Four-layer perceptron approach for strength prediction of UHPC

García, Joaquín Abellán

doi:10.1016/j.conbuildmat.2020.119465

Cited by 71 publications

(20 citation statements)

References 32 publications

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“…It can be observed that training the models with synthetic data was performed successfully, such that low MAE and RMSE values were achieved. Such error values were lower than the errors reported in similar studies [30,51]. This demonstrates the significant potential of TGAN to generate credible data for training powerful and generalized ML models.…”

Section: Machine Learning Modelingmentioning

confidence: 43%

“…Qu et al [50] used 162 data examples on the compressive strength of UHPC. Abellán-García [51] collected 717 data points from the literature along with 210 experimental data from laboratory testing to construct a dataset with 927 observations. After outlier detection, their final number of data points used for training and testing was reduced to 827.…”

Section: Data Collectionmentioning

confidence: 99%

“…In another study, Qu et al [50] used 166 experimental data for training and testing an ANN model to predict the effect of steel fibers on the compressive strength of UHPC; 80% of the dataset (133 data points) was used for training, while only 33 data points were used for testing the model. Abellán-García [51] collected a large dataset consisting of 837 datapoints to predict the compressive strength of UHPC; 209 data points (25% of the dataset) were used to test the developed multi-layer perceptron (MLP) model. Therefore, in the present study, a significantly much larger test dataset was utilized to evaluate the prediction accuracy and generalization capability of the developed models than in previous studies.…”

Section: Comparing With Other Studiesmentioning

confidence: 99%

See 2 more Smart Citations

Predicting Ultra-High-Performance Concrete Compressive Strength Using Tabular Generative Adversarial Networks

2020

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There have been abundant experimental studies exploring ultra-high-performance concrete (UHPC) in recent years. However, the relationships between the engineering properties of UHPC and its mixture composition are highly nonlinear and difficult to delineate using traditional statistical methods. There is a need for robust and advanced methods that can streamline the diverse pertinent experimental data available to create predictive tools with superior accuracy and provide insight into its nonlinear materials science aspects. Machine learning is a powerful tool that can unravel underlying patterns in complex data. Accordingly, this study endeavors to employ state-of-the-art machine learning techniques to predict the compressive strength of UHPC using a comprehensive experimental database retrieved from the open literature consisting of 810 test observations and 15 input features. A novel approach based on tabular generative adversarial networks was used to generate 6513 plausible synthetic data for training robust machine learning models, including random forest, extra trees, and gradient boosting regression. While the models were trained using the synthetic data, their ability to generalize their predictions was tested on the 810 experimental data thus far unknown and never presented to the models. The results indicate that the developed models achieved outstanding predictive performance. Parametric studies using the models were able to provide insight into the strength development mechanisms of UHPC and the significance of the various influential parameters.

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Section: Machine Learning Modelingmentioning

confidence: 43%

Section: Data Collectionmentioning

confidence: 99%

Section: Comparing With Other Studiesmentioning

confidence: 99%

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Predicting Ultra-High-Performance Concrete Compressive Strength Using Tabular Generative Adversarial Networks

2020

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“…Para aplicaciones más complejas se usan las arquitecturas de perceptrón multicapa feed-forward [2,3].…”

Section: Redes Neuronales Artificiales (Ann)unclassified

“…En los últimos años la investigación sobre concretos especiales de alto desempeño ha sufrido un importante progreso [1][2][3]. Entre éstos cabrían destacar los concretos de ultra alto desempeño reforzados con fibras (UHPFRC por sus siglas en inglés).…”

Section: Introductionunclassified

Comparación de modelación por Inteligencia Artificial y Regresión Multivariable del comportamiento a flexión del UHPFRC

García

2020

DYNA

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El estudio presentado tiene como objetivo modelar el comportamiento a flexión de los concretos de ultra alto desempeño reforzados con fibras (UHPFRC), incluyendo límite de proporcionalidad (LOP), módulo de rotura (MOR) y sus deflexiones asociadas δLOP y δMOR, utilizando análisis de regresión multivariable y algoritmos de inteligencia artificial (AI). Se construyeron cuatro modelos redes neuronales artificiales (ANN), uno para cada respuesta, con una capa de entrada y una capa oculta, y cuatro modelos de regresión tipo LASSO (least absolute shrinkage and selection operator). Los resultados demostraron la eficiencia de los modelos, evaluados mediante los estadísticos error absoluto medio (MAE), raíz del error cuadrático medio (RMSE), error de sesgo medio normalizado (NMBE) y coeficiente de determinación (R2). Los modelos de redes neuronales mostraron mayor precisión, con valores de R2 de 0.982, 0.969, 0.978 y 0.978, en la predicción de los parámetros (δLOP, LOP, δMOR y MOR) del comportamiento a flexión de los UHPFRC.

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ANN‐based analysis of the effect of SCM on recycled aggregate concrete

Mosquera,

Acosta,

Rodríguez

et al. 2024

Structural Concrete

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Rising environmental awareness has prompted in‐depth studies on how the concrete industry affects the environment. Using recycled concrete aggregates (RCAs) and supplementary cementitious materials (SCMs) in concrete manufacturing provides advantages for sustainability. However, the broader chemical composition of SCMs and the inferior qualities of RCAs compared with natural aggregates (NAs) often lead to a decrease in concrete mechanical strength. The difficulty lies in foreseeing how the inclusion of SCMs and RCAs will affect the concrete compressive strength. The artificial neural network (ANN) approach presented herein can precisely forecast the recycled aggregate concrete (RAC) compressive strength, even when incorporates SCMs. The analysis employing the connection weight approach (CWA) determines how input variables influence compressive strength. Results indicate silica fume contributes most to compressive strength, followed by cement content, silica modulus, fine natural aggregate dosage, and coarse natural aggregate. Additionally, the amount of water utilized, the water/cement ratio, and the presence of RCA are all detrimental to compressive strength. The adverse effect of the cementitious materials' alumina modulus can be attributed to increased water demand during their reaction. Performance metrics of the final ANN model on the testing data subset include R2 = 0.94, and RMSE = 3.11, utilizing 834 data observations after outlier treatment for training and validation purposes. In summary, the ANN‐based approach demonstrates its efficacy in predicting concrete compressive strength when incorporating SCMs and RCAs, shedding light on the influential factors in concrete performance.

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Four-layer perceptron approach for strength prediction of UHPC

Cited by 71 publications

References 32 publications

Predicting Ultra-High-Performance Concrete Compressive Strength Using Tabular Generative Adversarial Networks

Predicting Ultra-High-Performance Concrete Compressive Strength Using Tabular Generative Adversarial Networks

Comparación de modelación por Inteligencia Artificial y Regresión Multivariable del comportamiento a flexión del UHPFRC

ANN‐based analysis of the effect of SCM on recycled aggregate concrete

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