Brunel University London
Browse
IMAGE
Figure 1.png (3.9 MB)
IMAGE
Figure 2.png (1.14 MB)
IMAGE
Figure 3.png (224.52 kB)
IMAGE
Figure 4.png (1.2 MB)
DATASET
Figure 4.xlsx (294.1 kB)
IMAGE
Figure 5.png (133.26 kB)
DATASET
Figure 5.xlsx (17.91 kB)
IMAGE
Figure 6a.png (90.63 kB)
IMAGE
Figure 6b.png (87.35 kB)
IMAGE
Figure 7.png (1004.55 kB)
IMAGE
Figure 8.png (537.88 kB)
IMAGE
Figure 9.png (2.59 MB)
IMAGE
Figure 10.png (1.13 MB)
DATASET
Figure 11.xlsx (23.09 kB)
IMAGE
Figure 11a.png (103.72 kB)
IMAGE
Figure 11b.png (106.81 kB)
IMAGE
Figure 11c.png (108.87 kB)
IMAGE
Figure 11d.png (98.36 kB)
IMAGE
Figure 12.png (569.35 kB)
ARCHIVE
Program for RFT Generation.zip (5.49 MB)
1/0
20 files

The random RFPA method for modeling rock failure

Version 2 2024-10-22, 09:58
Version 1 2024-07-17, 11:25
figure
posted on 2024-10-22, 09:58 authored by Bin GongBin Gong, Tao Zhao, Indrasenan Thusyanthan, Chun’an Tang

The random rock failure process analysis (RRFPA) method is proposed by combining RFPA and random field theory (RFT) to provide an effective approach for characterizing material spatial variability and uncertainties and improving the reliability of predictions in rock mechanics. In this method, RFT is utilized to overcome the shortcoming of the traditional RFPA in modeling rock heterogeneity and represent the variation of rock parameters as a function of relative distance. The influence of material intrinsic correlation on fracturing behavior and failure modes can be appropriately captured. Furthermore, 300 random field simulations were conducted to investigate the mechanical responses of rock under uniaxial compression. By incorporating a spectrum of material properties, the numerical outcomes delineate the upper and lower bounds of stress across all possible testing scenarios. Meanwhile, the obtained numerical stress-strain curves could effectively capture the experimental relationships. The histograms of uniaxial compressive strength and elastic modulus illustrate their adherence to normal distributions with the averages of 10.099 MPa and 1.818 GPa. The corresponding coefficient of variations are 0.450 and 0.038, respectively. The localized failure pattern tends to produce faster release of acoustic emission energy and smaller cumulative energy than the global failure pattern. Additionally, in terms of the uniaxial compressive strength, elastic modulus and critical axial strain, the maximum relative error of the proposed method is only 0.66%.

Funding

ENHANCE: Exploring how climate change affects coastal cliff recession: modelling and forecasting

UK Research and Innovation

Find out more...

Multiscale and probabilistic modelling of progressive slope failure

Engineering and Physical Sciences Research Council

Find out more...

History

Usage metrics

    Brunel University London

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC