Strength Data Research Articles

Interlaboratory study of flexural strength in additively manufactured alumina

AbstractWe report the results of an international interlaboratory study of the flexural strength of alumina fabricated across six laboratories using the vat photopolymerization ceramic additive manufacturing (AM) technology. The mechanical testing of all the specimens, 142 in total, was performed at the National Institute of Standards and Technology (NIST) according to the well‐established four‐point bending method standardized for traditional ceramics. Overall, the existing ASTM standard for the four‐point bend testing proved adequate for AM ceramics, with several modifications to the specimen requirements to account for the specifics of AM processes. Critical flaws that caused failure were identified in all but two cases using optical fractography augmented with the imaging of fracture surfaces in a scanning electron microscope. The flexural strength data, analyzed following the Weibull statistics, exhibited considerable variation among the specimen sets manufactured by different laboratories. This variability correlated with the presence of many distinct critical flaws. We identified seven types of flaws that accounted for the failure of 94% of specimens. The prevalent flaws depend on the printing direction relative to the specimen's geometry. We discuss the nature of these flaws and their relation to the printing and post‐processing conditions. Removal of several types of critical flaws will significantly improve mechanical properties of ceramic parts built using vat‐photopolymerization AM.

Predicting 5G throughput with BAMMO, a boosted additive model for data with missing observations

Abstract To deliver on the promise of 5G, network providers and application developers need to understand the factors impacting millimetre wave (mmWave) 5G throughput. Missing data, however, pose significant challenges for modelling throughput. Even in controlled settings, signal strength data may be only intermittently observed when a device’s connection is weak, leading to missing predictor values in model training. In addition, users may choose not to share their data once the model is deployed, meaning that key predictors may be missing when we want to predict throughput for their devices. To address these challenges, we introduce boosted additive model for data with missing observation (BAMMO), a novel additive model estimator obtained via a componentwise boosting algorithm that naturally incorporates data with missing values in model fitting. We validate BAMMO’s approach to handling missing data by comparing it with competing methods on real 5G network data with a high proportion of missing values and in simulations, finding that it delivers more accurate predictions and takes less time to compute. To identify key predictors of mmWave 5G throughput, we develop a novel extension of sparsity oriented importance learning for BAMMO, giving us a measure of variable importance based on the entire boosting solution path rather than a single selected model.

Indoor Scene Intelligent Identification System Based on Spatial Topological Relationship Constraints

Abstract. Indoor scene identification can provide prior environmental information for indoor positioning applications, achieving positioning enhancement. Due to the similarity of wireless signals in adjacent spaces, it can lead to incorrect identification in neighbouring scenario units. To address this problem, this paper first employs a Naive Bayes classifier to train and classify wireless signal strength data from different scenes, constructing a scene identification algorithm. Secondly, a topological relationship adjacency matrix and adjacency list tailored for indoor positioning are constructed, imposing spatial topological constraints to assist scene identification. Finally, an indoor scene intelligent identification system is developed and implemented. The experimental results indicate that the scene identification method based on spatial topological relationship constraints can effectively improve identification accuracy. The overall accuracy for 8 scenario units is 98.2%, and the identification accuracy is increased by 1.1% compared with the original method.

Bonding and Cleaning Effects of Irrigation Protocols Using Calcium Hypochlorite on the Post-space Radicular Dentin.

This study evaluated the effect of 2.5% sodium hypochlorite (SH) or calcium hypochlorite (CH) submitted to passive ultrasonic irrigation (PUI) or conventional irrigation (CI) on the incidence of residues and the bond strength of the cementation system to post-space dentin. Distilled water (DW) and 2.5% SH followed by 17% EDTA (SH-ED) were used as negative and positive control groups, respectively. The cervical, middle, and apical thirds of the post space were evaluated. One hundred and twenty bovine incisors were endodontically treated and post-space preparation was performed. The specimens were randomly assigned to six groups, according to the solution and irrigation method: DW-CI, SH-ED-CI-SH, SH-CI, SH-PUI, CH-CI, and CH-PUI. The incidence of residues (n=10) over the dentin was evaluated by scores using SEM images. Other specimens were irrigated as previously described and the post cementation was immediately performed using a conventional dual resin cement and a two-step etch-and-rinse adhesive system. Push-out and failure modes were performed for bonding evaluation. Kruskal-Wallis and Dunn test for incidence of residues data and one-way ANOVA and Tukey tests for bond strength data were used at a significance level of 5%. The protocols that showed a lower incidence of residues were: SH-ED-CI-SH, SH-PUI, and CH-PUI for the cervical third and SHED-CI-SH for the middle third (p<0.05). In the apical third, the protocols were similar to each other (p>0.05). Bond strength values were higher after irrigation with DW-CI for all thirds (p<0.05). 2.5% sodium or calcium hypochlorite negatively impacted the adhesion interface and exhibited a greater incidence of residues over the post-space radicular dentin.

