EFFECT OF FILLER LOADING ON PHYSICAL AND FLEXURAL PROPERTIES OF RAPESEED STEM/PP COMPOSITES

The objective of the study is to develop a new filler for the production of natural filler thermoplastic composites using the waste rapeseed stalks. The long-term water absorption and thickness swelling behaviors and flexural properties of rapeseed filled polypropylene (PP) composites were investigated. Three different contents of filler were tested: 30, 45, and 60 wt%. Results of long-term hygroscopic tests indicated that by the increase in filler content from 30% to 60%, water diffusion absorption and thickness swelling rate parameter increased. A swelling model developed by Shi and Gardner can be used to quantify the swelling rate. The increasing of filler content reduced the flexural strength of the rapeseed/PP composites significantly. In contrast to the flexural strength, the flexural modulus improved with increasing the filler content. The flexural properties of these composites were decreased after the water uptake, due to the effect of the water molecules

A new design equation for prediction of ultimate bearing capacity of shallow foundation on granular soils

A major concern in design of structures is to provide precise estimations of ultimate bearing capacity of soil beneath their foundations. Direct determination of the bearing capacity of foundations requires performing expensive and time consuming laboratory tests. To cope with this issue, several numerical models have been presented by researchers. This paper presents the development of a new design equation for the prediction of the ultimate bearing capacity of shallow foundations on granular soils using linear genetic programming (LGP) methodology. The ultimate bearing capacity is formulated in terms of width of footing, footing geometry, depth of footing, unit weight of sand, and angle of shearing resistance. The LGP-based design equation is established using the results of several load tests on real sized foundations presented in the literature. Validity of the model is verified using a part of laboratory data that are not involved in the calibration process. The statistical measures of coefficient of determination, root mean squared error and mean absolute error are used to evaluate the performance of the model. Sensitivity and parametric analyses are conducted and discussed. The proposed model accurately characterizes the ultimate bearing capacity resulting in a very good prediction performance. The LGP model reaches a better prediction performance than the well-known prediction equations for the bearing capacity of shallow foundations