scholarly journals Unloading-creep test and long-term strength study of different initial conditions of limestone

2021 ◽  
Vol 861 (2) ◽  
pp. 022068
Author(s):  
Xiaoliang Xu ◽  
Cheng Zhang ◽  
Sijie Yin ◽  
Tianzhu Huang ◽  
Lehua Wang
Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Rongbin Hou ◽  
Leige Xu ◽  
Duoxin Zhang ◽  
Yanke Shi ◽  
Luyang Shi

The creep behavior of rock has received much attention for analyzing the long-term response and stability of underground rock engineering structures. Numerous studies have been carried out on the creep properties of various rocks under pure compression conditions. However, little attention has been paid to the creep behavior of rocks in a combined compression-shear loading state. In this work, a novel combined compression and shear test (C-CAST) system was used to carry out inclined uniaxial compression tests and creep tests for various inclination angles (0°, 5°, 10°, and 15°). The results revealed that the peak strength of the coal decreased with the inclination angle of the specimen, which could provide the basis for setting up a creep test scheme. Multistage compression-shear creep tests were carried out on specimens with different inclination angles. Based on the analysis of the creep test data, the creep behavior of the coal in a combined compression-shear state was studied. It was found that the specimen inclination affected the time-dependent deformation, long-term strength (LTS), and time to failure. Compared with the specimen under pure compression, the inclination specimens tend to produce large shear strain with time, while they were more prone to shear failure. The reduction of the long-term strength was closely associated with the increase of the specimen inclination angle when the angle was more than 5°. Moreover, the ratio of the peak strength to the LTS was not affected by the specimen inclination, which is considered an inherent characteristic. We anticipate that the results obtained will assist in pillar design and long-term stability analysis.


2009 ◽  
Vol 58 (6) ◽  
pp. 525-532 ◽  
Author(s):  
Yoshitaka NARA ◽  
Masafumi TAKADA ◽  
Daisuke MORI ◽  
Hitoshi OWADA ◽  
Tetsuro YONEDA ◽  
...  

Aerospace ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 113
Author(s):  
Pedro Andrade ◽  
Catarina Silva ◽  
Bernardete Ribeiro ◽  
Bruno F. Santos

This paper presents a Reinforcement Learning (RL) approach to optimize the long-term scheduling of maintenance for an aircraft fleet. The problem considers fleet status, maintenance capacity, and other maintenance constraints to schedule hangar checks for a specified time horizon. The checks are scheduled within an interval, and the goal is to, schedule them as close as possible to their due date. In doing so, the number of checks is reduced, and the fleet availability increases. A Deep Q-learning algorithm is used to optimize the scheduling policy. The model is validated in a real scenario using maintenance data from 45 aircraft. The maintenance plan that is generated with our approach is compared with a previous study, which presented a Dynamic Programming (DP) based approach and airline estimations for the same period. The results show a reduction in the number of checks scheduled, which indicates the potential of RL in solving this problem. The adaptability of RL is also tested by introducing small disturbances in the initial conditions. After training the model with these simulated scenarios, the results show the robustness of the RL approach and its ability to generate efficient maintenance plans in only a few seconds.


2021 ◽  
Vol 144 ◽  
pp. 106424 ◽  
Author(s):  
Xueyu Pang ◽  
Jiankun Qin ◽  
Lijun Sun ◽  
Ge Zhang ◽  
Honglu Wang

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