Estimating Models and Evaluating their Efficiency under Multicollinearity in Multiple Linear Regression: A Comparative Study

Multicollinearity between independent variables occurs in multiple linear regression analysis characterized by high correlations, which complicates discerning individual variable effect, impacting model accuracy, stability, and interpretation of relationships. The research aims to diagnose the multicollinearity problem between explanatory variables in the linear regression model and identifying the variables causing this problem based on the variance inflation factor (VIF), then estimation and evaluate the performance of three alternative methods, which are Ridge regression, principal components analysis, and Feedforward Neural Networks (FFNN) models with one and two hidden layers and application of the models to compressive strength data for high-performance concrete. The results showed that Ridge regression and PCA effectively addressed multicollinearity problem, but the single hidden layer model FFNN showed superior predictive accuracy in estimating the compressive strength of high-performance concrete when comparing RMSE, MAE, and R2 values.

Predictions of mechanical properties of Fiber Reinforced Concrete using ensemble learning models

Fiber Reinforced Concrete (FRC) significantly improves the tensile, crack resistance, and durability of concrete by adding fiber materials, making it highly promising for applications in structural reinforcement, repair and new construction projects. The strength of FRC is a crucial indicator of its mechanical properties. This study utilizes ensemble learning techniques to predict and analyze the compressive strength, splitting tensile strength, and flexural strength of FRC. A total of 1589 sets of compressive strength data, 1137 sets of splitting tensile strength data, and 1061 sets of flexural strength data were collected from existing literature. Four ensemble models (GBDT, Xgboost, LightGBM, RF) were used to establish prediction models and analyze influencing factors for these three FRC mechanical properties, and the results were compared with traditional models. The results show that the ensemble models can effectively solve the FRC strength prediction problems compared to traditional models. The GBDT regression model performed best in predicting compressive and flexural strength, with R2 values of 0.939 and 0.867, respectively. The Xgboost regression model performed best in predicting splitting tensile strength, with an R2 value of 0.944. Further analysis using SHAP revealed that cement and water significantly impact FRC strength. The inclusion of fibers significantly affects the splitting tensile strength and flexural strength of FRC but has a minimal impact on compressive strength. The study not only demonstrates the feasibility of using machine learning algorithms for FRC strength prediction but also provides a fast and reliable means for FRC strength prediction, offering more reliable data support for the design and application of FRC materials, aiding practical engineering applications.

A fabrication of universal multifunctional coating with excellent adhesion on the surface of solid rocket motor insulation materials through a surface activation strategy based on multiple interaction forces

In this work, a novel universal multifunctional coating for insulation materials was prepared by using polyethyleneimine as the coating substrate, low-temperature molten glass powders and MXene as additives. Meanwhile, the universal coating was tightly adhered to the surface of the insulation materials through a surface activation strategy based on the synergistic effects of hydrogen bonding, electrostatic adsorption, and chemical bonding. The shear strength data showed that the adhesion between the universal coating and the three types of insulation material were higher than that of most commercial adhesives. In the case of EPDM, for example, the adhesion of the coating is 4.17 (epoxy glue), 1.04 (chemlok) and 1.67 (Neoprene) times that of commercial adhesives, respectively. In addition, the universal coating showed good heat resistance and thermal insulation, and formed a complete and dense char on the surface after exposure to flame. The maximum mass loss temperatures of the three coated insulation materials were 2.8, 6.5, and 9.3°C higher than the original samples, respectively. Thermal insulation tests showed that the coated insulation materials reduced the average top surface temperature by 29.68%, 40.84% and 29.06%, respectively, compared to the original samples. The results of anti-migration tests showed that the prepared universal coatings displayed generally superior anti-migration properties to the insulation materials, with equilibrium migration concentrations reduced by up to more than 80% compared to the original samples, which is better than the previous products of the same type. In addition, the application potential of the advanced multifunctional insulation materials obtained from the preparation was evaluated, resulting in an increase of more than 35% in peel strength and more than 20% in shear strength for all insulation materials with propellants. This work provides a simple and environmentally friendly generalized strategy to develop high performance insulation materials for aerospace fields.

