Simulating reinforced concrete members. Part 1: partial interaction properties

Reinforced concrete members are extremely complex under loading because of localised deformations in the concrete (cracks, sliding planes) and between the reinforcement and concrete (slip). An ideal model for simulating behaviour of reinforced concrete members should incorporate both global behaviour and the localised behaviours that are seen and measured in practice; these localised behaviours directly affect the global behaviour. Most commonly used models do not directly simulate these localised behaviours that can be seen or measured in real members; instead, they overcome these limitations by using empirically or semi-empirically derived strain-based pseudo properties such as the use of effective flexural rigidities for deflection; plastic hinge lengths for strength and ductility; and energy-based approaches for both concrete softening in compression and concrete softening after tensile cracking to allow for tension stiffening. Most reinforced concrete member experimental testing is associated with deriving these pseudo properties for use in design and analysis, and this component of development is thus costly. The aim of the present research is to reduce this cost substantially. In this paper, localised material behaviours and the mechanisms they induce are described. Their incorporation into reinforced concrete member behaviour without the need for empirically derived pseudo properties is described in a companion paper.

Effect of poly ethylene glycol on the mechanical and thermal properties of bioactive Poly (ε–caprolactone) melt extrudates for pharmaceutical applications

This paper investigates the effects of polyethylene glycol (PEG), on the mechanical and thermal properties of nalidixic acid/ploy ε-caprolactone (NA)/PCL blends prepared by hot melt extrusion. The blends were characterized by tensile and flexural analysis, dynamic mechanical analysis, differential scanning calorimetry, thermogravimetric analysis and X-ray diffraction. Experimental data indicated that the addition of NA caused loss of the tensile strength and toughness of PCL. Thermal analysis of the PCL showed that on addition of the thermally unstable NA, thermal degradation occurred early and was autocatalytic. However, the NA did benefit from the heat shielding provided by the PCL matrix resulting in more thermally stable NA particles. Results show that loading PEG in the PCL had a detrimental effect on the tensile strength and toughness of the blends, reducing them by 20-40%. The partial miscibility of the PCL-PEG system, causes an increase in Tg. While increases in the crystallinity is attributed to the plasticisation effect of PEG and the nucleation effect of NA. The average crystal size increased by 8% upon PEG addition.

Melt processing and properties of linear low density polyethylene-graphene nanoplatelet composites

Composites of Linear Low Density Polyethylene (LLDPE) and Graphene Nanoplatelets (GNPs) were processed using a twin screw extruder under different extrusion conditions. The effects of screw speed, feeder speed and GNP content on the electrical, thermal and mechanical properties of composites were investigated. The inclusion of GNPs in the matrix improved the thermal stability and conductivity by 2.7% and 43%, respectively. The electrical conductivity improved from 10−11 to 10−5 S/m at 150 rpm due to the high thermal stability of the GNPs and the formation of phonon and charge carrier networks in the polymer matrix. Higher extruder speeds result in a better distribution of the GNPs in the matrix and a significant increase in thermal stability and thermal conductivity. However, this effect is not significant for the electrical conductivity and tensile strength. The addition of GNPs increased the viscosity of the polymer, which will lead to higher processing power requirements. Increasing the extruder speed led to a reduction in viscosity, which is due to thermal degradation and/or chain scission. Thus, while high speeds result in better dispersions, the speed needs to be optimized to prevent detrimental impacts on the properties.

Mechanical properties and cellular response of novel electrospun nanofibers for ligament tissue engineering: Effects of orientation and geometry

Electrospun nanofibers are a promising material for ligamentous tissue engineering, however weak mechanical properties of fibers to date have limited their clinical usage. The goal of this work was to modify electrospun nanofibers to create a robust structure that mimics the complex hierarchy of native tendons and ligaments. The scaffolds that were fabricated in this study consisted of either random or aligned nanofibers in flat sheets or rolled nanofiber bundles that mimic the size scale of fascicle units in primarily tensile load bearing soft musculoskeletal tissues. Altering nanofiber orientation and geometry significantly affected mechanical properties; most notably aligned nanofiber sheets had the greatest modulus; 125% higher than that of random nanofiber sheets; and 45% higher than aligned nanofiber bundles. Modifying aligned nanofiber sheets to form aligned nanofiber bundles also resulted in approximately 107% higher yield stresses and 140% higher yield strains. The mechanical properties of aligned nanofiber bundles were in the range of the mechanical properties of the native ACL: modulus=158±32MPa, yield stress=57±23MPa and yield strain=0.38±0.08. Adipose derived stem cells cultured on all surfaces remained viable and proliferated extensively over a 7 day culture period and cells elongated on nanofiber bundles. The results of the study suggest that aligned nanofiber bundles may be useful for ligament and tendon tissue engineering based on their mechanical properties and ability to support cell adhesion, proliferation, and elongation.

Optimization and Prediction of Mechanical and Thermal Properties of Graphene/LLDPE Nanocomposites by Using Artificial Neural Networks

The focus of this work is to develop the knowledge of prediction of the physical and chemical properties of processed linear low density polyethylene (LLDPE)/graphene nanoplatelets composites. Composites made from LLDPE reinforced with 1, 2, 4, 6, 8, and 10 wt% grade C graphene nanoplatelets (C-GNP) were processed in a twin screw extruder with three different screw speeds and feeder speeds (50, 100, and 150 rpm). These applied conditions are used to optimize the following properties: thermal conductivity, crystallization temperature, degradation temperature, and tensile strength while prediction of these properties was done through artificial neural network (ANN). The three first properties increased with increase in both screw speed and C-GNP content. The tensile strength reached a maximum value at 4 wt% C-GNP and a speed of 150 rpm as this represented the optimum condition for the stress transfer through the amorphous chains of the matrix to the C-GNP. ANN can be confidently used as a tool to predict the above material properties before investing in development programs and actual manufacturing, thus significantly saving money, time, and effort.