Browsing by Author "Shakeri, Jamshid"

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    Application of ANFIS hybrids to predict coefficients of curvature and uniformity of treated unsaturated lateritic soil for sustainable earthworks
    (Cleaner Materials, 2021) Onyelowe, Kennedy C.; Shakeri, Jamshid; Salahudeen, Bunyamin; Arinze, Emmanuel E.; Ugwu, Hyginus U.
    Unsaturated lateritic soils are complex soils to work with due to moisture effects. So, the determination of its properties requires lots of time, labor and equipment. For this reason, the application of evolutionary learning techniques has been adopted to overcome these complexities. Lateritic soil under unsaturated condition classified as poorly graded and A‐7–6 group was subjected to treatment by using hybrid cement and nanostructured quarry fines in a stabilization method. The clay activity, clay content and frictional angle were determined through multiple experiments at different proportions of the additives. 121 datasets were collected through the multiple testing of treated specimens and 70% and 30% of the datasets were used in the model training and testing, respectively to predict the coefficients of curvature and uniformity (Cc and Cu) of the unsaturated lateritic soil. Fist, the multi‐linear regression (MLR) model showed that the selected input parameters correlated well with the output parameters. The model performance evaluation and validation selected indicators; R2, RMSE and MAE showed that ANFIS with 0.9999, 0.0021 and 0.0015 respectively, for the training and 0.9994, 0.0077 and 0.0059 respectively outclassed all its hybrid techniques and MLR in both training and testing. However, ANFIS‐PSO with performance indicators 0.9996, 0.0062 and 0.0050 respectively (training) and 0.9989, 0.0095 and 0.0073 respectively (testing); followed by ANFIS‐GA; 0.9991, 0.0094, and 0.0065 respectively (training) and 0.0089, 0.0099, and 0.0079 (testing) outclassed the other learning techniques for the Cc prediction model while ANFIS‐GA; 0.9949, 0.1000, and 0.0798 respectively (training) and 0.9954, 0.0983, and 0.0807 respectively, followed by ANFIS‐PSO; 0.9893, 0.1347, and 0.1011 respectively (training) and 0.9951, 0.1127, and 0.0924 respectively outclassed the other techniques for the Cu prediction model. Finally, ANFIS and its evolutionary hybrid techniques have shown their usefulness and flexibility in predicting stabilized unsaturated soil properties for sustainable earthwork design, construction and foundation performance monitoring.
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    Computational Modeling of Desiccation Properties (CW, LS, and VS) of Waste-Based Activated Ash-Treated Black Cotton Soil for Sustainable Subgrade Using Artificial Neural Network, Gray-Wolf, and Moth-Flame Optimization Techniques
    (Advances in Materials Science and Engineering, 2022) Onyelowe, Kennedy C.; Shakeri, Jamshid; Amini-Khoshalan, Hasel; Usungedo, Thompson F.; Alimoradi-Jazi, Mohammadreza
    Artificial neural network (ANN), gray-wolf, and moth-flame optimization (GWO and MFO) techniques have been used in this research work to predict the effect of activated sawdust ash (ASDA) on the crack width (CW), linear shrinkage (LS), and volumetric shrinkage (VS) of a black cotton soil utilized as a subgrade material. Problematic soils or black cotton soils are not good pavement foundation materials except that they are pretreated in order to meet the basic strength characteristics required for roads in Nigeria. Due to this reason, there has been ongoing research to evaluate the best practices in which black cotton soils can be favorably utilized in earthwork construction. On the other hand, there is a huge concern on the solid waste management system in the wood processing environment and the recycling of sawdust into ash and its reuse as an alternative binder has offered a sustainable disposal system. +e work tries to use AI-based techniques to predict the crack and shrinkage behaviors of BCS treated with saw dust ash activated with alkali materials. +ere was appreciable improvement in the shrinkage and crack parameters over the 30-day drying period due to the addition of ASDA. +e intelligent model results showed that the three techniques successfully predicted the CW, LS, and VS with a performance accuracy above 90%, while ANN produced the minimal error in performance outperforming the other techniques. Sensitivity study showed that the drying time (T) was the most influential of the studied parameter. Hence, soil stabilization has shown its potential system of waste management in the wood processing industry.
