Littérature scientifique sur le sujet « Joint Volumetric Count »

Block volumes are generally estimated by analyzing the discontinuity spacing measurements obtained either from the scan lines placed over the rock exposures or the borehole cores. Discontinuity spacing measurements made at the Mesozoic limestone quarries in Karaburun Peninsula were used to estimate the average block volumes that could be produced from them using the suggested methods in the literature. The Block Quality Designation (BQD) ratio method proposed by the authors has been found to have given in the same order of the rock block volume to the volumetric joint count (Jv) method. Moreover, dimensions of the 2378 blocks produced between the years of 2009 and 2011 in the working quarries have been recorded. Assuming, that each block surfaces is a discontinuity, the mean block volume (Vb), the mean volumetric joint count (Jvb) and the mean block shape factor of the blocks are determined and compared with the estimated mean in situ block volumes (Vin) and volumetric joint count (Jvi) values estimated from the in situ discontinuity measurements. The established relations are presented as a chart to be used in practice for estimating the mean volume of blocks that can be obtained from a quarry site by analyzing the rock mass discontinuity spacing measurements.

Volumetric joint countis an important parameter to evaluate the development of fractures. It is a fundamental representative for the strength and permeability of rock masses. However, cannot be directly measured in field. In this study, an indirect method is applied for its estimation. The main procedures are as follows: firstly, the volumetric joint frequencyis assumed for th fracture set, and then a series of 3D stochastic discrete fracture networks (DFNs) are generated using the Monte Carlo method according to; secondly, a survey line is drawn perpendicular to the fracture set in the each fracture network generated, the fracture spacing is measured along the survey line, then the average fracture spacing and its variance coefficient are calculated from all the DFNs; thirdly, by repeating the above two steps for differentassumed, the relevant average fracture spacing and its variance coefficient are obtained, and two relation curves are built up between and the average fracture spacing (or its variance coefficient); fourth, the exactis estimated through this relation curve between and the average fracture spacing according to the exact fracture spacing measured in situ; finally,is calculated by summing the exactof all fracture sets up. In this study, this indirect method is applied in the rock masses of Xiaowan Hydropower Station. The result shows that the fracture spacing will reduce and its variation coefficient becomes stable asincreases.

Fracturing is the most limiting factor in the extraction of large blocks of granite for gang saw processing and should be carefully studied in the exploration stage. This paper presents the results of the fracturing evaluation of the granite from the quarries of Mondim de Basto, located in the north of Portugal. This very weathered granite has a high market demand and its commercial value is controlled by its yellowish brown colour. The fracturing was evaluated based on the mean/median joint spacing, as well as the volumetric joint count. The results stress the difficulty in obtaining large blocks since few places have the appropriate degree of jointing.

Rock quality designation (RQD) index, provides a general indication of rock mass quality and is widely used in many rock mass classification systems. From the literature review, it is concluded that the RQD methodology has several limitations and one of them is highlighted in this paper with the help of stochastic analysis with Monte Carlo simulation. The purpose of this paper is to improve the results of the RQD index through volumetric joint count (Jv) from accessible surface areas or when drill cores are not available. In this paper, we introduce a low-cost photogrammetric method for rock slope reconstruction with scaled and oriented 3D point cloud, ideally suited for geomechanical analysis. As an outcome, the 3D point cloud is then used to detect the discontinuity sets and to, define their orientation, normal spacing and persistence. Results are then used to calculate the number of joints per m3, which is then used as input in the empirical correlation between the RQD index and the Jv. The main advantage of the proposed methodology is that it is completely based on open-source software.

The successful exploration of dimension stone mainly depends on the quality, size, and shape of extractable blocks of dimension stone. The investigated area is in the Pelješac Peninsula (Croatia), in the External Dinarides orogeny, built from thick carbonate succession, characterized by relatively small deposits of high-quality dimension stone. These conditions demand challenging geological investigations in the “pre-quarry” phase to find optimal quarry location. The size and shape of dimension stone blocks are mainly controlled by fracture pattern systems. In the rugged, covered terrains, it is very hard to obtain a satisfactory amount of fracture data from the surface, so it is necessary to collect them from the underground. Borehole camera technology can visualize the inner part of the rock mass and measure the fracture characteristics. The main conclusions are as follows: (1) the digital borehole camera technology provides a quick, effective, and low-cost geological survey of fractured rock mass; (2) statistical fracture distribution parameters, P10, fracture spacing, Volumetric Joint Count (Jv) based on borehole wall survey can reflect the integrity of rock mass, providing a solid decision-making base for further investment plans and dimension stone excavation method.

