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ITS » PhD Theses » Program Doktoral Teknik Sipil
Posted by tondoindra@gmail.com at 10/01/2014 13:26:37  •  1788 Views


Applying dan Modelling Artificial Neural Networks ANNs in Self Compacting Concrete SCC Mixes Refer to Setting Time and the Concrete Compressive Strength at age of 28 days.

Author :
SURYADI, AKHMAD  ( 3107301006 )




ABSTRAK

Teknologi campuran beton konvensional yang cenderung kaku ini sering kali menimbulkan permasalahan antara lain penggunaan vibrator tidak sempurna pada konstruksi yang terlalu tinggidalam dan tipis seperti pada pekerjaan bawah tanah pekerjaan terowongan dinding penahan dll. Teknologi beton Self-compacting concrete SCC dapat mengatasi permasalahan tersebut karena SCC dapat mengalir akibat berat sendiri dapat masuk ke dalam setiap sudut bekesting tanpa vibrator tidak memerlukan banyak tenaga manusia dan menghasilkan permukaan yang halus. Disamping beberapa kelebihan ada salah satu kekurangan yang dimiliki oleh campuran beton SCC yaitu belum adanya mix design campuran SCC. Penelitian bertujuan membangun dan menyediakan program mix design campuran beton SCC terhadap setting time dan kuat tekan beton pada umur 28 hari dengan pemodelan Artificial Neural Networks ANNs. Tahapan penelitian meliputi pengujian sifat fisik dan kimia semua material dasar beton mendesain campuran beton SCC dengan coba-coba hingga diperoleh campuran yang ideal. Mengevaluasi dan menganalisa hubungan input dan target 576 set data dengan pemodelan ANNs. Memvalidasi hasil pemodelan ANNs dengan program yang umum beredar di pasaran. Membangun dan menyusun program mix design campuran beton SCC dan memverifikasi hasil running program dengan pengujian eksperimental di laboratorium. Artificial Neural Networks ANNs metodealat yang potensial untuk memprediksi perilaku suatu set data berdasarkan hasil ekspreimental. Hasil validasi menunjukkan terjadi korelasi yang baik antara program ANNs yang dibangun oleh peneliti program komersial matematik-1 dan program ANNs yang umum beredar di pasaran program matematik komersial-2 terbukti dengan tingkat kebenaran mencapai 98.805 persen. Verifikasi mix design hasil running program terhadap hasil pengujian eksperimen di laboratorium menunjukkan korelasi yang baik dengan nilai kesalahan rata-rata 4.178 persen dan menunjukkan tingkat kebenaran 98.641 persen pada fase validasi dan 98.543 pada fase testing. Berarti secara bersama-sama variabel independen target berkorelasi kuat terhadap variabel dependen output.


ABSTRACT

Conventional concrete mixture technology that tends to be sticky potentially often gives some problems such as the constructions that difficult to compact by normal vibration and high demands on aesthetics for examples in underground installation structures rock tunnel entrances retention walls etc. Self-compacting concrete SCC technology should not only flow under its own weight but should also completely fill the entire form without vibration less labor involved and better surface finishes. In determining the mix compositiondesign of SCC necessary should be determined by practical trials and errors. The objective is to construct and build the concrete mix design of SCC using Artificial Neural Networks ANNs model refer to setting time and concrete compressive strength in 28 days. After finishing all basic materials tests the next step is to prepare and design the concrete mixes by trial and error until the ideal concrete composition founded. A total of 576 used to evaluate and analysis the relationship between input and target data sets using Artificial Neural Networks ANNs. There are six neurons as input data sets such as the number of cement coarse and fine agregate fly ash chemical admixture and cement water ratio and there are two neurons as target data sets such as the value of setting time and concrete compressive strength. To optimized the results the researcher using two different comersial mathematic program. Validating the result of running program to the experimantal tests is the final step. The results of the present investigation indicate that ANNs have strong potential as a powerful tool for evaluating setting time and the compressive strength of SCC at 28 day. The validation shows that there is correlation between program that built by reseacrh commercial mathematic program-1 and program that used widely commercial mathematic program-2 with the error 4.178 percent and the degree of correctness 98.641 percent in validation phase and 98.543 percent in testing phase.



Keywordsmix design, Self-compacting concrete (SCC), Artificial neural networks (ANNs), setting time dan kuat tekan beton
 
Subject:  Jaringan komputer
Contributor
  1. Prof. DR. Ir. Triwulan, DEA
  2. DR. Techn. Pujo Aji, ST., MT
Date Create: 11/07/2013
Type: Text
Format: PDF
Language: Indonesian
Identifier: ITS-PhD-31004140000074
Collection ID: 31004140000074
Call Number: RDS 006.32 Sur a


Source
PhD of Civil Engineering RDS 006.32 Sur a 2013

Coverage
ITS Community

Rights
Copyright @2013 by ITS Library. This publication is protected by copyright and per obtained from the ITS Library prior to any prohibited reproduction, storage in a re transmission in any form or by any means, electronic, mechanical, photocopying, reco For information regarding permission(s), write to ITS Library




[ Download - Open Access ]

  1.  ITS-PhD-28975-3107301006-Abstract_id.pdf - 185 KB
  2.  ITS-PhD-28975-3107301006-Abstract_en.pdf - 184 KB
  3.  ITS-PhD-28975-3107301006-Conclusion.pdf - 219 KB




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