TY  - JOUR
AU  - Onyelowe, Fortune K. C.
AU  - Manan, Aneel
AU  - Khan, Amir
AU  - Hanandeh, Shadi
AU  - Ebid, Ahmed M.
AU  - Ulloa, Nestor
PY  - 2025
DA  - 2025/05/31
TI  - Machine Learning Prediction of the Improvement of Black Cotton Soil by Partial Displacement with Quarry Dust and Fly Ash for Sustainable Road Construction
JO  - Sustainable Intelligent Infrastructure
T2  - Sustainable Intelligent Infrastructure
JF  - Sustainable Intelligent Infrastructure
VL  - 1
IS  - 2
SP  - 52
EP  - 66
DO  - 10.62762/SII.2025.901022
UR  - https://www.icck.org/article/abs/SII.2025.901022
KW  - black cotton soil
KW  - quarry dust
KW  - fly ash
KW  - road construction
KW  - CBR
KW  - UCS
AB  - In this research paper, advanced artificial intelligence (AI) techniques have been applied in predicting the mechanical properties of black cotton soil (BCS) treated by the method of partial displacement of the soil. The materials of the displacement operation were fly ash (FA) and quarry dust (QD), which are both solid wastes derived from coal combustion in power plants and quarrying of stones for the production of aggregates. Previous activities show that BCS has never been treated by displacement of the soil sample but by the addition of these cementitious materials as wt % of the dry soil. The advanced AI techniques were the ANN, GP and the EPR, which executed forty data entries collected from experimental samples of the treated BCS. At the end of the modelling exercise, it was observed that the ANN with the performance indices of SSE 0.8, MAE 0.1, MSE 0.02, RMSE 0.141 and R2 0.983 for the CBR and SSE 0.9, MAE 0.11, MSE 0.023, RMSE 0.151, and R2 0.960 for the UCS outperformed all the other AI techniques. Also, the ANN outperformed other machine learning techniques applied in previous works. In conclusion, the models proposed in this research can be applied in the design and monitoring of pavement foundations constructed with treated BCS.
SN  - 3067-8137
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Onyelowe2025Machine,
  author = {Fortune K. C. Onyelowe and Aneel Manan and Amir Khan and Shadi Hanandeh and Ahmed M. Ebid and Nestor Ulloa},
  title = {Machine Learning Prediction of the Improvement of Black Cotton Soil by Partial Displacement with Quarry Dust and Fly Ash for Sustainable Road Construction},
  journal = {Sustainable Intelligent Infrastructure},
  year = {2025},
  volume = {1},
  number = {2},
  pages = {52-66},
  doi = {10.62762/SII.2025.901022},
  url = {https://www.icck.org/article/abs/SII.2025.901022},
  abstract = {In this research paper, advanced artificial intelligence (AI) techniques have been applied in predicting the mechanical properties of black cotton soil (BCS) treated by the method of partial displacement of the soil. The materials of the displacement operation were fly ash (FA) and quarry dust (QD), which are both solid wastes derived from coal combustion in power plants and quarrying of stones for the production of aggregates. Previous activities show that BCS has never been treated by displacement of the soil sample but by the addition of these cementitious materials as wt \% of the dry soil. The advanced AI techniques were the ANN, GP and the EPR, which executed forty data entries collected from experimental samples of the treated BCS. At the end of the modelling exercise, it was observed that the ANN with the performance indices of SSE 0.8, MAE 0.1, MSE 0.02, RMSE 0.141 and R2 0.983 for the CBR and SSE 0.9, MAE 0.11, MSE 0.023, RMSE 0.151, and R2 0.960 for the UCS outperformed all the other AI techniques. Also, the ANN outperformed other machine learning techniques applied in previous works. In conclusion, the models proposed in this research can be applied in the design and monitoring of pavement foundations constructed with treated BCS.},
  keywords = {black cotton soil, quarry dust, fly ash, road construction, CBR, UCS},
  issn = {3067-8137},
  publisher = {Institute of Central Computation and Knowledge}
}