Prediction the Settlement of the Lightweight Road Embankments Using ANN


  • Ahlam Ismael Hasan Highway and bridge department, Technical College of engineering, Duhok Polytechnic University. Duhok, Kurdistan.
  • Ahmed Mohammad Najmalden Technical College of engineering, Duhok, Kurdistan. Polytechnic University.
  • Abdulbasit Abdulaziz Muhmood Transportation and highway engineering, Northern Technical University, Iraq-Mosul.



        The effectiveness of the Artificial Neural Network in modelling the link between the settlement of the embankment and embankment parameters is demonstrated in this study. Data were collected from previous research and the Plaxis 3D program. Eleven factors affecting the settlement of embankments were included in this study. These factors are (Side slope & Height(h) of the embankment), (Axial load, and No. of point axial load), in addition to (γunsat, γsat, Initial void ratio (e), Friction angle (Ø), Cohesion (c), Poisson ratio (ⱱ), and modulus of elasticity (E) which are the properties of the soil under embankment). In the current study, a back-propagation neural network approach was used. The relative importance analysis showed that the soil modulus of elasticity (E) and height (h) of the embankment was the most influential parameters than other inputs. For each training, validation, and testing step, the correlation coefficient R-values of the settlement embankment dataset of the ANN model were obtained to be approximately 0.984. The results revealed the validity of using Artificial Neural Networks to generate settlement embankment soil values.


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جمهورية العراق وزارة العمال والسكان. دليل المهندس المقيم للمشاريع الانشائية2015. الطبعة الثانية. من الرابط.



How to Cite

Ismael Hasan , A. ., Mohammad Najmalden, A. ., & Abdulaziz Muhmood, A. (2024). Prediction the Settlement of the Lightweight Road Embankments Using ANN. Academic Journal of Nawroz University, 13(2), 666–682.