Estimating of the dry unit weight of compacted soils using general linear model and multi-layer perceptron neural networks

Compaction of earth fill is a very important stage of construction projects. Degree of compaction is defined by relative compaction. The relative compaction of a compacted earth fill is calculated by dividing the dry unit weight obtained from in situ tests by-into the maximum dry unit weight obtained from laboratory compaction tests. This rate represents compaction quality in the field. Numerous test methods such as sand cone, rubber balloon, nuclear measurements, etc., are available to determine the maximum dry unit weight of soils in the field. It is well known that these methods have disadvantages as well as advantages. This study focused on estimation of dry unit weight of soils depending on water contents and P-wave velocities of compacted soils. The multi-layer perceptron (MLP) neural networks and general linear model (GLM) were used in this study to estimate the dry unit weight of different types of soils. Results of the MLP neural networks were compared with the GLM results. Based on the comparisons, it is found that the MLP generally gives better dry unit weight estimates than the GLM technique. The laboratory experiments and modeling studies showed that a new method for compaction control can be developed depending on P-wave velocity to estimate of the dry unit weight of compacted soils.