Based on the relevant data in the construction process of the south of the Qinling tunnel of the Hanjiang-to-Weihe River Diversion Project, this article obtains the main influencing factors of the tunnel boring machine (TBM) performance of the deep-buried tunnel. According to the characteristics of deep-buried tunnel excavation, the random forest algorithm is used to select the features of the factors affecting the TBM penetration rate, and the four factors with large influence weights including total thrust, revolutions per minute, uniaxial compressive strength and volumetric joint count, are used as TBM penetration rate prediction models input parameters, which can improve the prediction accuracy and convergence speed of the model, and enhance the engineering practicality of the prediction model. Three types of TBM penetration rate prediction models are established: multiple regression model (MR), back propagation neural network model (BPNN) and support vector regression model (SVR). The prediction accuracy of the three models is compared and analyzed. The BPNN prediction model exhibits better prediction performance and generalization ability than the multiple regression model and SVR model, which manifest higher prediction accuracy and prediction stability.

Sandstone-roofed roadways are susceptible to deformation and failure caused by reservoir-water-induced disturbances, thereby compromising human safety. Using rock-mechanics testing techniques, numerical simulations, and engineering principles, this study investigates the strength, deformation, and pore-structure characteristics of sandstone roofs as well as means to support the surrounding rock structure. The results obtained in this study reveal that the residual strain is proportional to the pore-water pressure, which, in turn, causes a significant reduction in the elastic modulus during the unloading phase. Furthermore, an increase in the pore-water pressure causes the shear failure of specimens in compression. The delay between crack initiation and specimen-volume expansion decreases. Moreover, the specimen demonstrates increased deformation and failure responses to changes in the confining pressure, thereby resulting in accelerated conversion. Changes in water inflow can be correlated to crack initiation, propagation, and fracture. This water inflow gradually increases with an increase in the osmotic pressure. Correspondingly, the volumetric strain required for maximum water inflow undergoes a gradual decrease. The increased water inflow can be considered a precursor to specimen failure. In addition, fractures in the surrounding rock structures are mainly caused by joint dislocations. The increase in pore pressure promotes the development of dislocation fractures in the deep surrounding rocks. Subsequently, these fractures overlap with their open counterparts to form large fractures; this increases the roadway-roof subsidence and layer separation of the shallow surrounding rocks, thereby further increasing the fracture count. Lastly, the use of high-performance rock bolts, cable-bolt reinforcements, and W-shaped steel bands is expected to ensure the stability of rocks surrounding sandstone-roofed roadways subject to water-pressure disturbances.

RNA sequencing (RNAseq) is a recent technology that profiles gene expression by measuring the relative frequency of the RNAseq reads. RNAseq read counts data is increasingly used in oncologic care and while radiology features (radiomics) have also been gaining utility in radiology practice such as disease diagnosis, monitoring, and treatment planning. However, contemporary literature lacks appropriate RNA-radiomics (henceforth, radiogenomics) joint modeling where RNAseq distribution is adaptive and also preserves the nature of RNAseq read counts data for glioma grading and prediction. The Negative Binomial (NB) distribution may be useful to model RNAseq read counts data that addresses potential shortcomings. In this study, we propose a novel radiogenomics-NB model for glioma grading and prediction. Our radiogenomics-NB model is developed based on differentially expressed RNAseq and selected radiomics/volumetric features which characterize tumor volume and sub-regions. The NB distribution is fitted to RNAseq counts data, and a log-linear regression model is assumed to link between the estimated NB mean and radiomics. Three radiogenomics-NB molecular mutation models (e.g., IDH mutation, 1p/19q codeletion, and ATRX mutation) are investigated. Additionally, we explore gender-specific effects on the radiogenomics-NB models. Finally, we compare the performance of the proposed three mutation prediction radiogenomics-NB models with different well-known methods in the literature: Negative Binomial Linear Discriminant Analysis (NBLDA), differentially expressed RNAseq with Random Forest (RF-genomics), radiomics and differentially expressed RNAseq with Random Forest (RF-radiogenomics), and Voom-based count transformation combined with the nearest shrinkage classifier (VoomNSC). Our analysis shows that the proposed radiogenomics-NB model significantly outperforms (ANOVA test, p < 0.05) for prediction of IDH and ATRX mutations and offers similar performance for prediction of 1p/19q codeletion, when compared to the competing models in the literature, respectively.

