Potential of neural networks for structural damage localization

Abambres, Miguel; Marcy, Marília; Doz, Graciela

doi:10.18272/aci.v11i2.1305

Cited by 5 publications

(6 citation statements)

References 40 publications

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“…The validation of the developed software can be found in [86]. Moreover, several papers involving the successful application of this software have already been published [84,87].…”

Section: Methodsmentioning

confidence: 99%

ANN-Based Fatigue Strength of Concrete under Compression

Abambres

Lantsoght

2019

Materials

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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.

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“…The validation of the developed software can be found in [86]. Moreover, several papers involving the successful application of this software have already been published [84,87].…”

Section: Methodsmentioning

confidence: 99%

ANN-Based Fatigue Strength of Concrete under Compression

Abambres

Lantsoght

2019

Materials

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“…If the training set selected in (e) guarantees |P t* /P − p t | ≤ 0.2, then that becomes the training data to be taken for simulation. Otherwise, the training data should be selected according to [112].…”

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confidence: 99%

ANN-Based Shear Capacity of Steel Fiber-Reinforced Concrete Beams without Stirrups

Abambres

Lantsoght

2019

Fibers

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Comparing experimental results of the shear capacity of steel fiber-reinforced concrete (SFRC) beams without stirrups to the capacity predicted using current design equations and other available formulations shows that predicting the shear capacity of SFRC beams without mild steel shear reinforcement is still difficult. The reason for this difficulty is the complex mechanics of the problem, where the steel fibers affect the different shear-carrying mechanisms. Since this problem is still not fully understood, we propose the use of artificial intelligence (AI) to derive an expression based on the available experimental data. We used a database of 430 datapoints obtained from the literature. The outcome is an artificial neural network-based expression to predict the shear capacity of SFRC beams without shear reinforcement. For this purpose, many thousands of artificial neural network (ANN) models were generated, based on 475 distinct combinations of 15 typical ANN features. The proposed “optimal” model results in maximum and mean relative errors of 0.0% for the 430 datapoints. The proposed model results in a better prediction (mean Vtest/VANN = 1.00 with a coefficient of variation 1 × 10−15) as compared to the existing code expressions and other available empirical expressions, with the model by Kwak et al. giving a mean value of Vtest/Vpred = 1.01 and a coefficient of variation of 27%. Until mechanics-based models are available for predicting the shear capacity of SFRC beams without shear reinforcement, the proposed model thus offers an attractive solution for estimating the shear capacity of SFRC beams without shear reinforcement. With this approach, designers who may be reluctant to use SFRC because of the large uncertainties and poor predictions of experiments, may feel more confident using the material for structural design.

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“…The validation of the developed software can be found in [86]. Moreover, several papers involving the successful application of this software have already been published [84,87].…”

Section: Characteristics Of Artificial Neural Network In This Studymentioning

confidence: 99%

ANN-based Fatigue Strength of Concrete Under Compression

Abambres¹,

Lantsoght²

2021

Preprint

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<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>

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Potential of neural networks for structural damage localization

Cited by 5 publications

References 40 publications

ANN-Based Fatigue Strength of Concrete under Compression

ANN-Based Fatigue Strength of Concrete under Compression

ANN-Based Shear Capacity of Steel Fiber-Reinforced Concrete Beams without Stirrups

ANN-based Fatigue Strength of Concrete Under Compression

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