All Outputs (4)

Prediction of strength enhancement of subgrade soil reinforced with geotextile using artificial neural network and M5P model tree (2020)
Journal Article
Singh, T., Goyal, Y., & Kumar, S. (2020). Prediction of strength enhancement of subgrade soil reinforced with geotextile using artificial neural network and M5P model tree. European Journal of Molecular and Clinical Medicine, 7(8),

Geosynthetics layers are being implemented as reinforcement to enhance the strength of subgrade soil (which is calculated in terms of CBR). Present research work, aims at investigating the strength enhancement in terms of CBR through experimental stu... Read More about Prediction of strength enhancement of subgrade soil reinforced with geotextile using artificial neural network and M5P model tree.

Modeling oblique load carrying capacity of batter pile groups using neural network, random forest regression and M5 model tree (2018)
Journal Article
Singh, T., Pal, M., & Arora, V. K. (2019). Modeling oblique load carrying capacity of batter pile groups using neural network, random forest regression and M5 model tree. Frontiers of Structural and Civil Engineering, 13(3), 674-685. https://doi.org/10.1007/s11709-018-0505-3

M5 model tree, random forest regression (RF) and neural network (NN) based modelling approaches were used to predict oblique load carrying capacity of batter pile groups using 247 laboratory experiments with smooth and rough pile groups. Pile length... Read More about Modeling oblique load carrying capacity of batter pile groups using neural network, random forest regression and M5 model tree.

Modeling of oblique load test on batter pile group based on support vector machines and gaussian regression (2017)
Journal Article
Singh, T., Pal, M., & Arora, V. K. (2018). Modeling of oblique load test on batter pile group based on support vector machines and gaussian regression. Geotechnical and Geological Engineering, 36(3), 1597-1607. https://doi.org/10.1007/s10706-017-0413-7

This paper evaluates the potential of two machine learning approaches i.e. Support vector machine (SVR) and Gaussian processes (GP) regression to model the oblique load capacity of batter pile groups. Linear regression was used to compare the perform... Read More about Modeling of oblique load test on batter pile group based on support vector machines and gaussian regression.

Ultimate capacity of battered pile groups subjected to oblique pullout loads in sand (2017)
Journal Article
Singh, T., Pal, M., & Arora, V. K. (2017). Ultimate capacity of battered pile groups subjected to oblique pullout loads in sand. International Journal of Geosynthetics and Ground Engineering, 3(3), 28. https://doi.org/10.1007/s40891-017-0103-9

A testing program comprising 250 oblique pullout tests was conducted to study the effect of variable parameter on pullout capacity of batter pile groups in sand. Test was conducted in laboratory on five pile groups. Model piles consist of aluminium h... Read More about Ultimate capacity of battered pile groups subjected to oblique pullout loads in sand.