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Understanding the quality of intact rock is one of the most important parts of any engineering projects in the field of rock mechanics. The expression of correlations between the engineering properties of intact rock has always been the scope of experimental research, driven by the need to depict the actual behaviour of rock and to calculate most accurately the design parameters. To determine the behaviour of intact rock, the value of important mechanical parameters such as Young’s modulus ( ), Poisson’s ratio (ν) and the strength of rock (σ ) was calculated. Recently, for modelling the behaviour of intact rock, the crack initiation stress (σ ) is another important parameter, together with the strain (σ). The ratio of Young’s modulus and the strength of rock is the modulus ratio ( ), which can be used for calculations. These parameters are extensively used in rock engineering when the deformation of different structural elements of underground storage, caverns, tunnels or mining opening must be computed. The objective of this paper is to investigate the relationship between these parameters for Hungarian granitic rock samples. To achieve this goal, the modulus ratio ( = /σ ) of 50 granitic rocks collected from Bátaapáti radioactive waste repository was examined. Fifty high-precision uniaxial compressive tests were conducted on strong (σ >100 MPa) rock samples, exhibiting the wide range of elastic modulus ( = 57.425–88.937 GPa), uniaxial compressive strength (σ = 133.34–213.04 MPa) and Poisson’s ratio (ν = 0.18–0.32). The observed value ( = 326–597) and mean value of = 439.4 are compared with the results of similar previous researches. Moreover, the statistical analysis for all studied rocks was performed and the relationshipbetween and other mechanical parameters such as maximum axial strain for studied rocks was discussed.

The maximum strain failure criterion is unified with the maximum stress failure criterion, after exploring the implications of two considerations responsible for this: (1) the failure strains for the direct strain components employed in the maximum strain criterion are all defined under uniaxial stress states, not uniaxial strain states, and (2) the contributions to the strain in a direction as a result of the Poisson effect do not contribute to the failure of the material in that direction. Incorporating these considerations into the maximum strain criterion, the maximum stress criterion is reproduced. For 3D stress/strain state applications primarily, the unified maximum stress/strain criterion is then subjected to further rationalization in the context of transversely isotropic materials by eliminating the treatments that undermine the objectivity of the failure criterion. The criterion is then applied based on the maximum and minimum direct stresses, the maximum transverse shear stress and the maximum longitudinal shear stress as the invariants of the stress state, instead of the conventional stress components directly.

In engineering practice, layered rock masses often display obvious anisotropy while deforming and failing, and the failure mode directly impacts the engineering construction stability. In this study, the fracture failure load, fracture toughness, crack deflection angle, and failure mode of a layered rock mass under different fracture modes were analyzed by utilizing improved asymmetric semi-circular disc specimens. According to the constitutive model of transversely isotropic materials, the maximum tensile stress (MTS), maximum energy release rate (MERR), and maximum strain energy density (MSED) calculation formulas were modified, and the calculation formulas of the three prediction criteria under anisotropic materials were derived. The calculation results were compared with the experimental results. The results show that the fracture toughness and crack deflection angle were significantly affected by the weak bedding plane. As a result of applying the MTS criterion, the results are closer to the experimental results, providing a solid foundation for engineering deformation, failure, and fracture analyses.

In civil engineering, one of the main problems is the stability of structures. Blast vibrations can be one of several elements influencing the stability of these structures. In order to prevent any effects on the structures, the maximum suggested values of the peak particle velocity and frequency from the blast are generally determined using the existing appropriate government rules and industry standards as reference. In this paper, the effect of these vibration on the site response has been investigated. Parameters such as peak ground acceleration, Pseudo spectral acceleration and maximum stress and strain has been compared for two different soil models. The results indicate that the soft soil has a significant impact and amplifies the input parameters considerably.

This paper experimentally and analytically investigated the damage and seismic behavior of concrete walls reinforced by low-bond ultra-high-strength (LBUHS) bars. To this end, four half-scale rectangular concrete walls were fabricated and tested under reversed cyclic loading and constant axial compression. The test variables were the shear span ratio and the axial load ratio. Based on the test results, the propagation of cracks on the wall surface, the maximum strain capacity of concrete, the hysteresis loops and envelope curves, the residual drifts, and the strain distributions of LBUHS rebars were presented and discussed. The experimental results showed that all the test walls could exhibit drift-hardening capability until at least a 2.0% drift ratio if LBUHS rebars were anchored by nuts at their ends. The test results also indicated that the maximum strain capacity of concrete was above 0.86%, much larger than the currently recommended 0.4%. After unloading from the transient drift ratios of 2.0% and 2.5% for the walls with shear span ratios of 1.5 and 2.0, respectively, the measured residual drift ratios were controlled below 0.4%, which is less than the critical drift ratio (0.5%) having 98% repairable probability recommended in the FEMA document (P-58) for general concrete structures. Furthermore, a numerical method was presented to evaluate the cyclic response of the test walls, and a comparison between the experimental and the calculated results verified the reliability and accuracy of the proposed numerical method.

