scholarly journals ANN-based Fatigue Strength of Concrete Under Compression

2020 ◽  
Author(s):  
Abambres M ◽  
Lantsoght E
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Fatigue Strength ◽  
Relative Error ◽  
Maximum Relative Error ◽  
Ann Model ◽  
Concrete Compressive Strength ◽  
Design Code ◽  
Number Of Cycles

<p>When concrete is subjected to cycles of compression, its strength is lower than the statically determined concrete compressive strength. This reduction is typically expressed as a function of the number of cycles. In this work, we predict the reduced capacity as function of a given number of cycles by means of artificial neural networks (ANN). A 203-point experimental dataset gathered from the literature was used. The proposed ANN model results in a maximum relative error of 5.1% and a mean counterpart of 1.2% for the whole dataset. It’s shown that the proposed analytical model outperforms the existing design code expressions.</p>

scholarly journals ANN-based Fatigue Strength of Concrete Under Compression

2021 ◽  
Author(s):  
Miguel Abambres ◽  
Lantsoght E
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Fatigue Strength ◽  
Relative Error ◽  
Maximum Relative Error ◽  
Ann Model ◽  
Concrete Compressive Strength ◽  
Design Code ◽  
Number Of Cycles

<p>When concrete is subjected to cycles of compression, its strength is lower than the statically determined concrete compressive strength. This reduction is typically expressed as a function of the number of cycles. In this work, we predict the reduced capacity as function of a given number of cycles by means of artificial neural networks (ANN). A 203-point experimental dataset gathered from the literature was used. The proposed ANN model results in a maximum relative error of 5.1% and a mean counterpart of 1.2% for the whole dataset. It’s shown that the proposed analytical model outperforms the existing design code expressions.</p>

scholarly journals ANN-based Fatigue Strength of Concrete Under Compression

2020 ◽  
Author(s):  
Abambres M ◽  
Lantsoght E
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Fatigue Strength ◽  
Relative Error ◽  
Maximum Relative Error ◽  
Ann Model ◽  
Concrete Compressive Strength ◽  
Design Code ◽  
Number Of Cycles

<p>When concrete is subjected to cycles of compression, its strength is lower than the statically determined concrete compressive strength. This reduction is typically expressed as a function of the number of cycles. In this work, we predict the reduced capacity as function of a given number of cycles by means of artificial neural networks (ANN). A 203-point experimental dataset gathered from the literature was used. The proposed ANN model results in a maximum relative error of 5.1% and a mean counterpart of 1.2% for the whole dataset. It’s shown that the proposed analytical model outperforms the existing design code expressions.</p>

scholarly journals ANN-Based Fatigue Strength of Concrete under Compression

Materials ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 3787 ◽  
Author(s):  
Abambres ◽  
Lantsoght
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Relative Error ◽  
Maximum Relative Error ◽  
Neural Net ◽  
Accurate Assessment ◽  
Concrete Compressive Strength ◽  
Mean Relative Error ◽  
Proposed Model ◽  
Number Of Cycles

When concrete is subjected to cycles of compression, its strength is lower than the statically determined concrete compressive strength. This reduction is typically expressed as a function of the number of cycles. In this work, we study the reduced capacity as a function of a given number of cycles by means of artificial neural networks. We used an input database with 203 datapoints gathered from the literature. To find the optimal neural network, 14 features of neural networks were studied and varied, resulting in the optimal neural net. This proposed model resulted in a maximum relative error of 5.1% and a mean relative error of 1.2% for the 203 datapoints. The proposed model resulted in a better prediction (mean tested to predicted value = 1.00 with a coefficient of variation 1.7%) as compared to the existing code expressions. The model we developed can thus be used for the design and the assessment of concrete structures and provides a more accurate assessment and design than the existing methods.

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 Evaluation of the Undrained Shear Strength of Organic Soils from a Dilatometer Test Using Artificial Neural Networks

2018 ◽  
Vol 8 (8) ◽  
pp. 1395 ◽  
Author(s):  
Zbigniew Lechowicz ◽  
Masaharu Fukue ◽  
Simon Rabarijoely ◽  
Maria Sulewska
Keyword(s):  
Neural Networks ◽  
Shear Strength ◽  
Artificial Neural Networks ◽  
Relative Error ◽  
Undrained Shear Strength ◽  
Organic Soils ◽  
Maximum Relative Error ◽  
Empirical Methods ◽  
Pressure Reading ◽  
Undrained Shear

The undrained shear strength of organic soils can be evaluated based on measurements obtained from the dilatometer test using single- and multi-factor empirical correlations presented in the literature. However, the empirical methods may sometimes show relatively high values of maximum relative error. Therefore, a method for evaluating the undrained shear strength of organic soils using artificial neural networks based on data obtained from a dilatometer test and organic soil properties is presented in this study. The presented neural network, with an architecture of 5-4-1, predicts the normalized undrained shear strength based on five independent variables: the normalized net value of a corrected first pressure reading (po − uo)/σ′v, the normalized net value of a corrected second pressure reading (p1 − uo)/σ′v, the organic content Iom, the void ratio e, and the stress history indictor (oc or nc). The neural model presented in this study provided a more reliable prediction of the undrained shear strength in comparison to the empirical methods, with a maximum relative error of ±10%.

scholarly journals PERFORMANCE OF ARTIFICIAL NEURAL NETWORKS AND FUZZY INFERENCE SYSTEMS FOR PREDICTING CONCRETE COMPRESSIVE STRENGTH

2019 ◽  
Vol 3 (2) ◽  
Author(s):  
Jorge Luis Santamaria
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Fuzzy Inference Systems ◽  
Concrete Compressive Strength ◽  
Fuzzy Models ◽  
Inference Systems

Artificial neural networks (ANN) have been traditionally utilized for developing prediction models based on experimental data; however, fuzzy logic theory is a novel tool that could also be applied as well in this cases. The present study compares the performance of two different approaches; namely, ANN and fuzzy inference systems (FIS) for predicting concrete compressive strength. Adaptive neuro fuzzy inference systems (ANFIS) was the technique based on input – output experimental data that was used to create two Sugeno type fuzzy models for estimating concrete compressive strength and then compared with several ANN prediction models created through different methods. Subtractive clustering method was the clustering procedure for establishing the number of membership functions and fuzzy rules. Predicted data resulting from all models were presented in a comparative manner and validation analyses were conducted in order to observe the performance of ANN and ANFIS. The results indicate that both fuzzy models performed very well when estimating concrete compressive strength (i.e., all R2 values are greater than 90%) while for ANN, only one ANN method had an R2 value greater than 90%.

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