Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Khalid R. Aljanabi"'
Publikováno v:
Journal of King Saud University: Engineering Sciences, Vol 36, Iss 6, Pp 375-384 (2024)
This study focuses on assessing the resilient characteristics of a clayey soil modified with a fly ash (FA)-based geopolymer and reclaimed asphalt pavement (RAP) as an unpaved road material. RAP-geopolymer-soil mixtures were designed using the respon
Externí odkaz:
https://doaj.org/article/336e60a2062a440fab6cc49de5bb5e0a
Publikováno v:
International Journal of Geo-Engineering, Vol 12, Iss 1, Pp 1-18 (2021)
Highlights Neural Networks was used to forecast maximum deflection of braced excavation in homogeneous clay and its position. A sensitivity analysis was accomplished to examine the relative significance of the parameters that influence the models. Th
Externí odkaz:
https://doaj.org/article/3d6713c5d9ed41219bdbe2df76c18427
Publikováno v:
Journal of Engineering, Vol 28, Iss 4 (2022)
The shear strength of soil is one of the most important soil properties that should be identified before any foundation design. The presence of gypseous soil exacerbates foundation problems. In this research, an approach to forecasting shear strength
Externí odkaz:
https://doaj.org/article/1ad41cb9bc56461b861327b4fe278748
Publikováno v:
Journal of King Saud University - Engineering Sciences.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a00f9061a424344532df9d317ec892b6
https://doi.org/10.1016/j.jksues.2022.06.005
https://doi.org/10.1016/j.jksues.2022.06.005
Publikováno v:
International Journal of Geo-Engineering, Vol 12, Iss 1, Pp 1-18 (2021)
An attempt was carried out by using a neural network to predict the maximum deflection and its position caused by braced excavation in homogeneous clay. Six input variables, including excavation depth, Ratio of EI wall/EI of brace, the vertical dista
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::47064001ec3150e298596eca143bdcea
https://doi.org/10.1186/s40703-021-00158-z
https://doi.org/10.1186/s40703-021-00158-z
Publikováno v:
Measurement. 140:622-635
This study was implemented to examine pile load-settlement response and to develop a rapid, highly efficient predictive intelligent model, using a new computational intelligence (CI) algorithm. To achieve this aim, a series of experimental pile load
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ff74b59221161dfc332873f2fd9e83e4
https://doi.org/10.1016/j.measurement.2019.03.043
https://doi.org/10.1016/j.measurement.2019.03.043
Publikováno v:
IOP Conference Series: Earth and Environmental Science. 961:012019
To ensure safe design of structures against settlement, it is necessary to determine the compressibility parameters of the underneath soil especially compression and rebound indices. In this paper, an approach to forecast the compressibility paramete
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3f3c51f9af010eb9f0c6e8464f99db82
https://doi.org/10.1088/1755-1315/961/1/012019
https://doi.org/10.1088/1755-1315/961/1/012019
Autor:
Adel H. Majeed, Dhiya Al-Jumeily, William Atherton, Rafid Al Khaddar, Ameer A. Jebur, Jamila Mustafina, Khalid R. Aljanabi, Zeinab I. Alattar
Publikováno v:
Unsupervised and Semi-Supervised Learning ISBN: 9783030224745
In this study, artificial neural network (ANN) techniques are used in an attempt to predict the nonlinear hyperbolic soil stress–strain relationship parameters (k and Rf). Two ANN models are developed and trained to achieve the planned target, in a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1cefb17bcd403f1bfc607eb107e8e38e
https://doi.org/10.1007/978-3-030-22475-2_8
https://doi.org/10.1007/978-3-030-22475-2_8
Autor:
William Atherton, Dhiya Al-Jumeily, Ahmed J. Aljaaf, Zeinab I. Alattar, Adel H. Majeed, Rafid Al Khaddar, Jamila Mustafina, Ameer A. Jebur, Khalid R. Aljanabi
Publikováno v:
2018 1st Annual International Conference on Information and Sciences (AiCIS).
The principal aim of this study was to develop and verify a new Artificial Intelligence model to predict the hyperbolic soil stress-strain parameter, namely the modulus exponent (n). To achieve the planned aim, artificial neural network was developed
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::567a92c9755985889c16291a8ebd5d80
https://doi.org/10.1109/aicis.2018.00024
https://doi.org/10.1109/aicis.2018.00024