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Abstract

The primary objective of this paper was to develop an artificial neural network (ANN) simulation environment and mathematical models for predicting with high accuracy soil compression parameters. The experiments were conducted at the College of Agriculture - University of Basra, located at Garmat Ali, the soil was silty clay loam. The factors that were investigated are moisture content (14 and 24%), tillage depths (0, 15, 30, 45, and 50 cm) forward speeds (0.57, 0.94, and 1.34 m.s-1) and tire pressures (50, 100, and 150 kPa). ANN environment was developed with the back propagation algorithm using MATLAB software with various structures and training algorithms. Design Expert software utilized to evaluate the studied parameters and produce mathematical models. The results showed that all studied parameters had a significant effect on soil physical properties including bulk density and cone index. The effects of the studied factors on bulk density were depth > moisture content > forward speed, > tire pressure (6% 4%, 2.4%, 2%, respectively). Whereas, the order of the investigated factors based on their effects on cone index were depth > moisture content > tire pressure > forward speed (6%, 4%, 2.4% and 2%, respectively). The best model for predicting the bulk density under different field conditions was the 4-8-1 architecture. Levenberg-Marquardt (Trainlm) produced outstanding performance with an MSE of 0.00226 and R2 of 0.986. Moreover, this performance was occurring at an epoch of 100. For predicting cone index, the best performance was achieved by Levenberg-Marquardt (trainlm) in 85 epochs, giving minimum MSE equal to 0.005112 and greater (R2) equal to 0.967 during the training process. Thus, the optimal structure for predicting cone index was 4-7-1.

Keywords

ANN Design-Expert software Bulk density Cone index

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How to Cite
Salim, A.-E. A. ., Almaliki, S. A. ., & Nedawi, D. R. . (2022). Smart Computing Techniques for Predicting Soil Compaction Criteria under Realistic Field Conditions. Basrah Journal of Agricultural Sciences, 35(1), 188–211. https://doi.org/10.37077/25200860.2022.35.1.15
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