A deep CNN-based constitutive model for describing of statics characteristics of rock materials

Wu, Luyuan; Ma, Dan; Wang, Zifa; Zhang, Jianwei; Zhang, Boyang; Li, Jianhui; Liao, Jian; Tong, Jingbo

doi:10.1016/j.engfracmech.2023.109054

Cited by 18 publications

(8 citation statements)

References 54 publications

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“…The subsequent release of stored strain energy in loaded materials, in the form of elastic waves, is referred to as acoustic emission (AE) [14,15]. By analyzing the temporal variations in AE characteristic parameters, such as ring count, energy, RA/AF value, frequency, among others, the microcracking evolution during the failure process of coal can be monitored, thereby providing an early warning for material instability precursors [16]. Li et al conducted an analysis on the fractal characteristics of AE signals in coal samples subjected to triaxial multi-stage loading.…”

Section: Introductionmentioning

confidence: 99%

Mechanical Behaviors and Precursory Characteristics of Coal-Burst in Deep Coal Mining for Safety-Sustainable Operations: Insights from Experimental Analysis

Wang,

Zhou

et al. 2024

Sustainability

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Coalburst, a frequent and severe dynamic disaster, poses significant challenges to the safety and sustainable development of coal mines during deep excavation. To investigate the mechanical behaviors and precursory characteristics of coalburst subjected to in situ stress conditions, multiaxial cyclic loading experiments were conducted on cubic coal specimens, and the effects of different confining pressures on the mechanical parameters and energy evolution were analyzed. Acoustic emission (AE) technology was utilized to study the accumulation process of stress-induced damage and identify the source modes of microcracks. Then, nonlinear fractal theory and critical slowing theory were used to investigate the time-varying precursory characteristics of catastrophic failure in coalburst. The results show that as the confining pressure increases, the coal samples exhibit higher levels of elastic strain energy and dissipative energy, indicating an enhancement of plasticity. The AE count and accumulated energy show a strong correlation with cyclic loads. With an increasing number of cycles, the AE Felicity ratio gradually decreases, indicating a progressive increase in irreversible damage. Shear-mode microcracks also become more prominent with applied stress and confining pressures, as supported by varying AF/RA values of AE signals. The AE signals also follow the Hurst statistical law, and increasing applied stress and confining pressure strengthen this statistical pattern with a higher Hurst index. Throughout the cyclic loading process, certain AE varying trends were observed: the autocorrelation coefficient increased, the fractal dimension gradually decreased, and the variance suddenly increased. These trends serve as early, middle, and short–imminent warning signals, respectively, for the catastrophic failure of the loaded coal sample. These research findings contribute to a deeper understanding of coal failure evolution and provide a basis for early detection and warning of coalburst disasters, which are also essential for promoting the safe and sustainable development of deep coal mining operations.

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Section: Introductionmentioning

confidence: 99%

Mechanical Behaviors and Precursory Characteristics of Coal-Burst in Deep Coal Mining for Safety-Sustainable Operations: Insights from Experimental Analysis

Wang,

Zhou

et al. 2024

Sustainability

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“…Other researchers have also investigated methods for hydrogen production from energy sources [ 3 , 4 , 5 , 6 , 7 ]. Hydrogen production is best carried out with water when using sustainable sources on a large scale [ 8 ]. Due to its renewable, safe, and clean nature, solar energy is considered one of the best options.…”

Section: Introductionmentioning

confidence: 99%

Thermodynamics Investigation and Artificial Neural Network Prediction of Energy, Exergy, and Hydrogen Production from a Solar Thermochemical Plant Using a Polymer Membrane Electrolyzer

Jery

Salman

Al-Khafaji³

et al. 2023

Molecules

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Hydrogen production using polymer membrane electrolyzers is an effective and valuable way of generating an environmentally friendly energy source. Hydrogen and oxygen generated by electrolyzers can power drone fuel cells. The thermodynamic analysis of polymer membrane electrolyzers to identify key losses and optimize their performance is fundamental and necessary. In this article, the process of the electrolysis of water by a polymer membrane electrolyzer in combination with a concentrated solar system in order to generate power and hydrogen was studied, and the effect of radiation intensity, current density, and other functional variables on the hydrogen production was investigated. It was shown that with an increasing current density, the voltage generation of the electrolyzer increased, and the energy efficiency and exergy of the electrolyzer decreased. Additionally, as the temperature rose, the pressure dropped, the thickness of the Nafion membrane increased, the voltage decreased, and the electrolyzer performed better. By increasing the intensity of the incoming radiation from 125 W/m2to 320 W/m2, the hydrogen production increased by 111%, and the energy efficiency and exergy of the electrolyzer both decreased by 14% due to the higher ratio of input electric current to output hydrogen. Finally, machine-learning-based predictions were conducted to forecast the energy efficiency, exergy efficiency, voltage, and hydrogen production rate in different scenarios. The results proved to be very accurate compared to the analytical results. Hyperparameter tuning was utilized to adjust the model parameters, and the models’ results showed an MAE lower than 1.98% and an R2 higher than 0.98.

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“…Among other benefits of active control systems are applicability to multi-risk situations (such as wind and earthquakes), relative insensitivity to site circumstances and ground motions, and selectivity of control objectives like safety during severe dynamic loading and human comfort during non-critical times. Wu et al [34] studied the deformation and failure of structures using convolutional neural networks.…”

Section: Introductionmentioning

confidence: 99%

Building's Controlled Seismic Isolation by Using Upper Horizontal Dampers and Stiff Core

Jouneghani

Hosseini

Rohanimanesh

et al. 2023

Turkish Journal of Civil Engineering

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The fundamental period of the seismically isolated buildings may be close to that of the long period pulses of near-filed earthquakes, leading to very large lateral displacements in isolators, which in turn can considerably reduce the stability of isolators, increase the chance of collision of the isolated buildings to adjacent buildings, or even result in overturning of the isolated buildings. Therefore, it is important to control these types of buildings and reduce the amount of lateral displacement in their isolating system. In this study, by conducting a series of time history analyses for a set of five multi-story steel buildings with various numbers of stories from 3 to 14, each having a very stiff core structure and a set of crosswise viscous dampers, connecting the building structure to the core structure at the lowest and the top floors, as well as the same structures without the core structure and dampers, the effect of stiff core and dampers in reducing the lateral displacement at isolators has been shown. Results indicate that by the proposed technique, the lateral displacement of the base isolation system is significantly decreased particularly for low-rise buildings.

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A deep CNN-based constitutive model for describing of statics characteristics of rock materials

Cited by 18 publications

References 54 publications

Mechanical Behaviors and Precursory Characteristics of Coal-Burst in Deep Coal Mining for Safety-Sustainable Operations: Insights from Experimental Analysis

Mechanical Behaviors and Precursory Characteristics of Coal-Burst in Deep Coal Mining for Safety-Sustainable Operations: Insights from Experimental Analysis

Thermodynamics Investigation and Artificial Neural Network Prediction of Energy, Exergy, and Hydrogen Production from a Solar Thermochemical Plant Using a Polymer Membrane Electrolyzer

Building's Controlled Seismic Isolation by Using Upper Horizontal Dampers and Stiff Core

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