Sami Abdullah Osman
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Sami Abdullah Osman has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Muhammad Abubakar Dalhat
Transportation and Traffic Engineering Department, College of Engineering, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
3 shared publications - Abdulhamid Al Mojil
Transportation and Traffic Engineering Department, College of Engineering, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31451, Saudi Arabia
1 shared publication - Arshad Jamal
Department of Civil Engineering, College of Engineering, Qassim University, Buraydah, Saudi Arabia
1 shared publication - Fawaz Alharbi
Department of Civil Engineering, College of Engineering, Qassim University, Buraydah 51452, Saudi Arabia
1 shared publication - Hossam E. M. Sallam
Materials Engineering Department, Zagazig University, Zagazig 44519, Egypt
1 shared publication - Meshal Almoshaogeh
Department of Civil Engineering, College of Engineering, Qassim University, Buraydah 51452, Saudi Arabia
1 shared publication
Latest Publications
- Open Access
- 1 June 2025
- Published by KEAI Publishing in Artificial Intelligence in Geosciences
- Vol. 6 (1)
Deep learning based identification of rock minerals from un-processed digital microscopic images of undisturbed broken-surfaces
- 1 June 2025
- Published by KEAI Publishing in Artificial Intelligence in Geosciences
- Vol. 6 (1)
Open AccessDeep learning based identification of rock minerals from un-processed digital microscopic images of undisturbed broken-surfaces
- 19 January 2023
- Published by Taylor & Francis in Road Materials and Pavement Design
- Vol. 24 (11) , 2737-2758
Studying the impact of aggregates and mix volumetric properties on the moisture resistance of asphalt concrete using a feed-Forward artificial neural network
Several studies have reported the effect of various additives on the moisture resistance of AC, but limited studies explored the impact of aggregate’s properties on the moisture sensitivity of AC. In this study, the influence of aggregate properties and mix’s volumetric properties on the moisture sensitivity of AC was studied. The moisture sensitivity of the AC was based on Retained Stability Index (RSI). The study utilised results from 319 plant-produced asphalt mixtures. The was modelled as a function of aggregates and mix’s variables using Artificial Neural Network (ANN). The variables studied include air voids , void in mineral aggregates , clay lump , Los Angeles’s abrasion , soundness value (SV), sand equivalence value , gradation and mix type. Profile method along with weight-connection relative importance ranking were employed to analyse the influence of the input variables on the . The relationship between these variables and the fits higher order polynomial functions.- 19 January 2023
- Published by Taylor & Francis in Road Materials and Pavement Design
- Vol. 24 (11) , 2737-2758
Studying the impact of aggregates and mix volumetric properties on the moisture resistance of asphalt concrete using a feed-Forward artificial neural network
Several studies have reported the effect of various additives on the moisture resistance of AC, but limited studies explored the impact of aggregate’s properties on the moisture sensitivity of AC. In this study, the influence of aggregate properties and mix’s volumetric properties on the moisture sensitivity of AC was studied. The moisture sensitivity of the AC was based on Retained Stability Index (RSI). The study utilised results from 319 plant-produced asphalt mixtures. The was modelled as a function of aggregates and mix’s variables using Artificial Neural Network (ANN). The variables studied include air voids , void in mineral aggregates , clay lump , Los Angeles’s abrasion , soundness value (SV), sand equivalence value , gradation and mix type. Profile method along with weight-connection relative importance ranking were employed to analyse the influence of the input variables on the . The relationship between these variables and the fits higher order polynomial functions.- Open Access
- 28 December 2022
- Published by MDPI AG in Sustainability
- Vol. 15 (1) , 561
Intelligent Assessment of Pavement Condition Indices Using Artificial Neural Networks
The traditional manual approach of pavement condition evaluation is being replaced by more sophisticated automated vehicle systems. Although these automated systems have eased and hastened pavement management processes, research is ongoing to further improve their performances. An average state road agency handles thousands of kilometers of the road network, most of which have multiple lanes. Yet, for practical reasons, these automated systems are designed to evaluate road networks one lane at a time. This requires time, energy, and possibly more equipment and manpower. Multiple Linear Regression (MLR) analysis and Artificial Neural Network (ANN) were employed to examine the feasibility of modeling and predicting pavement distresses of multiple lanes as functions of pavement distresses of a single adjacent lane. The successful implementation of this technique has the potential to cut the energy and time requirement at the condition evaluation stage by at least half, for a uniform multi-lane highway. Results showed promising model performances that indicate the possibility of evaluating a multi-lane highway pavement condition (PC) by single lane inspection. Traffic direction parameters, location, and lane matching parameters contributed significantly to the performance of the ANN PC prediction models.
