Thermodynamic properties of sodium hexatitanate (Na2Ti6O13) at high temperature (298.15–1573 K)

Yang, Lilian; Pei, Guishang; Hou, Youling; Xin, Yuntao; Lv, Xuewei

doi:10.1016/j.calphad.2021.102307

Cited by 2 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using the maximum shear stress in the surrounding rock, uniaxial compressive strength and six other indicators were combined with the principles of the principal component analysis and probabilistic neural network analysis of multiple rockburst cases to predict the level of rock bursts (WU et al, 2018). Considering the fuzzy nature of rockburst grading predictions, combined with an SOFM (self-organizing feature mapping) neural network, a rockburst prediction model was established (YANG et al, 2021). CNNs (convolutional neural networks) were combined with LSTM (long short-term memory) to first predict the future state of the rockburst indicator eigenvolume, and this was then combined with the particle swarm algorithm to optimize the generalized neural network to predict the future state of the rockburst level (LIU et al, 2021a).…”

Section: Introductionmentioning

confidence: 99%

A rockburst grade evaluation method based on principal component analysis and the catastrophe progression method

Lou

Li²

2023

Front. Earth Sci.

View full text Add to dashboard Cite

Rockburst disasters always have a great influence on engineering practice. In order to accurately predict the occurrence of rockburst hazards, this paper proposes a rockburst rating evaluation method based on principal component analysis (PCA) and the catastrophe progression method, taking into account several influencing factors. In this paper, 15 indicators, such as strength brittleness factor (R), stress factor (P), and rock quality index (RQD) (reflecting the strength and fragmentation degree of rock mass), were selected from seven samples and were analyzed and downscaled by principal component analysis. Combined with the catastrophe progression method, each layer index was dimensionless and normalized to determine the mutation level value of each layer. Based on the principle of complementarity or non-complementarity, to determine the total mutation level value, the layer index was used to divide the rockblast-level interval and predict the rockblast level. The results show that the method proposed in this paper can be used to not only distinguish the importance of each of the same level of indicators but also avoid the impact of superimposed factor correlations between the same level of indicators, making the results more objective. This paper presents accurate rock explosion assessment results and an actual engineering situation. The number of factors affecting the assessment of the rock explosion level provides new insights.

show abstract

Section: Introductionmentioning

confidence: 99%