Probability Distributions of Asphalt Pavement Responses and Performance under Random Moving Loads and Pavement Temperature

Asphalt pavements are damaged by traffic load repetitions. Conventionally, the allowed number of load repetitions until pavement failure is calculated based on empirical transfer functions from deterministic pavement mechanical responses to performance. However, the mechanical responses and damage to the pavement are uncertain under a random realistic traffic load and pavement temperature. Therefore, the non-deterministic problem—that is, the probability distributions of asphalt pavement responses and performance under random moving loads and pavement temperatures—was investigated in this study. Random factors include the load pressure, vehicle wandering, speed, and temperature inside the asphalt layer. A combination of the response surface and first-order reliability methodologies was recommended to calculate the probability of mechanical responses at any point within the pavement, for reasons of computational efficiency. The accuracy of this method was verified by a Monte-Carlo simulation. Then, the effects of the mean values and standard deviations of the random factors on the probability distributions of the mechanical responses were discussed. Finally, probability distributions of pavement performance (i.e., probability density distributions of cumulative damage for fatigue failure and rutting after repeated random loads) were calculated using transfer functions and the probability distributions of the mechanical responses; thereby, the failure probability of the pavement after a given number of load repetitions was obtained. The results show that the previous deterministic analysis could not fully reflect the random characteristics of pavement mechanical responses under realistic random moving loads, and the mean values and standard deviations of the random factors have significant effects on the probability distributions of mechanical responses and performance. The failure probability of the pavement after a given number of load repetitions can be used as a guide to reliability-based pavement design. This study on the probability distributions of asphalt pavement responses and performance exhibits the potential to understand pavement behavior and could be beneficial as a complement during reliability-based pavement design

Tire–Pavement Interaction Simulation Based on Finite Element Model and Response Surface Methodology

Acquiring accurate tire–pavement interaction information is crucial for pavement mechanical analysis and pavement maintenance. This paper combines the tire finite element model (FEM) and response surface methodology (RSM) to obtain tire–pavement interaction information and to analyze the pavement structure response under different loading conditions. A set of experiments was initially designed through the Box–Behnken design (BBD) method to obtain input and output variables for RSM calibration. The resultant RSM was evaluated accurately using the analysis of variance (ANOVA) approach. Then, tire loading simulations were conducted under different magnitudes of static loading using the optimal parameter combination obtained from the RSM. The results show that the deviations between the simulations and the real test results were mostly below 5%, validating the effectiveness of the tire FEM. Additionally, three different dynamic conditions—including free rolling, full brake, and full traction—were simulated by altering the tire rolling angle and translational velocities. Finally, the pavement mechanical response under the three rolling conditions was analyzed based on the tire–pavement contact feature

Randomized control study of the use of faropenem for treating patients with pulmonary tuberculosis

ABSTRACT: Objectives: Faropenem has antituberculosis activity in vitro but its utility in treating patients with tuberculosis (TB) is unclear. Methods: We conducted an open-label, randomized trial in China, involving newly diagnosed, drug-susceptible pulmonary TB. The control group was treated with the standard 6-month regimen. The experimental group replaced ethambutol with faropenem for 2 months. The primary outcome was the treatment success rate after 6 months of treatment. Noninferiority was confirmed if the lower limit of a 95% one-sided confidence interval (CI) of the difference was greater than −10%. Results: A total of 227 patients eligible for the study were enrolled in the trial group and the control group in a ratio of 1:1. Baseline characteristics of participants were similar in both groups. In the modified intention-to-treat population, 88.18% of patients in the faropenem group achieved treatment success, and 85.98% of those in the control group were successfully treated, with a difference of 2.2% (95% CI, −6.73-11.13). In the per-protocol population, treatment success was 96.04% in the faropenem group and 95.83% in the control group, with a difference of 2.1% (95% CI, −5.31-5.72). The faropenem group showed noninferiority to the control group in the 6-month treatment success rates. The faropenem group had significantly fewer adverse events (P <0.01). Conclusions: Our study proved that oral faropenem regimen can be used for the treatment of TB, with fewer adverse events. (Chinese Clinical Trial Registry, ChiCTR1800015959)