scholarly journals Prediction of Mechanical Properties of Local Concrete in Compression Using Artificial Neural Networks

2020 ◽  
Vol 27 (1) ◽  
pp. 105-121
Author(s):  
Baylasan Mohamad ◽  
Soleman Alamoudi ◽  
Abd alrahman Issa
Keyword(s):  
Mechanical Properties ◽  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Actual Behavior ◽  
Stress Strain ◽  
Strain Relationship ◽  
Artificial Neural ◽  
Local Materials ◽  
Ready Mix Concrete

Mechanical properties of concrete are highly dependent on the local materials used in its preparation. experiments on ready mix concrete in our region illustrate the actual behavior of concrete produced by local materials. Six standard cylinders (D=150mm, H=300mm) were casted of most ready mix concrete in central area in Syria (13 of them) covering a wide range of compressive strength . Tests were carried out using a testing machine which gives the applied force values and the corresponding displacement simultaneously until failure. The mean curves representing the (stress-strain) relationship of concrete in compression are drawn, from which the mechanical properties of each mixture were derived, such modulus of elasticity compressive strength ,  and the corresponding strain . Artificial neural networks were trained on experimental test results (using MATLAB). The laws of concrete behaviour were well assimilated by Artificial neural networks, which is possible to be used as an alternative method of available models of stress-strain relationship, by predicting the curve directly for various concrete mixtures prepared using local materials with different mixing ratios, or a complementary method through the adoption of an appropriate mathematical model and then predict its parameters ( ، ، ). ANNs proved their ability to predict mechanical properties of concrete better than linear regression equations, which promises a more accurate and comprehensive prediction.

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.

Prediction of Ferrite-Martensite Dual-Phase Steels Mechanical Properties by Use of Artificial Neural Networks

2013 ◽  
Vol 773-774 ◽  
pp. 268-274
Author(s):  
Amir Ghiami ◽  
Ramin Khamedi
Keyword(s):  
Mechanical Properties ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Back Propagation ◽  
Total Elongation ◽  
Ultimate Tensile Stress ◽  
Dual Phase ◽  
Dual Phase Steels ◽  
Good Ability ◽  
Artificial Neural

This paper presents an investigation of the capabilities of artificial neural networks (ANN) in predicting some mechanical properties of Ferrite-Martensite dual-phase steels applicable for different industries like auto-making. Using ANNs instead of different destructive and non-destructive tests to determine the material properties, reduces costs and reduces the need for special testing facilities. Networks were trained with use of a back propagation (BP) error algorithm. In order to provide data for training the ANNs, mechanical properties, inter-critical annealing temperature and information about the microstructures of many specimens were examined. After the ANNs were trained, the four parameters of yield stress, ultimate tensile stress, total elongation and the work hardening exponent were simulated. Finally a comparison of the predicted and experimental values indicates that the results obtained from the given input data reveal a good ability of the well-trained ANN to predict the described mechanical properties.

Artificial Neural Networks for the Prediction of Mechanical Properties of Soils

2021 ◽  
pp. 758-779
Author(s):  
Lusdali Castillo Delgado ◽  
Daniel Enrique Porta Maldonado ◽  
Juan J. Soria ◽  
Leopoldo Choque Flores
Keyword(s):  
Mechanical Properties ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural ◽  
Prediction Of Mechanical Properties

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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