulkarni, N., 2013, An

Introduction Soft Computing

Over Hard Computing.

International Journal Of Latest

Trends In Engineering And

Technology (Ijltet), Vol. 3 Issue 1

September 2013.

Hertzmann, A., & Fleet, D, 2012, Univ

Toronto-Machine Learning And

Data Mining, Computer Science

Department, University Of

Toronto, Version: February 6,

Hui, C. Zhenyu, L., 2013, Research On

The Experiment Of Self Leveling

Concrete With Fly Ash.2013

Fifth Conference On Measuring

Technology And Mechatronics

Automation.

Husken, G., H.J.H. Brouwers, 2012,

On The Early-Age Behavior Of

Zero-Slump Concrete, Cement

And Concrete Research, Vol 42

(2012) 501-510, Elsevier.

Jain, A., Jha, S.K., & Misra, S., 2008,

Modeling And Analysis Of

Concrete Slump Using Artificial

Neural Networks, P 628-633.

Journal Of Materials In Civil

Engineering © Asce / September 2008

Kurt, M., Kotan, T., Gül, M.S., Gül,

R., Aydin, A.C., 2016, The

Effect Of Blast Furnace Slag On

The Self-Compactability Of

Pumice Aggregate Lightweight

Concrete. Sadhana,¯ Vol. 41,

No. 2, February 2016, Pp. 253–

Maimon, O., & Rokach, L., Ed., 2008,

Soft Computing For Knowledge

Discovery And Data Mining.

Library Of Congress Control

Number: 2007934794, Isbn 978-

-387-69934-9 E- Isbn 978-0-

-69935-6, © 2008 Springer

Science+Business Media, Llc.

Mukhopadhyay, A., &

Bandyopadhyay, S., 2014,

Survey Of Multiobjective

Evolutionary Algorithms For

Data Mining Part I. Ieee

Transactions On Evolutionary

Computation, Vol. 18, No. 1,

February 2014.

Najimi, M., J. Sobhani, A.R.

Pourkhorshidi, 2012, A

Comprehensive Study On

Noslump Concrete: From

Laboratory Towards

Manufactory, Construction And

Building Material Vol 30 (2012)

-536, Elsevier.

Nikoo. Mehdi. Farshid Torabian

Moghadam. And Aukasz

Sadowski, 2015, Prediction Of

Concrete Compressive Strength

By Evolutionary Artificial

Neural Networks. Advances In

Materials Science And

Engineering Volume (2015.

Article Id 8491(26. 8 Pages.

Hindawi Publishing Corporation.

Nobile, L., 2014, Prediction Of

Concrete Compressive Strength

By Combined Non-Destructive

Methods. Meccanica, (2015)

:411–417, Doi

1007/S11012-014-9881-5

Ray, N., Dedi, P., Rizky, D., 2016,

Studi Angka Koefisien Korelasi

Kuat Tekan Beton Mutu Tinggi

Berdasarkan Umur & Bentuk

Benda Uji Standar Sni 03-2847-

Agregat Issn: 2541 – 0318,

Vol.1 , No.1, November 2016.

Setyawan, Sigit, 2017, Software

Perancangan Campuran (Mix

Design) Beton Dengan Bahasa

Pemograman Python Berbasis

Gui (Graphical User Interface),

Jurusan Teknik Sipil Fakultas

Teknik, Universitas

Muhammadiyah Surakarta.

Simem, 2018, Tower Beton – Vertical

Batching And Mixing Plants.

Https://Www.Euromarket.Bg/Bg

/Products-Download-

Pdf?File...Pdf. Diakses Tanggal

Maret 2018.

SNI 2847-2013, Persyaratan Beton

Struktural Untuk Bangunan

Gedung, Badan Standarisasi

Nasional.

Takisawa, 2014, Computational

Engineering Analysis With The

New-Generation Space–Time

Methods. Computational

Mechanics, August 2014,

Volume 54, Issue 2, Pp 193–211

Tangchirapat, W. Chaiyanunt

Rattanashotinunt, Rak

Buranasing, Chai Jaturapitakkul,

, Influence Of Fly Ash On Slump Loss And Strength Of

Concrete Fully Incorporating

Recycled Concrete Aggregates,

Journal Of Materials In Civil

Engineering February 2013,

Asce.

Tosye Teknik, 2018, Proposal Otomasi

Batching Plan (Ready Mix &

Precast Concrete).

University Of Texas, 2018,

Computational Engineering

Solving 21st Century

Engineering Problems

Wendner, R., et. al., 2015,

Characterization Of Concrete

Failure Behavior, A

Comprehensive Experimental

Database For The Calibration

And Validation Of Concrete

Models, Materials And

Structures, (2015) 48:3603-3626.

Rilem.

Yeh. Ic., 2007, Modeling Slump Flow

Of Concrete Using Second-Order

Regressions And Artificial

Neural Networks, Cement &

Concrete Composites 29, 2007,

P.474–480. Elsevier.

Yeh. I.C. Che-Hui Lien. Chien-Hua

Peng. Li-Chuan Lien, 2010,

Modeling Concrete Strength

Using Genetic Operation Trees.

Proceedings Of The Ninth

International Conference On

Machine Learning And

Cybernetics. Qingdao. 11-14

July (2010). IEEE




DOI: http://dx.doi.org/10.32497/wahanats.v25i1.1917

Refbacks

  • There are currently no refbacks.


Creative Commons License

ISSN : 0853-8727
e-ISSN : 2527-4333

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Web Analytics View My Stats