Failure evolution and instability prediction of fiber-reinforced polymer-confined cement mortar specimens under axial compression.

In geotechnical engineering, a large number of pillars are often left in underground space to support the overlying strata and protect the surface environment. To enhance pillar stability and prevent instability, this study proposes an innovative technology for pillar reinforcement. Specifically, local confinement of the pillar is achieved through fiber-reinforced polymer (FRP) strips, resulting in the formation of a more stable composite structure. In order to validate the effectiveness of this structural approach, acoustic emission characteristics and surface strain field characteristics were monitored during failure processes, while mathematical models were employed to predict specimen instability. The test results revealed that increasing FRP strip confinement width led to heightened activity in acoustic emission events during failure processes, accompanied by a decrease in shear cracks but an increase in tensile cracks. Moreover, ductility was improved and deformation resistance capacity was enhanced within specimens. Notably, initial crack generation occurred within unconfined regions of specimens during failures; however, both length and width as well as overall numbers of cracks significantly decreased due to implementation of FRP strips. Consequently, specimen failure speed was slowed down accordingly. Finally, the instability of the partial FRP-confined cement mortar could be more accurately predicted based on the model of FRP-confined concrete. It was verified by the test results.

Study of the Relationship between Pulmonary Artery Pressure and Heart Valve Vibration Sound Based on Mock Loop.

The vibration of the heart valves' closure is an important component of the heart sound and contains important information about the mechanical activity of a heart. Stenosis of the distal pulmonary artery can lead to pulmonary hypertension (PH). Therefore, in this paper, the relationship between the vibration sound of heart valves and the pulmonary artery blood pressure was investigated to contribute to the noninvasive detection of PH. In this paper, a lumped parameter circuit platform of pulmonary circulation was first set to guide the establishment of a mock loop of circulation. By adjusting the distal vascular resistance of the pulmonary artery, six different pulmonary arterial pressure states were achieved. In the experiment, pulmonary artery blood pressure, right ventricular blood pressure, and the vibration sound of the pulmonary valve and tricuspid valve were measured synchronously. Features of the time domain and frequency domain of two valves' vibration sound were extracted. By conducting a significance analysis of the inter-group features, it was found that the amplitude, energy and frequency features of vibration sounds changed significantly. Finally, the continuously varied pulmonary arterial blood pressure and valves' vibration sound were obtained by continuously adjusting the resistance of the distal pulmonary artery. A backward propagation neural network and deep learning model were used, respectively, to estimate the features of pulmonary arterial blood pressure, pulmonary artery systolic blood pressure, the maximum rising rate of pulmonary artery blood pressure and the maximum falling rate of pulmonary artery blood pressure by the vibration sound of the pulmonary and tricuspid valves. The results showed that the pulmonary artery pressure parameters can be well estimated by valve vibration sounds.