Talal Awwad

Prof. Dr. Talal Awwad

Soils and Foundations Department, Petersburg State Transport University, Saint Petersburg, Sankt-Peterburg, Russia
orcid
Academician of NAMS. Honorary Academician of National Engine...

Research Focus & Metrics

Academic Career Length

14Years (2013-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
1 January 2017 in Procedia Engineering
Verified
Journal Article
Open access
Talal AwwadTalal Awwad
Talal Awwad
MDModar Donia
Modar Donia
LALana Awwad
Lana Awwad

1 March 2016 in Earthquake Engineering and Engineering Vibration
Verified
Journal Article
Closed access
PDF file
Talal AwwadTalal Awwad
Talal Awwad
MDModar Donia
Modar Donia

12 July 2025 in Buildings
Verified
Journal Article
Open access
PDF file
Yelbek UtepovYelbek Utepov
Yelbek Utepov
AAAliya K. Aldungarova
Aliya K. Aldungarova
AMAssel Mukhamejanova
Assel Mukhamejanova
Talal AwwadTalal Awwad
Talal Awwad
SKSabit Karaulov
Sabit Karaulov
IMIndira Makasheva
Indira Makasheva
To support bearing capacity estimates, this study develops and tests a geoprocessing workflow for predicting soil properties using Empirical Bayesian Kriging 3D and a classification function. The model covers a 183 m × 185 m × 24 m site in Astana (Kazakhstan), based on 16 boreholes (15–24 m deep) and 77 samples. Eight geotechnical properties were mapped in 3D voxel models (812,520 voxels at 1 m × 1 m × 1 m resolution): cohesion (c), friction angle (φ), deformation modulus (E), plasticity index (PI), liquidity index (LI), porosity (e), particle size (PS), and particle size distribution (PSD). Stratification patterns were revealed with ~35% variability. Maximum φ (34.9°), E (36.6 MPa), and PS (1.29 mm) occurred at 8–16 m; c (33.1 kPa) and PSD peaked below 16 m, while PI and e were elevated in the upper and lower strata. Strong correlations emerged in pairs φ-E-PS (0.91) and PI-e (0.95). Classification identified 10 soil types, including one absent in borehole data, indicating the workflow’s capacity to detect hidden lithologies. Predicted fractions of loams (51.99%), sandy loams (22.24%), and sands (25.77%) matched borehole data (52%, 26%, 22%). Adjacency analysis of 2,394,873 voxel pairs showed homogeneous zones in gravel–sandy soils (28%) and stiff loams (21.75%). The workflow accounts for lateral and vertical heterogeneity, reduces subjectivity, and is recommended for digital subsurface 3D mapping and construction design optimization.

30 October 2018
Verified
Book Chapter
Closed access
PDF file
Talal AwwadTalal Awwad
Talal Awwad
ZSZh. A. Shakhmov
Zh. A. Shakhmov
RLR. E. Lukpanov
R. E. Lukpanov
SYS. B. Yenkebayev
S. B. Yenkebayev

1 August 2019 in International Journal of GEOMATE
Verified
Journal Article
Closed access
Talal AwwadTalal Awwad
Talal Awwad

13 July 2018
Verified
Book Chapter
Closed access
PDF file
Talal AwwadTalal Awwad
Talal Awwad
VGVladimir Gruzin
Vladimir Gruzin
VKVladimir Kim
Vladimir Kim