Comparison of the Torsional Strength of Material Samples Made Using Selected Rapid Prototyping Methods

In recent years, there has been a marked increase in the use of rapid prototyping techniques that support industrial processes. The analysis of studies on strength tests of parts manufactured using additive techniques from model materials and hybrid composites usually includes tests of material and strength parameters based on uniaxial tension, bending, and compression tests. For elements manufactured from polymeric materials using traditional methods (e.g. injection moulding), data obtained from these tests are sufficient to determine the strength of the material and to apply the results in the product design process. In the case of the layered extrusion method, due to the model construction process, strength data based on standard samples do not have a direct transposition on the strength of machine parts operating under various load ranges, especially torsional loads. The publication presents the results of tests on the torsional strength of cylindrical samples with splines produced by layered extrusion, vacuum casting technology, and hybrid technology, which combines both technologies. The test samples were made using a Prusa i3 MK3 3D printer and the plasticised plastic modelling (fused filament fabrication, FFF) method, while PolyJet technology was used to create a reference model. The findings indicate that hybrid technology, in which a thin-walled mould is filled with a chemically hardened polymer, achieves the desired strength for the manufactured parts while maintaining dimensional accuracy and shape. The research results show that producing rotating elements using the hybrid technology reduces the sample core’s ability to undergo plastic deformation due to the adhesion between the materials and the occurrence of notches resulting from the model production process using the layered extrusion method.

Elemental Design of Alkali-Activated Materials with Solid Wastes Using Machine Learning.

Understanding the strength development of alkali-activated materials (AAMs) with fly ash (FA) and granulated blast furnace slag (GBFS) is crucial for designing high-performance AAMs. This study investigates the strength development mechanism of AAMs using machine learning. A total of 616 uniaxial compressive strength (UCS) data points from FA-GBFS-based AAM mixtures were collected from published literature to train four tree-based machine learning models. Among these models, Gradient Boosting Regression (GBR) demonstrated the highest prediction accuracy, with a correlation coefficient (R-value) of 0.970 and a root mean square error (RMSE) of 4.110 MPa on the test dataset. The SHapley Additive exPlanations (SHAP) analysis revealed that water content is the most influential variable in strength development, followed by curing periods. The study recommends a calcium-to-silicon ratio of around 1.3, a sodium-to-aluminum ratio slightly below 1, and a silicon-to-aluminum ratio slightly above 3 for optimal AAM performance. The proposed design model was validated through laboratory experiments with FA-GBFS-based AAM mixtures, confirming the model's reliability. This research provides novel insights into the strength development mechanism of AAMs and offers a practical guide for elemental design, potentially leading to more sustainable construction materials.

Research on the Stability and Response of Food Packaging Polystyrene Resin Materials to γ-ray Irradiation.

Radiation stability of food packaging materials is the key to ensuring food quality. In this study, 60Co γ-ray was selected to investigate the radiation resistance of food packaging polystyrene (PS) resin material, although the FTIR analysis showed that the intensity of several peaks decreased slightly. The gel permeation chromatography (GPC) results displayed that the value of peak molecular weight (Mp) of PS went from 2.68 × 105 g/mol down to 2.22 × 105 g/mol. Moreover, the residual mass (Res) of PS increased from 7.208 to 30.23%, indicating that the tendency of coking of PS was stronger after irradiation. In addition, the peak intensities of the three main pyrolysis products -CH2-, CH4, and CH2=CH2 increased by more than 30% compared to unirradiated PS, and a large number of them were detected in the whole pyrolysis process. Moreover, mechanical property analysis finds that both breaking strength and elongation data increased before irradiation dose of 50 kGy, then, decreased sharply with further increase of irradiation dose. The theoretical bond order analysis confirmed that the tertiary carbon bond attaching the benzene ring had the lowest bond energy. This study can give helpful guidance when using PS for food packing materials.

Higher Unilateral Muscle Imbalance at the Contralateral Knee 6 Months after Anterior Cruciate Ligament Reconstruction.

