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Bearing capacity and settlement prediction of multi-edge skirted footings resting on sand
Capacidad de carga y predicción de asentamiento de zapatas bordeadas de bordes múltiples que descansan sobre arena
DOI:
https://doi.org/10.15446/ing.investig.v40n3.83170Keywords:
square/circular skirted footings, multi-edged skirted footings, bearing capacity ratio, settlement reduction factor, artificial neural networks, multivariable regression analysis (en)zapatas de zócalo cuadradas/circulares, zapatas bordeadas de bordes múltiples, relación de capacidad de carga, factor de reducción de liquidación, redes neuronales artificiales, análisis de regresión multivariable (es)
This paper presents the application of artificial neural networks (ANN) and multivariable regression analysis (MRA) to predict the bearing capacity and the settlement of multi-edge skirted footings on sand. Respectively, these parameters are defined in terms of the bearing capacity ratio (BCR) of skirted to unskirted footing and the settlement reduction factor (SRF), the ratio of the difference in settlement of unskirted and skirted footing to the settlement of unskirted footing at a given pressure. The model equations for the prediction of the BCR and the SRF of the regular shaped footing were first developed using the available data collected from the literature. These equations were later modified to predict the BCR and the SRF of the multi-edge skirted footing, for which the data were generated by conducting a small scale laboratory test. The input parameters chosen to develop ANN models were the angle of internal friction (ϕ) and skirt depth (Ds) to the width of the footing (B) ratio for the prediction of the BCR; as for the SRF one additional input parameter was considered: normal stress (?). The architecture for the developed ANN models was 2-2-1 and 3-2-1 for the BCR and the SRF, respectively. The R2 for the multi-edge skirted footings was in the range of 0,940-0,977 for the ANN model and 0,827-0,934 for the regression analysis. Similarly, the R2 for the SRF prediction might have been 0,913-0,985 for the ANN model and 0,739-0,932 for the regression analysis. It was revealed that the predicted BCR and SRF for the multi-edge skirted footings with the use of ANN is superior to MRA. Furthermore, the results of the sensitivity analysis indicate that both the BCR and the SRF of the multi-edge skirted footings are mostly affected by skirt depth, followed by the friction angle of the sand.
Este documento presenta la aplicación de redes neuronales artificiales (ANN) y el análisis de regresión multivariable (MRA) para predecir la capacidad de carga y el asentamiento de las zapatas bordeadas de bordes múltiples en arena. Estos parámetros se definen, respectivamente, en términos de la relación de capacidad de carga (BCR) de carga de la zapata con zócalo y sin zócalo y el factor de reducción de asentamiento (SRF), la razón de la diferencia en la solución de zócalo sin zócalo y zapatas bordeadas para el asentamiento de zapatas sin falda a una presión determinada. Las ecuaciones modelo para predecir la BCR y el SRF de la zapata de forma regular se desarrollaron primero utilizando los datos disponibles recopilados de la literatura. Estas ecuaciones se modificaron posteriormente para predecir la BCR y el SRF de la zapata bordeada de bordes multiples, para la cual se generaron los datos mediante la realización de una prueba de laboratorio a pequeña escala. Los parámetros de entrada elegidos para desarrollar modelos ANN fueron el ángulo de fricción interna (ϕ), la profundidad del faldón (Ds) al ancho de la relación de zapata (B) para la predicción del BCR; en cuanto al SRF, se consideró un parámetro de entrada adicional: la tensión normal (?). La arquitectura para los modelos ANN desarrollados fue 2-2-1 y 3-2-1 para la BCR y el SRF, respectivamente. El R2 para las zapatas bordeadas de bordes múltiples estuvo en el rango de 0,940-0,977 para el modelo ANN y 0,827-0,934 para el análisis de regresión. De manera similar, el R2 para la predicción del SRF pudo haber sido de 0,913-0,985 para el modelo ANN y 0,739-0,932 para el análisis de regresión. Se reveló que la BCR predicha y el SRF para las zapatas con borde de múltiples bordes con el uso de ANN es superior al MRA. Además, los resultados del análisis de sensibilidad indican que tanto el BCR como el SRF de las zapatas bordeadas de bordes múltiples se ven más afectados por la profundidad de la falda, seguida del ángulo de fricción de la arena.
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Copyright (c) 2020 Rakesh Kumar Dutta, Tammineni Gnananandarao, Vishwas Nandkishor Khatri
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