- 28 December 2022
- Published by MDPI AG in Sustainability
- Vol. 15 (1) , 561
Open AccessIntelligent Assessment of Pavement Condition Indices Using Artificial Neural Networks
The traditional manual approach of pavement condition evaluation is being replaced by more sophisticated automated vehicle systems. Although these automated systems have eased and hastened pavement management processes, research is ongoing to further improve their performances. An average state road agency handles thousands of kilometers of the road network, most of which have multiple lanes. Yet, for practical reasons, these automated systems are designed to evaluate road networks one lane at a time. This requires time, energy, and possibly more equipment and manpower. Multiple Linear Regression (MLR) analysis and Artificial Neural Network (ANN) were employed to examine the feasibility of modeling and predicting pavement distresses of multiple lanes as functions of pavement distresses of a single adjacent lane. The successful implementation of this technique has the potential to cut the energy and time requirement at the condition evaluation stage by at least half, for a uniform multi-lane highway. Results showed promising model performances that indicate the possibility of evaluating a multi-lane highway pavement condition (PC) by single lane inspection. Traffic direction parameters, location, and lane matching parameters contributed significantly to the performance of the ANN PC prediction models.
- Open Access
- 1 January 2019
- Published by FapUNIFESP (SciELO) in Latin American Journal of Solids and Structures
- Vol. 16 (3)
Effect of RAP content on flexural behavior and fracture toughness of flexible pavement
The use of recycled Reclaimed Asphalt Pavement (RAP) material into flexible pavement contributes to sustainable development with environmental and economic benefits. In the present work, Semi-Circular Bending (SCB) specimen has been adopted to study the flexural strength and mode-I fracture toughness of flexible pavement with and without RAP. Two different resources of RAP, namely KS and KF, with two different percentages (20% and 40%) have been examined. Smooth and cracked specimens have been cast to measure the flexural strength and mode-I fracture of flexible pavement containing RAP, respectively. Smooth and cracked specimens for flexible pavement without RAP, as control beams, have been cast for the sake of comparisons. The experimental results showed that, the flexural strength and mode-I fracture toughness of flexible pavement containing 40% RAP are higher than those of flexible pavement without RAP, for the two types of RAP. Comparison between the of work of fracture calculated based on AASHTO TP 105-13 and AASHTO TP 124-16 showed that, the calculation based on AASHTO TP 105-13 is more accurate than that based on AASHTO TP 124-16. Furthermore, the suggested quad-linear relation to work of fracture predictions is easier and more reasonable for presenting the experimental results especially for the descending part of P-LLD curve than that suggested as exponential curve by AASHTO TP 124-16. For all groups, the type of failure is intergranular-mode fracture.
- 1 January 2019
- Published by FapUNIFESP (SciELO) in Latin American Journal of Solids and Structures
- Vol. 16 (3)
Open AccessEffect of RAP content on flexural behavior and fracture toughness of flexible pavement
The use of recycled Reclaimed Asphalt Pavement (RAP) material into flexible pavement contributes to sustainable development with environmental and economic benefits. In the present work, Semi-Circular Bending (SCB) specimen has been adopted to study the flexural strength and mode-I fracture toughness of flexible pavement with and without RAP. Two different resources of RAP, namely KS and KF, with two different percentages (20% and 40%) have been examined. Smooth and cracked specimens have been cast to measure the flexural strength and mode-I fracture of flexible pavement containing RAP, respectively. Smooth and cracked specimens for flexible pavement without RAP, as control beams, have been cast for the sake of comparisons. The experimental results showed that, the flexural strength and mode-I fracture toughness of flexible pavement containing 40% RAP are higher than those of flexible pavement without RAP, for the two types of RAP. Comparison between the of work of fracture calculated based on AASHTO TP 105-13 and AASHTO TP 124-16 showed that, the calculation based on AASHTO TP 105-13 is more accurate than that based on AASHTO TP 124-16. Furthermore, the suggested quad-linear relation to work of fracture predictions is easier and more reasonable for presenting the experimental results especially for the descending part of P-LLD curve than that suggested as exponential curve by AASHTO TP 124-16. For all groups, the type of failure is intergranular-mode fracture.