There are a considerable number of patients who, after anterior cruciate ligament reconstruction (ACL), suffer from relapses or reduced performance. Data collected from isokinetic dynamometry can provide useful information on the condition of the knee during rehabilitation. Seventy-one young sports patients with ACL reconstruction performed concentric (CON) isokinetic dynamometry (CON/CON 90°/s and CON/CON 240°/s) to assess the muscle strength of the quadriceps (Q) and hamstrings (H) in both knees at 6 months after ACL reconstruction. Limb symmetry index (LSI) and the H/Q ratio were calculated. Comparative statistical tests and multivariate regression were performed. At 90°/s, 57 patients (80.3%) had an LSI below 90% for quadriceps and 28 (60.6%) for hamstring. The number of imbalanced patients according to H/Q ratio was higher in the non-operated knee (n = 56, 78.9%) (p < 0.001). At 240°/s, 49 cases (69.1%) had LSI values above 90% for quadriceps and 37 (52.1%) for hamstrings. Regarding H/Q, imbalanced cases were higher in the non-operated limb (n = 60, 84.5%) (p < 0.001). Strength data at 6 months after ACL reconstruction and post-operative rehabilitation indicated greater unilateral (H/Q) muscle imbalance in the non-operated knee than in the operated knee. Most patients did not achieve the adequate LSI values.

Quantitative prediction of minced chicken gel strength under ultrasonic treatment by NIR spectroscopy coupled with nonlinear chemometric tools evaluated using APaRPs

Achieving the ideal gel strength is essential for desired texture in minced chicken products. This study developed a rapid, non-destructive method using near-infrared (NIR) spectroscopy and nonlinear chemometric modeling to predict minced chicken gel strength under ultrasonic treatment. Initially, minced chicken samples were subjected to high-intensity ultrasound for 0–50 min. This was followed by heat-induced gelation. Gel strength was conventionally measured, and NIR spectra were collected. Nonlinearity between gel strength and spectral data was confirmed using augmented partial residual plots (APaRPs). Subsequently, nonlinear support vector machine (SVM) and extreme learning machine (ELM) models were developed using full NIR spectra and variable selection methods, including uninformative variable elimination (UVE), competitive adaptive reweighting (CARS), and genetic algorithms (GA). GA proved most effective for enhancing model performance, achieving the highest predicted coefficient of determination (Rp2 = 0.8772) with the ELM model, demonstrating potential for rapid, non-destructive prediction of minced chicken gel strength quality.

Tongue strength and swallowing-related masseter activity and oropharyngeal timing across the lifespan

Purpose: This study examined lifespan changes in maximum tongue strength, swallowing time, and masseter activity during swallowing. It provides normative data with which to compare clinical assessments of orofacial myofunctional disorders (OMD) and oropharyngeal dysphagia (OPD). Method: 409 healthy participants without identified OMD or OPD (ages 5 – 79 years) provided instrumental measures of tongue strength and electromyographic measurements for oropharyngeal transit time and masseter activity during swallows of four boluses. Participants were placed in three broad age groups (5 – 15, 16 – 59, 60 – 79) for crosssectional analysis. Results: Differences were found between age groups for tongue strength, such that the youngest group had significantly lower anterior tongue strength than the other groups, and lower posterior tongue strength than the 16 – 59 age group. Anterior tongue strength was significantly greater for males than females; posterior tongue strength did not differ significantly between the sexes. The youngest group had longer oropharyngeal transit times than either of the two older groups for most boluses. Swallowing transit time decreased in duration across the age groups, from youngest to oldest, for the 2.5 cc pudding bolus. Both right and left masseters differed in activation among tasks and age groups. The oldest age group had consistently greater levels of activation of the right masseter, and all groups had greater activation for the cracker bolus. Spearman rank-order correlations largely confirmed the inferential statistics and provided evidence of a relationship between tongue weakness and increased oropharyngeal transit time. Conclusion: Maximum tongue pressure generation and oropharyngeal timing measures support a developmental hypothesis, with lower tongue strength and longer swallowing transit times for children ages 5 through 15. The smaller pudding bolus provided the greatest differentiation among the age groups, which may prove to be a functional indicator for clinical evaluation. These results are largely consistent with existing data for tongue strength and oropharyngeal swallowing transit times.

