scholarly journals Prediction of Concrete Compressive Strength by Evolutionary Artificial Neural Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Mehdi Nikoo ◽  
Farshid Torabian Moghadam ◽  
Łukasz Sadowski
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Ann Model ◽  
Ann Models ◽  
Compressive Strength Of Concrete ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Different Characteristics ◽  
Mlr Model

Compressive strength of concrete has been predicted using evolutionary artificial neural networks (EANNs) as a combination of artificial neural network (ANN) and evolutionary search procedures, such as genetic algorithms (GA). In this paper for purpose of constructing models samples of cylindrical concrete parts with different characteristics have been used with 173 experimental data patterns. Water-cement ratio, maximum sand size, amount of gravel, cement, 3/4 sand, 3/8 sand, and coefficient of soft sand parameters were considered as inputs; and using the ANN models, the compressive strength of concrete is calculated. Moreover, using GA, the number of layers and nodes and weights are optimized in ANN models. In order to evaluate the accuracy of the model, the optimized ANN model is compared with the multiple linear regression (MLR) model. The results of simulation verify that the recommended ANN model enjoys more flexibility, capability, and accuracy in predicting the compressive strength of concrete.

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 Prediction of Compressive Strength of Concrete in Wet-Dry Environment by BP Artificial Neural Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Chengyao Liang ◽  
Chunxiang Qian ◽  
Huaicheng Chen ◽  
Wence Kang
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Statistical Analysis ◽  
Influencing Factors ◽  
Mechanical Performance ◽  
Tidal Zone ◽  
Structure Degradation ◽  
Compressive Strength Of Concrete ◽  
Artificial Neural

Engineering structure degradation in the marine environment, especially the tidal zone and splash zone, is serious. The compressive strength of concrete exposed to the wet-dry cycle is investigated in this study. Several significant influencing factors of compressive strength of concrete in the wet-dry environment are selected. Then, the database of compressive strength influencing factors is established from vast literature after a statistical analysis of those data. Backpropagation artificial neural networks (BP-ANNs) are applied to establish a multifactorial model to predict the compressive strength of concrete in the wet-dry exposure environment. Furthermore, experiments are done to verify the generalization of the BP-ANN model. This model turns out to give a high accuracy and statistical analysis to confirm some rules in marine concrete mix and exposure. In general, this model is practical to predict the concrete mechanical performance.

Estimation of liquefaction-induced horizontal displacements using artificial neural networks

2001 ◽  
Vol 38 (1) ◽  
pp. 200-207 ◽  
Author(s):  
M Chiru-Danzer ◽  
C H Juang ◽  
R A Christopher ◽  
J Suber
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Field Performance ◽  
Horizontal Displacement ◽  
Lateral Spreading ◽  
Ann Model ◽  
Modeling And Analysis ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Horizontal Displacements

In the present study, artificial neural network (ANN) models based on field performance data are developed for predicting liquefaction-induced horizontal displacements. A database consisting of 443 measurements of horizontal displacements forms the basis for ANN modeling and analysis. The ANN model resulted in predictive capabilities that surpass those of published methods. A sensitivity analysis of the ANN model is conducted to evaluate the effect of each individual input variable on the calculated horizontal displacement. The newly developed ANN model is compared with and shown to be more accurate than other existing methods in predicting liquefaction-induced horizontal displacements.Key words: liquefaction, artificial neural networks, lateral spreading.

Estimation of Groundwater Level Using Artificial Neural Networks: a Case Study of Hatay-Turkey

2017 ◽  
Author(s):  
Fatih Üneş ◽  
Mustafa Demirci ◽  
Eyup Ispir ◽  
Yunus Ziya Kaya ◽  
Mustafa Mamak ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Groundwater Level ◽  
Underground Water ◽  
Population Increase ◽  
Ann Model ◽  
Total Water Consumption ◽  
Ann Models ◽  
Artificial Neural

Groundwater, which is a strategic resource in Turkey, is used for drinking-use, agricultural irrigation and industrial purposes. Population increase and total water consumption are constantly increasing. In order to meet the need for water, over-shoots from underground water have caused significant falls in groundwater level. Estimation of water level is important for planning an efficient and sustainable groundwater management. In this study, groundwater level, monthly mean precipitation and temperature observations of Turkish General Directorate of State Hydraulic Works (DSI) in Hatay, Amik Plain, Kumlu district were used between 2000 and 2015 years. The performance evaluation was done by creating Multi Linear Regression (MLR) and Artificial Neural Networks (ANN) models. The ANN model gave better results than the MLR model.

scholarly journals Prediction of modulus of elasticity and compressive strength of concrete specimens by means of artificial neural networks

2016 ◽  
Vol 38 (1) ◽  
pp. 65 ◽  
Author(s):  
José Fernando Moretti ◽  
Carlos Roberto Minussi ◽  
Jorge Luis Akasaki ◽  
Cesar Fabiano Fioriti ◽  
José Luis Pinheiro Melges ◽  
...  
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Modulus Of Elasticity ◽  
Compressive Strength Of Concrete ◽  
Artificial Neural
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