Effect of Sand Size on Mechanical Performance of Cement-Based Composite Containing PVA Fibers and Nano-SiO2.

Both finer sand and nanoparticles have a filler effect on mechanical performance of cement-based composite. In this paper, the influence of sand size in mechanical performance of cement-based composites, containing polyvinyl alcohol fiber (PVA) and nano-SiO2 (NS), was investigated. The studied mechanical performance, included compressive, flexural, tensile strength, and fracture toughness. A 0.9% volumetric percentage of PVA and a 2% NS mass content were used to make cement-based composites with a 0.38 w/b. Silica sand with four sand size ranges (380-830 µm, 212-380 µm, 120-212 µm and 75-120 µm) was adopted as fine aggregate. The 28-day curing was conducted for all specimens under 20 °C and 95% humidity. It is concluded that the finer sand decreased workability and mechanical strength of PVA-reinforced composites containing NS. However, this reduction was very limited for the sand particles less than 380 µm. The ultimate tensile stain, fracture toughness, and energy were decreased as sand size declined. In addition, the fracture performance of the composites was greatly dependent on fracture energy.

Compressive Strength Prediction of PVA Fiber-Reinforced Cementitious Composites Containing Nano-SiO2 Using BP Neural Network.

In this study, a method to optimize the mixing proportion of polyvinyl alcohol (PVA) fiber-reinforced cementitious composites and improve its compressive strength based on the Levenberg-Marquardt backpropagation (BP) neural network algorithm and genetic algorithm is proposed by adopting a three-layer neural network (TLNN) as a model and the genetic algorithm as an optimization tool. A TLNN was established to implement the complicated nonlinear relationship between the input (factors affecting the compressive strength of cementitious composite) and output (compressive strength). An orthogonal experiment was conducted to optimize the parameters of the BP neural network. Subsequently, the optimal BP neural network model was obtained. The genetic algorithm was used to obtain the optimum mix proportion of the cementitious composite. The optimization results were predicted by the trained neural network and verified. Mathematical calculations indicated that the BP neural network can precisely and practically demonstrate the nonlinear relationship between the cementitious composite and its mixture proportion and predict the compressive strength. The optimal mixing proportion of the PVA fiber-reinforced cementitious composites containing nano-SiO2 was obtained. The results indicate that the method used in this study can effectively predict and optimize the compressive strength of PVA fiber-reinforced cementitious composites containing nano-SiO2.

Durability of Steel Fiber-Reinforced Concrete Containing SiO2 Nano-Particles.

An experimental study was conducted to investigate the effect ofnano-SiO2 and steel fiber content on the durability of concrete. Five different dosages of nano-SiO2 particles and five volume dosages of steel fiber were used. The durability of concretes includes permeability resistance, cracking resistance, carbonation resistance, and freezing-thawing resistance, and these were evaluated by the water permeation depth, number of cracks, total cracking area per unit area of the specimens, carbonation depth of the specimens, and the relative dynamic elastic modulus of the specimens after freezing-thawing cycles, respectively. The results indicate that the addition of nano-SiO2 particles significantly improves the durability of concrete when the content of nano-SiO2 is limited within a certain range. With the increase of nano-SiO2 content, the durability of concrete first increases and then decreases. An excessive number of nano-SiO2 particles could have an adverse effect on the durability of the concrete. The addition of the correct amount of steel fibers improves the carbonation resistance of concrete containing nano-particles, but excessive steel fiber reduces the carbonation resistance. Moreover, the addition of steel fibers reduces the permeability resistance of concrete containing nano-particles. The incorporation of steel fiber enhanced the freezing-thawing resistance and cracking resistance of concrete containing nano-particles. With increasing steel fiber content, the freezing-thawing resistance of the concrete containing nano-particles increases, and the cracking resistance of the concrete decreases gradually.

Computed tomography perfusion and computed tomography angiography for prediction of clinical outcomes in ischemic stroke patients after thrombolysis.

Cerebral blood perfusion and cerebrovascular lesions are important factors that can affect the therapeutic efficacy of thrombolysis. At present, the majority of studies focus on assessing the accuracy of lesion location using imaging methods before treatment, with less attention to predictions of outcomes after thrombolysis. Thus, in the present study, we assessed the efficacy of combined computed tomography (CT) perfusion and CT angiography in predicting clinical outcomes after thrombolysis in ischemic stroke patients. The study included 52 patients who received both CT perfusion and CT angiography. Patients were grouped based on the following criteria to compare clinical outcomes: (1) thrombolytic and non-thrombolytic patients, (2) thrombolytic patients with CT angiography showing the presence or absence of a vascular stenosis, (3) thrombolytic patients with CT perfusion showing the presence or absence of hemodynamic mismatch, and (4) different CT angiography and CT perfusion results. Short-term outcome was assessed by the 24-hour National Institution of Health Stroke Scale score change. Long-term outcome was assessed by the 3-month modified Rankin Scale score. Of 52 ischemic stroke patients, 29 were treated with thrombolysis and exhibited improved short-term outcomes compared with those without thrombolysis treatment (23 patients). Patients with both vascular stenosis and blood flow mismatch (13 patients) exhibited the best short-term outcome, while there was no correlation of long-term outcome with CT angiography or CT perfusion findings. These data suggest that combined CT perfusion and CT angiography are useful for predicting short-term outcome, but not long-term outcome, after thrombolysis.