Experimental studies of the strength of bent reinforced concrete elements with crimp couplings of reinforcement

Introduction. As indicated in the first part of the article published in the Industrial and Civil Engineering journal (No. 12, 2023), the relevance of the work is due to the increased volume of use of longitudinal reinforcement couplings in reinforced concrete structures. The analysis of the domestic and foreign regulatory and technical base has shown that there is an opportunity to optimize coupling connections in terms of arrangement of their spacing. In that work, prototypes were described for conducting experimental studies to assess the effect of various design solutions for the spacing of coupling joints of stretched reinforcement on the strength of normal sections of bent elements. This article analyzes the experimental strength data obtained and offers appropriate recommendations for the design of bent reinforced concrete structures with coupling joints. Aim. Improvement of the urban planning system in terms of clarifying and supplementing of existing regulatory documents on the design of reinforced concrete structures. Materials and methods. The research was carried out taking into account modern requirements, as well as the analysis of the results of their own experiments. Results. The result of the work is an analysis of the experimental data obtained for bent reinforced concrete beams with coupling joints of stretched reinforcement. Based on the analysis, recommendations have been formed to optimize design solutions and clarify the requirements for the input control of couplings. Conclusions. Considering that, in general, according to the results of the work, a fairly good convergence of experimental and theoretical results has been obtained, it is possible to assume a reduced value of coupling spacing for certain types of load-bearing reinforced concrete structures. The need to tighten the input control of couplings in order to detect defects has also been identified. In addition, the need for further research on this issue was noted.

Normative and correlation data of hip and groin strength in professional male Australian football players

Normative and correlation data of hip and groin strength in professional male Australian football players

Polyaxial failure criteria for in situ stress analysis using borehole breakouts: Review of existing methods and development of an empirical alternative

Analysis of compressive wellbore failure, or breakouts, is one of the primary methods of constraining the maximum horizontal stress in deep boreholes. To estimate stress using the observation of breakouts, one needs to measure the breakout width from image logs and use a failure theory to predict the stress that led to the development of the measured breakout. Most commonly, Mohr–Coulomb failure criterion has been used which disregards the influence of intermediate stress on strength. Hence, various polyaxial criteria have been proposed to include this effect. Here, we first review some selected polyaxial criteria: Drucker–Prager, Mogi, Modified Wiebols–Cook, and Modified Lade, and we conclude that their application in breakout analysis may be cumbersome and often unreliable. One reason for these problems is that the criteria are defined using stress invariants, while the stress estimation is most easily performed and analyzed in the principal stress space. Therefore, an alternative is to define the polyaxial criterion as a simple relation between maximum and intermediate stresses. We propose to define such an empirical criterion as a second order polynomial which fits trends observed in polyaxial laboratory strength data. Such approach allows to limit strength overestimation, often associated with the use of previous polyaxial criteria, and to easily relate uncertainties in strength estimation to uncertainty in maximum horizontal stress prediction.

Effect of phosphoric acid etching and blasting with aluminum oxide on the enamel topography and adhesion of resin composite to intact or abraded enamel

ObjectivesTo evaluate the effects of the phosphoric acid (PA) etching, self-etching technique (SE) and blasting with Al2O3 particles (BL) on the bonding of a dental adhesive to intact (INT) or abraded (ABR) enamel. MethodsEnamel surfaces were treated as follows: 1- ABR-PA: INT was abraded with SiC paper and etched with PA (20 s) before Clearfil Universal Bond Quick adhesive application; 2- ABR-SE: ABR was SiC and adhesive applied in SE mode; 3- INT-PA: INT was etched with PA and adhesive applied; 4- INT-SE: the adhesive (SE mode) was applied to INT; 5- INT-BL: INT was BL and the adhesive was applied (SE mode), and 6- INT-BA: INT was BL, etched with PA and adhesive applied (SE mode). The enamel surface treated was examined with scanning electron microscopy (SEM) (n = 3) and Al2O3 particles were characterized using SEM and EDX. The enamel bond strength was measured by microtensile test (24 h and 1 year) (n = 8) and the morphology of enamel-adhesive interfaces were analyzed by SEM (n = 3). Bond strength data were analyzed by two-way ANOVA and Tukey’s test (α = 0.05). ResultsAl2O3 particles had an irregular shape, their length varied (50–20 µm) and the perimeter mean was 38.8 µm. The enamel morphology significantly influenced the enamel bond strength. ABR-PA, INT-BL, and INT-BA provided greater and stable enamel-dentin interaction and bond strength. SignificanceThe enamel morphology significantly influenced the enamel bond strength. Using the adhesive in etch-and-rinse mode, enamel must be abraded before etching and must be Al2O3-blasted when used in SE mode.

