Damage Characteristics of Thermally Deteriorated Carbonate Rocks: A Review

Waqas, Umer; Rashid, Hafiz Muhammad Awais; Ahmed, Muhammad Farooq; Rasool, Ali Murtaza; Al‐Atroush, Mohamed Ezzat

doi:10.3390/app12052752

Cited by 6 publications

(3 citation statements)

References 121 publications

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“…The intact rock samples may have hidden flaws, microcracks, internal defects, etc. 47 . The propagation of high-frequency stress waves through rock samples causes the plastic deformation of microcracks.…”

Section: Resultsmentioning

confidence: 99%

Optimization of neural-network model using a meta-heuristic algorithm for the estimation of dynamic Poisson’s ratio of selected rock types

Waqas

Ahmed

Rashid

et al. 2023

Sci Rep

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This research focuses on the predictive modeling between rocks' dynamic properties and the optimization of neural network models. For this purpose, the rocks' dynamic properties were measured in terms of quality factor (Q), resonance frequency (FR), acoustic impedance (Z), oscillation decay factor (α), and dynamic Poisson’s ratio (v). Rock samples were tested in both longitudinal and torsion modes. Their ratios were taken to reduce data variability and make them dimensionless for analysis. Results showed that with the increase in excitation frequencies, the stiffness of the rocks got increased because of the plastic deformation of pre-existing cracks and then started to decrease due to the development of new microcracks. After the evaluation of the rocks’ dynamic behavior, the v was estimated by the prediction modeling. Overall, 15 models were developed by using the backpropagation neural network algorithms including feed-forward, cascade-forward, and Elman. Among all models, the feed-forward model with 40 neurons was considered as best one due to its comparatively good performance in the learning and validation phases. The value of the coefficient of determination (R2 = 0.797) for the feed-forward model was found higher than the rest of the models. To further improve its quality, the model was optimized using the meta-heuristic algorithm (i.e. particle swarm optimizer). The optimizer ameliorated its R2 values from 0.797 to 0.954. The outcomes of this study exhibit the effective utilization of a meta-heuristic algorithm to improve model quality that can be used as a reference to solve several problems regarding data modeling, pattern recognition, data classification, etc.

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Section: Resultsmentioning

confidence: 99%

Optimization of neural-network model using a meta-heuristic algorithm for the estimation of dynamic Poisson’s ratio of selected rock types

Waqas

Ahmed

Rashid

et al. 2023

Sci Rep

Self Cite

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show abstract

“…The intact rock samples may have hidden aws, microcracks, internal defects, etc. (Waqas et al 2022). The propagation of high-frequency stress waves through rock samples causes the plastic deformation of microcracks.…”

Section: The Behavior Of Rocks Subjected To the Excitation Frequenciesmentioning

confidence: 99%

Optimization of Neural-Network Models Using a Meta-Heuristic Algorithm for the Estimation of Dynamic Poisson’s Ratio of Selected Rock Types

Waqas

Ahmed

Rashid

et al. 2022

Preprint

Self Cite

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This research investigates the dynamic behavior of rocks subjected to excitation frequencies at ambient condition. The dynamic response of rocks was evaluated in terms of quality factor (Q), resonance frequency (FR), acoustic impedance (Z), oscillation decay factor (α), and dynamic Poisson’s ratio (v). These parameters were measured in both longitudinal and torsion modes. Their ratios were taken to reduce data variability and make them dimensionless for analysis. Results showed that with the increase in excitation frequencies, the stiffness of the rocks got increased because of plastic deformation of pre-existing cracks and then started to decrease due to the development of new microcracks. After the evaluation of the behavior of the rocks, the v was estimated by the prediction modeling. Overall, 15 models were developed by using the backpropagation neural network algorithms including feed-forward, cascade-forward, and Elman. Among all models, the feed-forward model with 40 neurons was considered as best one due to its comparatively good performance in the learning and validation phases. The values of its Pearson correlation coefficient (R = 0.885) and coefficient of determination (R2 = 0.797) were estimated higher than the rest of the models. To further improve its quality, the model was optimized using the particle swarm optimization (PSO) algorithm. The optimizer ameliorated its R and R2 values from 0.885 to 0.980 and 0.797 to 0.954 respectively. The outcomes of this study exhibit the effective utilization of a meta-heuristic algorithm to improve model quality that can be used as a reference to solve several problems regarding data modeling, pattern recognition, data classification, etc.

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“…For example, in northeast China, many jointed rock mass projects have obvious freeze-thaw damage and shear failure effects under a freezethaw environment, as shown in Figure 1. So, it is of great significance to study the shear failure law and damage development mechanism of jointed rock masses under freeze-thaw cycles [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Study on the Damage Model of Non-Persistent Jointed Rock Mass under the Coupling of Freeze–Thaw and Shear

Lei¹,

Chen

et al. 2023

Materials

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Considering that a jointed rock mass in a cold area is often affected by periodic freeze–thaw cycles and shear failure, definitions for the mesoscopic and macroscopic damage to a jointed rock mass under the coupling of freeze–thaw and shear are proposed, and the damage mechanism is verified according to experimental results. The results show that: (1) the jointed rock specimens increase macro-joints and meso-defects, the mechanical properties deteriorate significantly under freeze–thaw cycles, and the damage degree becomes more and more significant with the increases in freeze–thaw cycles and joint persistency. (2) When the number of freeze–thaw cycles is constant, the total damage variable value gradually increases with the increase in joint persistency. The damage variable difference in specimens with different persistency is distinct, which is gradually reduced in the later cycles, indicating a weakening influence of persistency on the total damage variable. (3) The shear resistance of non-persistent jointed rock mass in a cold area is determined by the coupling effect of meso-damage and frost heaving macro-damage. The coupling damage variable can accurately describe the damage variation law of jointed rock mass under freeze–thaw cycles and shear load.

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Damage Characteristics of Thermally Deteriorated Carbonate Rocks: A Review

Cited by 6 publications

References 121 publications

Optimization of neural-network model using a meta-heuristic algorithm for the estimation of dynamic Poisson’s ratio of selected rock types

Optimization of neural-network model using a meta-heuristic algorithm for the estimation of dynamic Poisson’s ratio of selected rock types

Optimization of Neural-Network Models Using a Meta-Heuristic Algorithm for the Estimation of Dynamic Poisson’s Ratio of Selected Rock Types

Study on the Damage Model of Non-Persistent Jointed Rock Mass under the Coupling of Freeze–Thaw and Shear

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