Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks

2011 ◽  
Vol 42 (10) ◽  
pp. 780-786 ◽  
Author(s):  
Rafat Siddique ◽  
Paratibha Aggarwal ◽  
Yogesh Aggarwal
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Bottom Ash ◽  
Self Compacting Concrete ◽  
Artificial Neural

An Artificial Neural Networks Model for Compressive Strength of Self-Compacting Concrete

2016 ◽  
Vol 845 ◽  
pp. 226-230
Author(s):  
Akhmad Suryadi ◽  
Qomariah ◽  
M. Sarosa
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Experimental Tests ◽  
Experimental Program ◽  
Self Compacting Concrete ◽  
Design Program ◽  
Artificial Neural ◽  
Program Sample ◽  
Mathematic Program

An experimental program was undertaken to evaluate the compressive strength of self-compacting concrete using commercial mathematic program. Sample variation was monitored using an experimental cylinder of concrete measuring 150 mm in diameter and 300 mm in height. This research examined various mixture designs in the laboratory tests with the goal of creating mixtures with desirable flow specification that did not require additional vibration yet provided adequate compressive strength. After 28 days, compressive strength of cylinder concrete determination, a model of Artificial Neural Networks (ANNs) was designed for this research and the results were obtained in this model of ANN. Both experimental tests and mix design program data was analyzed with statistical packet software. The result of statistical analysis has been done in 98.54 percent of confidence interval. It has been seen that the ANN can be used as reliable modelling method for similar experiment.

scholarly journals Prediction of Compressive Strength of Self-Compacting Concrete (SCC) with Silica Fume Using Neural Networks Models

2021 ◽  
Vol 7 (1) ◽  
pp. 118-139
Author(s):  
Mahmoud Serraye ◽  
Said Kenai ◽  
Bakhta Boukhatem
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Silica Fume ◽  
Carbon Dioxide Emissions ◽  
Experimental Studies ◽  
Back Propagation Algorithm ◽  
Self Compacting Concrete ◽  
Cement Replacement ◽  
Artificial Neural

Self-Compacting Concrete (SCC) is a relatively new type of concrete with high workability, high volume of paste and containing cement replacement materials such as slag, natural pozzolana and silica fume. Cement replacement materials provide a wide variety of benefits such as lower cost, reduced consumption of natural resources, reduced carbon dioxide emissions and improved fresh and hardened properties. SCC is used in many applications such as sections with congested reinforcement and high rise shear walls and there is a need for the prediction of the performance of SCC used. Artificial Neural networks (ANN) are widely used in civil engineering for the prediction of the performance of some engineering materials such as compressive strength and durability. However, currently, studies on SCC containing silica fume are very rare. In this paper, an artificial neural networks (ANN) model is developed to predict the compressive strength of SCC with silica fume using the Levenberg-Marquardt back propagation algorithm based on a database from 366 experimental studies. The model developed was correlated with a nonlinear relationship between the constituents (input) and the compressive strength of SCC (output). To evaluate the predictive ability and generalize the developed model, other researchers’ experimental results were compared with the model prediction and good agreements are found. A parametric study was conducted to study the sensitivity of the ANN proposed model to some parameters such as water/binder ratio and superplasticizer content. The model developed in this study can potentially be used for SCC compressive strength prediction with very acceptable results and a high correlation coefficient R2=0.93. The developed model is practical, easy to use and user friendly. Doi: 10.28991/cej-2021-03091642 Full Text: PDF

scholarly journals Use of Nondestructive Testing of Ultrasound and Artificial Neural Networks to Estimate Compressive Strength of Concrete

Buildings ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 44
Author(s):  
Fernando A. N. Silva ◽  
João M. P. Q. Delgado ◽  
Rosely S. Cavalcanti ◽  
António C. Azevedo ◽  
Ana S. Guimarães ◽  
...  
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Cross Sections ◽  
Influential Factors ◽  
Average Error ◽  
Ann Model ◽  
Cement Ratio ◽  
Water Cement Ratio ◽  
Artificial Neural

The work presents the results of an experimental campaign carried out on concrete elements in order to investigate the potential of using artificial neural networks (ANNs) to estimate the compressive strength based on relevant parameters, such as the water–cement ratio, aggregate–cement ratio, age of testing, and percentage cement/metakaolin ratios (5% and 10%). We prepared 162 cylindrical concrete specimens with dimensions of 10 cm in diameter and 20 cm in height and 27 prismatic specimens with cross sections measuring 25 and 50 cm in length, with 9 different concrete mixture proportions. A longitudinal transducer with a frequency of 54 kHz was used to measure the ultrasonic velocities. An ANN model was developed, different ANN configurations were tested and compared to identify the best ANN model. Using this model, it was possible to assess the contribution of each input variable to the compressive strength of the tested concretes. The results indicate an excellent performance of the ANN model developed to predict compressive strength from the input parameters studied, with an average error less than 5%. Together, the water–cement ratio and the percentage of metakaolin were shown to be the most influential factors for the compressive strength value predicted by the developed ANN model.

scholarly journals Retraction Note to: Prediction compressive strength of Portland cement-based geopolymers by artificial neural networks

2020 ◽  
Vol 32 (19) ◽  
pp. 15669-15669
Author(s):  
Ali Nazari ◽  
Hadi Hajiallahyari ◽  
Ali Rahimi ◽  
Hamid Khanmohammadi ◽  
Mohammad Amini
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Portland Cement ◽  
Artificial Neural

scholarly journals Designing the Composition of Cement Stabilized Rammed Earth Using Artificial Neural Networks

Materials ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1396 ◽  
Author(s):  
Hubert Anysz ◽  
Piotr Narloch
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Rammed Earth ◽  
Particle Sizes ◽  
Construction Site ◽  
Construction Technique ◽  
Soil Cement ◽  
Energy Consuming ◽  
Artificial Neural

Cement stabilized rammed earth (CRSE) is a sustainable, low energy consuming construction technique which utilizes inorganic soil, usually taken directly from the construction site, with a small addition of Portland cement as a building material. This technology is gaining popularity in various regions of the world, however, there are no uniform standards for designing the composition of the CSRE mixture. The main goal of this article is to propose a complete algorithm for designing CSRE with the use of subsoil obtained from the construction site. The article’s authors propose the use of artificial neural networks (ANN) to determine the proper proportions of soil, cement, and water in a CSRE mixture that provides sufficient compressive strength. The secondary purpose of the paper (supporting the main goal) is to prove that artificial neural networks are suitable for designing CSRE mixtures. For this purpose, compressive strength was tested on several hundred CSRE samples, with different particle sizes, cement content and water additions. The input database was large enough to enable the artificial neural network to produce predictions of high accuracy. The developed algorithm allows us to determine, using relatively simple soil tests, the composition of the mixture ensuring compressive strength at a level that allows the use of this material in construction.

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