Clinical Outcome of Prefabricated Zirconia Crowns Cemented with Self-Adhesive Resin and Pure Glass Ionomer on Primary Teeth: A Retrospective Cohort Study.

Retentive strength data are critical to predicting the long-term clinical performance of zirconia crowns for primary teeth. Objectives: This research assessed the clinical outcome of prefabricated zirconia crowns (PZCs) cemented with either self-adhesive resin cement or pure glass ionomer cement in the primary teeth of children. Method: In the present research, a sample of 162 prefabricated zirconia crowns were collected through convenience sampling. A follow-up examination was conducted at 12 and 24 months to assess the clinical outcomes of PZCs post cementation. Results: Zirconia crowns cemented with self-adhesive resin showed high clinical outcomes at both 12 and 24 months, with 95.1% of crowns retained. In contrast, crowns cemented with glass ionomer cement had slightly lower clinical outcomes, with 91.4% retained at 12 months and 84.0% retained at 24 months, indicating a significant difference (p-value). Long-term follow-up is crucial for the optimal maintenance of crown stability. Conclusions: Self-adhesive resin cement is a viable option for cementing PZCs in pediatric dentistry, demonstrating satisfactory clinical performance over both 12 and 24 months. Future studies comparing different types of cement are recommended for further validation of these results.

Optimization of a coal mine roof characterization model using machine learning

Predicting areas of increased propensity for roof deformation is crucial for the proactive management of geotechnical risk in underground coal mines. Current practices rely largely on assessing rock mass strength or characterization indices in isolation. Validation of applied ground support systems typically, and in part, comprises a review of observed deformation and analysis of extensometer data from adjacent previously mined areas. This information is seldom routinely assessed relative to the rock mass strength or characterization data. Typically, roof deformation is a response to several combining factors such as mining-induced stress, roof lithology, rock mass strength, and roadway geometry. Therefore, it is optimal to develop an approach to integrate these factors to quantify their relative significance to causing roof deformation, with the goal of establishing a reliable quantitative model for predicting roof deformation.This paper provides a case study in machine learning using Artificial Neural Network (ANN) and geophysical log data information from an underground coal mine located in the Bowen Basin (Australia), whereby a suite of parameters is considered pertinent to excavation stability. This machine learning predictive model analyses key geotechnical parameters including field and mining-induced stress, rock mass strength, clay/shale content, and strata monitoring data to identify strata hazards. Finally, the classification model can indicate the risk of roof displacement and observed roof deformation as communicated through a contoured ‘geotechnical hazard map’. This hazard map can assist site engineers understand roof conditions in underground coal mining and thus put in place operational mitigation processes to reduce geotechnical risk on mining. This could potentially improve the identification of high-risk areas and inform future bolting requirements to maintain a safe working environment and minimise production interruptions.

Analysis of thermally-induced fracture of Callovo-Oxfordian claystone: From lab tests to field scale

Argillaceous rocks are a suitable host rock for the deep geological disposal of exothermic High-Level Radioactive Waste (HLW) and Spent Fuel (SF). Excess pore pressures develop in this type of rocks when subject to increased temperatures that, in some circumstances, may lead to the fracturing of the rock. The paper explores this phenomenon by means of coupled numerical analyses carried out within a fully coupled thermo-hydro-mechanical (THM) framework. The general THM formulation is described as well as the anisotropic elastic and anisotropic elastoviscoplastic constitutive laws employed. Thermal extension triaxial tests are simulated as a check on the performance of the numerical formulation and to provide calibration data for the tensile strength of the material. Selected results from a comprehensive set of three-dimensional analyses of a large-scale field heating test, designed to study the possibility of thermal-induced failure in the rock, are presented and discussed. The analyses reproduce satisfactorily the observed patterns of behaviour. The effects of the constitutive law, material parameters and the presence of the excavation damage zone (EDZ) around the main drift and around the heater boreholes are studied. In particular, their effects on the state of the stress in the heated area are examined in the context of the potential for thermal fracturing of the rock.