The damage that occurs around the tire bead region is one of the critical failure forms of a tire. Generally, the prediction of tire durability is carried out by the experimental method. However, it takes a lot of money and time to conduct experiments. Therefore, to determine the fatigue life of radial tire bead, a reasonable prediction method is proposed in this paper. Fatigue testing of bead rubber compounds to determine the ΔSED-number of the cycle (Nf) was applied. The maximum strain energy density range (ΔSEDmax) of several bead compounds was obtained by steady-state rolling analysis with a finite element method. This quantity is then inserted into a fatigue life equation to estimate the fatigue life. The experimental results of the 175/75R14 tire were compared with the estimated value, which showed a good correlation. It is concluded that the method can be effectively applied to the fatigue life prediction of the tire bead.

This paper studies propagation criteria in three-dimensional fracture mechanics within the extended finite element framework (XFEM). The crack in this paper is described by a hybrid explicit–implicit approach as proposed in Fries and Baydoun (Int J Numer Methods Eng, 2011 ). In this approach, the crack update is realized based on an explicit crack surface mesh which allows an investigation of different propagation criteria. In contrast, for the computation of the displacements, stresses and strains by means of the XFEM, an implicit description by level set functions is employed. The maximum circumferential stress criterion, the maximum strain energy release rate criterion, the minimal strain energy density criterion and the material forces criterion are realized. The propagation paths from different criteria are studied and compared for asymmetric bending, torsion, and combined bending and torsion test cases. It is found that the maximum strain energy release rate and maximum circumferential stress criterion show the most favorable results.

Adhesive bonds, which are of critical importance in modern engineering structures, can be damaged and develop cracks under repeated loads (fatigue) over time. Accurately predicting how fast these cracks will grow and how the material expends energy during this process is of vital importance for the safety and durability of structures, but traditional engineering methods are often insufficient for modeling these complex behaviors. This study utilized experimental data from adhesive bond fatigue crack growth tests on double cantilever beam (DCB) specimens. Three key parameters characterizing the damage behavior of adhesive bonds, fatigue crack growth rate (da/dN), total energy dissipation (Total_(E)nergy), and maximum strain energy release rate (Gmax) were predicted using a comprehensive suite of sixteen (16) different Machine Learning (ML) regression models. These models included Linear Regression (lm), Ridge Regression (ridge), Lasso Regression (lasso), Elastic Net Regression (glmnet), Random Forest (rf), Support Vector Machine Linear (svmLinear) and Support Vector Machine Radial (svmRadial), Gradient Boosting Machine (gbm), Decision Tree (rpart), eXtreme Gradient Boosting Machine (xgbTree), K-Nearest Neighbor (knn), Partial Least Squares (pls), Generalized Additive Model (gam), Bayesian Regularized Neural Networks (brnn), Gaussian Process Regression (gpr), and Quantile Regression Neural Networks (qrnn). Model performance was evaluated using standard metrics, influential experimental factors were identified via the Boruta algorithm, and relationships were explained using model interpretation techniques (e.g., Linear Regression equations and Decision Tree structures). ML models demonstrated high accuracy in predicting these critical parameters from experimental data, achieving high R.sup.2 values and low error metrics across the test sets. Different ML models were observed to excel for different prediction tasks; for instance, linear models often performed well for total energy prediction, while tree-based and more complex non-linear methods (like Gaussian Process Regression) frequently showed superior performance for fatigue crack growth rate and Gmax predictions. Important engineering insights were also gained regarding the influence of experimental conditions on predictions. This study demonstrates that ML is a powerful and promising tool for understanding the behavior of complex materials like adhesive bonds and for developing safer, more durable engineering designs. To support these analyses and make them available to the research community, an open-source R-Shiny code designed as a user-friendly data analysis and regression dashboard for the \"Damage Tolerance of Adhesive Bonds Dataset\" was developed. Sharing these codes aims to provide practical tools for the field and facilitate further research.

Brain deformation caused by a head impact leads to traumatic brain injury (TBI). The maximum principal strain (MPS) was used to measure the extent of brain deformation and predict injury, and the recent evidence has indicated that incorporating the maximum principal strain rate (MPSR) and the product of MPS and MPSR, denoted as MPS × SR, enhances the accuracy of TBI prediction. However, ambiguities have arisen about the calculation of MPSR. Two schemes have been utilized: one is to use the time derivative of MPS (MPSR1), and another is to use the first eigenvalue of the strain rate tensor (MPSR2). Both MPSR1 and MPSR2 have been applied in previous studies to predict TBI. To quantify the discrepancies between these two methodologies, we compared them across eight in-vivo and one in-silico head impact datasets and found that 95MPSR1 was slightly larger than 95MPSR2 and 95MPS × SR1 was 4.85 % larger than 95MPS × SR2 in average. Across every element in all head impacts, the average MPSR1 was 12.73 % smaller than MPSR2, and MPS × SR1 was 11.95 % smaller than MPS × SR2. Furthermore, logistic regression models were trained to predict TBI using MPSR (or MPS × SR), and no significant difference was observed in the predictability. The consequence of misuse of MPSR and MPS × SR thresholds (i.e. compare threshold of 95MPSR1 with value from 95MPSR2 to determine if the impact is injurious) was investigated, and the resulting false rates were found to be around 1 %. The evidence suggested that these two methodologies were not significantly different in detecting TBI.