School of Infrastructure Process Engineering and Technology (SIPET)

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School of Infrastructure Process Engineering and Technology (SIPET)

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    Development of Models for Prediction of Soil Cohesion Using Machine Learning Algorithms
    (Department of Civil Engineering, FUT Minna, 2024-12-12) Muhammed, R. O.,; Adejumo, T. E.; Alhaji, M. M.; Kolo, D. N.; Eze, F. E.
    Accurate prediction of soil cohesion is crucial for the safe and economical design of geotechnical structures. This study employed five machine learning models—Artificial Neural Network (ANN), Random Forest (RF), Support Vector Regression (SVR), Gradient Boosting (GB), and Decision Tree (DT)—to predict cohesion (c) using a laboratory dataset of 233 samples. The dataset, augmented to 5000 samples using Getel, was split into 70% training and 30% testing sets. Model performance was evaluated using R-squared and Mean Squared Error (MSE). Results showed that Random Forest outperformed other models, achieving the highest R-squared score of 0.622 and the lowest MSE of 56.74, indicating excellent model fit and high predictive accuracy. Feature importance analysis revealed that plasticity, primarily influenced by Liquid Limit (LL) with an importance score of 0.879606, and Plasticity Index (PI) with an importance score of 1.441646, significantly impacts cohesion. Natural Moisture Content (NMC) also showed significant influence with a score of 0.670434. Particle Size Distribution and Specific Gravity (Gs) also contributed to the predictions. This study demonstrates the potential of machine learning models to enhance the accuracy and efficiency of soil characterization and geotechnical engineering design in predicting soil cohesion.
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    DEVELOPMENT OF MODELS FOR PREDICTING CALIFORNIA BEARING RATIO OF LATERITIC SOIL USING SELECTED SOFT COMPUTING TECHNIQUES
    (3rd International Conference on Artificial intelligence and Robotics, 2023-05-10) F.E Eze; T.E Adejumo; A A. Amadi; YUSUF, Abdulazeez
    Models for predicting the California bearing ratio values of lateritic soil was developed using soft computing techniques. Soft computing techniques are algorithm which find provably correct and optimal solutions to problem. The Soaked CBR values used in pavement design takes about 96 hours to complete the test process. This can be time-consuming and expensive, Hence the need for researches to seek for alternate means of obtaining it. Several researchers have employed the use of Artificial Neural network (ANN), Gene expression programming (GEP), Support Vector machine (SVM) and Deep neural network (DNN) to predict CBR values, these models have inherent limitations such as sensitivity to hyper-parameters, limited flexibility and lack of interpretability. This study proposes a new model to address this challenge, Artificial Neural Networks (ANN) and its hybrid (ANFIS) were considered. Soil samples were collected from a burrow pit and required tests were conducted on the collected soil samples, Tests carried out are index, compaction and California bearing ratio. The experimental result data was augmented from data gotten from previous research work (unpublished) in same study area. The result gotten was used for training the models. 70% of the data was used for training and the remaining for the validation of the models. Two different models were developed and the performance of each model was measured by the coefficient of determination (R2), Mean Square Error (MSE) and Root mean square Error (RMSE). Upon analyzing the result, the both models ANN and ANFIS demonstrated high accuracies but ANFIS model gave a higher predictive accuracy of 0.98 as R2, RMSE of 0.11 and MSE of 0.33. ANFIS Model demonstrated exceptional accuracy and precision in capturing complex relationships within the data and hence should be adopted in the prediction of CBR values of lateritic soil.