Abdussamad Ismail
Abdussamad Ismail has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Dong-Sheng Jeng
Griffith Univ. Gold Coast Campus
5 shared publications L.L. Zhang
3 shared publicationsJ.-S. Zhang
2 shared publications
Latest Publications
- 30 May 2019
- Published by Springer Nature in Frontiers of Structural and Civil Engineering
- Vol. 13 (4) , 926-936
Estimating moment capacity of ferrocement members using self-evolving network
- 30 May 2019
- Published by Springer Nature in Frontiers of Structural and Civil Engineering
- Vol. 13 (4) , 926-936
Estimating moment capacity of ferrocement members using self-evolving network
- 7 August 2018
- Published by Springer Nature in Multiscale and Multidisciplinary Modeling, Experiments and Design
- Vol. 2 (1) , 49-61
Prediction of resistance to pile driving using evolutionary neural network
- 7 August 2018
- Published by Springer Nature in Multiscale and Multidisciplinary Modeling, Experiments and Design
- Vol. 2 (1) , 49-61
Prediction of resistance to pile driving using evolutionary neural network
- 13 December 2017
- Published by Springer Nature in Frontiers of Structural and Civil Engineering
- Vol. 12 (4) , 594-608
ANN-based empirical modelling of pile behaviour under static compressive loading
- 13 December 2017
- Published by Springer Nature in Frontiers of Structural and Civil Engineering
- Vol. 12 (4) , 594-608
ANN-based empirical modelling of pile behaviour under static compressive loading
- 1 November 2013
- Published by Elsevier in Ocean Engineering
- Vol. 72, 167-175
Empirical model for the prediction of lateral stability coefficient for un-trenched submarine pipes based on self-evolving neural network (SEANN)
- 1 November 2013
- Published by Elsevier in Ocean Engineering
- Vol. 72, 167-175
Empirical model for the prediction of lateral stability coefficient for un-trenched submarine pipes based on self-evolving neural network (SEANN)
- 1 November 2013
- Published by Elsevier in Engineering Applications of Artificial Intelligence
- Vol. 26 (10) , 2305-2314
An optimised product-unit neural network with a novel PSO–BP hybrid training algorithm: Applications to load–deformation analysis of axially loaded piles
- 1 November 2013
- Published by Elsevier in Engineering Applications of Artificial Intelligence
- Vol. 26 (10) , 2305-2314
An optimised product-unit neural network with a novel PSO–BP hybrid training algorithm: Applications to load–deformation analysis of axially loaded piles
- 1 May 2013
- Published by Elsevier in Engineering Applications of Artificial Intelligence
- Vol. 26 (5-6) , 1540-1549
Predictions of bridge scour: Application of a feed-forward neural network with an adaptive activation function
- 1 May 2013
- Published by Elsevier in Engineering Applications of Artificial Intelligence
- Vol. 26 (5-6) , 1540-1549
Predictions of bridge scour: Application of a feed-forward neural network with an adaptive activation function
- 29 March 2012
- Published by American Society of Civil Engineers (ASCE)
- p. 285-294
Empirical Method for Settlement Prediction of Single Piles Using Higher Order Neural Network and Particle Swarm Optimization
The settlement analysis of axially loaded is a challenging task due to the complicated nature of load transfer mechanism, the nonlinear behaviour of soil and the modification of soil properties as a result of pile installation. In this paper, an empirical model for predicting load - settlement relationship of a single pile under axial load has been developed using a hybrid PSO/higher-order neural network algorithm. The aim of developing the model is to reduce the current level of difficulty in predicting the deformation of piles under axial loading, taking into account the complex behaviour of soil-pile interface and the effects of pile installation on the soil behaviour. A database of static pile loading tests is used to train and validate the model. The inputs to the model include the variation of SPT blow counts and soil type along the pile embedment, type of pile installation and geometric parameters and elastic modulus of the pile. Based on the comparisons with load transfer method and field measurements, the prediction of the new model outperforms the former and in good agreement with the latter.
- 29 March 2012
- Published by American Society of Civil Engineers (ASCE)
- p. 285-294
Empirical Method for Settlement Prediction of Single Piles Using Higher Order Neural Network and Particle Swarm Optimization
The settlement analysis of axially loaded is a challenging task due to the complicated nature of load transfer mechanism, the nonlinear behaviour of soil and the modification of soil properties as a result of pile installation. In this paper, an empirical model for predicting load - settlement relationship of a single pile under axial load has been developed using a hybrid PSO/higher-order neural network algorithm. The aim of developing the model is to reduce the current level of difficulty in predicting the deformation of piles under axial loading, taking into account the complex behaviour of soil-pile interface and the effects of pile installation on the soil behaviour. A database of static pile loading tests is used to train and validate the model. The inputs to the model include the variation of SPT blow counts and soil type along the pile embedment, type of pile installation and geometric parameters and elastic modulus of the pile. Based on the comparisons with load transfer method and field measurements, the prediction of the new model outperforms the former and in good agreement with the latter.
- 1 August 2011
- Published by Elsevier in Engineering Applications of Artificial Intelligence
- Vol. 24 (5) , 813-821
Modelling load–settlement behaviour of piles using high-order neural network (HON-PILE model)
- 1 August 2011
- Published by Elsevier in Engineering Applications of Artificial Intelligence
- Vol. 24 (5) , 813-821
Modelling load–settlement behaviour of piles using high-order neural network (HON-PILE model)