An algorithm for daily prediction of soil precompression stress (Case study in Shahrekord)

To manage the traffic-induced soil compaction in a field, the applied stress on soil with machinery traffic needs to be controlled below the soil bearing capacity (i.e. precompression stress, σpc) to prevent increase in soil compaction. Precompresson stress is primarily a function of soil moisture and secondarily soil texture. This study aimed at developing an empirical -analytical algorithm for daily prediction of soil precompression stress in a selected field at Shahrekord University. Statistical analyses showed that using the meteorology variables of each year including the precipitation of the target day and its previous day, temperature, radiation and wind velocity, daily changes in soil moisture could be well predicted (R2 = 0.85, RMSE= 3.3%). To determine the relationship between the soil moisture and precompression stress, remolded soil samples were prepared at three bulk densities of 1.15, 1.22 and 1.3 Mg m-3 and four moisture levels of 10, 15, 20 and 25% and subjected to stepwise confined compressive stress. Precompression stress was estimated at the point of maximum curvature on the void ratio- log stress curves with fitting Gompertz function. In addition, the analytical model of Elbanna & Witney (1987) was tested for extending the results to different soil textures using an empirical relation between cone index and precompression stress. The results showed that the model predicts well the variations in precompression stress as affected by soil moisture. The algorithm developed in this study can be implemented in managing the machinery traffic and predicting the trafficable days of each year.