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    Intelligent prediction of coefficients of curvature and uniformity of hybrid cement modified unsaturated soil with NQF inclusion
    (Cleaner Engineering and Technology, 2021) Onyelowe, Kennedy C.; Shakeri, Jamshid
    The cost and sophisticated equipment required to conduct earthwork laboratory experiments have been of concern to the design and performance monitoring of infrastructures in recent times. Lateritic soils especially those under unsaturated conditions are erratic and deserve close attention in terms of laboratory studies. In order to overcome the rigors and time consumed during experimental procedures, soft computing has been used to predict soil parameters for the purpose of design and construction. In this work, the ANN, GEP and LMR were employed to predict the coefficients of curvature and uniformity of lateritic soil treated with multiple binders locally generated, which were hybrid cement (HC) and nanostructured quarry fines (NQF). The effect of the varying dosages of HC and NQF added to the soil were studied and the behavior of clay activity, clay content, frictional angle, coefficients of curvature and uniformity were measure. 121 datasets were generated from the experimental exercise for the selected parameter both for the predictors and for the targets. These datasets were deployed in the ratio of 70 is to 30% for training and testing of the models predictions respectively. The performances of the models were evaluated using error analysis (VAF, RMSE, MAE) and accuracy (R2) indices and it was observed that the ANN outclassed both GEP and LMR due to its speed and robustness in adopting backpropagation and feed-forward algorithms. Furthermore, the sensitivity analysis showed that F, C, H (HC), NQF and Ac in that order of most influential to least influential influenced the behavior of the Cc model with H (HC) and NQF showing equal effect on the Cc. Also, H (HC), NQF, F, C and Ac in that order of influence from most to least affected the behavior of the Cu predicted model also with HC and NQF having equal effect on the Cu. Generally, the learning techniques showed good performance in predicting the outputs hence are good techniques to be utilized in design and performance evaluation.
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    Support vector machine (SVM) prediction of coefficients of curvature and uniformity of hybrid cement modified unsaturated soil with NQF inclusion
    (leaner Engineering and Technology, 2021) Onyelowe, Kennedy C.; Mahesh, Chilakala B.; Srikanth, Bandela; Nwa-David, Chidobere; Obimba-Wogu, Jesuborn; Shakeri, Jamshid
    Support vector machine (SVM) with its feature known as the statistical risk minimization (SRM) has been employed in the prediction of coefficient of curvature and uniformity on unsaturated lateritic soil treated with composites of hybrid cement and nanostructured quarry fines. This feature utilized by SVM is the advantage it exercises over other intelligent learning techniques. This prediction has become necessary due to the time and equipment needs required to regularly conduct laboratory experiments prior to earthwork designs and construction. It is important to note that earthwork projects involving unsaturated soils pose threats of failure due to volume changes during seasonal cycles of wetting and drying especially for hydraulically bound environments and substructures. With an intelligent prediction, these design and construction worries are overcome. The soil used in the current work has been classified as an A-7-6 group soil with highly plastic consistency. Multiple experiments were conducted to generate multitude of datasets for the hybrid cement, nanostructured quarry fines, clay content and activity and frictional angle, which were selected as the independent variables for the model to predict coefficients of curvature and uniformity as the dependent variables. In order to correlate the relationship between the input and output parameters and as well validate the SVM model, detailed statistical analysis including Pearson’s coefficient of correlation (R) and determination (R2) and error analysis were conducted. Based upon the statistical analysis, the parameters were observed to have good correlation and determination ranging between 0.97 and 0.99. It was also observed that SVM outclassed MLR more in predicting Cu then it did in predicting Cc. Finally, sensitivity analysis was carried out and it was found that the Cc value is dependent mostly on frictional angle while Cu is dependent most on the NQF.