The overarching goal of the doctoral thesis was thus the development of a systematic procedure capable to examine and enhance the role of geomechanical and climatic processes in rockfall susceptibility, performed with statistically based and Machine Learning techniques. To achieve this purpose, two case studies were analysed in the Italian Alps (Valchiavenna, Lombardy Region; Mountain Communities of Mont Cervin and Mont Emilius, Aosta Valley Region). For both case studies, Generalized Additive Models (GAM) were used for rockfall susceptibility assessment; for the Valchiavenna case study, a Random Forest (RF) model was tested too. All models were validated through k-fold cross validation routines and their performance evaluated in terms of area under the receiver operating characteristic curve (AUROC). Predictors’ behaviour physical plausibility was verified through the analysis of the mathematical functions describing the predictors-susceptibility modelled relationships. Specific objectives of the two case studies differed. The Valchiavenna case study was dedicated to testing the role of the outcrop-scale geomechanical properties in a rockfall susceptibility model. Specific objectives were: (i) the optimal selection of sampling points for the execution of geomechanical surveys to be integrated within an already available dataset; (ii) the regionalization over the study area of three geomechanical properties, namely Joint Volumetric Count (Jv), rock-mass weathering index (Wi) and rock-mass equivalent permeability (Keq); (iii) the implementation of the regionalized properties as predictors in a rockfall susceptibility model, along with the traditional morphometric variables; (iv) the investigation of prediction limitations related to inventory incompleteness; (v) the implementation of a methodology for the interpretation of predictors’ behaviour in the RF model, usually considered a black box algorithm; (vi) the integration of the RF and GAM outputs to furnish a spatially distributed measure of uncertainty; (vii) the exploitation of satellite-derived ground deformation data to verify susceptibility outputs and interpret them in an environmental management perspective. The additional geomechanical sampling points were selected by means of the Spatial Simulated Annealing technique. Once collected the necessary geomechanical data, regionalization of the geomechanical target properties was carried out by comparing different deterministic, regressive and geostatistical techniques. The most suitable technique for each property was selected and geomechanical predictors were implemented in the susceptibility models. To verify rockfall inventory completeness related effects, the GAM model was performed both on rockfall data from the official landslide Italian inventory (IFFI) and on its updating with a field-mapped rockfall dataset. Regarding the RF model, the Shapely Additive exPlanations (SHAP) were employed for the interpretation of the predictors’ behaviour. A comparison between GAM and RF related outputs was carried out to verify their coherency, as well as a quantitative integration of the resulting susceptibility maps to reduce uncertainties. Finally, the rockfall susceptibility maps were coupled with Synthetic Aperture Radar (SAR) data from 2014 to 2021: a qualitative geomorphological verification of the outputs was performed, and composite maps were produced. The key results were: (i) geomechanical predictor maps were obtained applying an ordinary kriging for Jv and Wi (NRMSE equal to 13.7% and 14.5%, respectively) and by means of Thin Plate Splines for Keq (NRMSE= 18.5%). (ii) Jv was the most important geomechanical predictor both in the GAM (witha deviance explained of 7.5%) and in the RF model, with a rockfall susceptibility increase in correspondence of the most fractured rock masses. (iii) Wi and Keq were penalized (i.e., they had low influence on rockfall susceptibility) in the GAM model, whereas Keq showed an importance comparable to Jv in the RF model. (iv) In a complex Machine Learning model (RF), the SHAPs allowed the interpretation of predictors’ behaviour, which demonstrated to be coherent with that shown in the GAM model. (v) The models including the geomechanical predictors resulted in acceptable rockfall discrimination capabilities (AUROC>0.7). (vi) The introduction of the geomechanical predictors led to a redistribution of the high-susceptibility areas in plausible geomorphological contexts, such as in correspondence of active slope deformations and structural lineaments, otherwise not revealed by the topographic predictors alone. (vii) Models built with solely the IFFI inventory, resulted in physically implausible susceptibility maps and predictor behaviour, highlighting a bias in the official inventory. (viii) The discordance in predicting rockfall susceptibility between the GAM and the RF models varied from 13% to 8% of the total study area. (ix) From the integration of InSAR data and susceptibility maps, a “SAR Integrated Susceptibility Map”, and an “Intervention Priority Map” were developed as operational products potentially exploitable in environmental planning activities. The Aosta Valley case study was dedicated to challenge the concept of “susceptibility stationarity” by including the climate component in the rockfall susceptibility model. The availability of a large historical rockfall inventory and an extensive, multi-variable meteorological dataset for the period 1990-2020 were crucial input for the analysis. Specific objectives were: (i) the identification of climate conditions related to rockfall occurrence (ii) the summary of the identified relationships in variables to be used in a susceptibility model; (iii) the optimization of a rockfall susceptibility model, including both topographic, climatic and additional snow-related predictors (from a SWE weekly gridded dataset). Starting from an hourly meteorological dataset, climate conditions were summarized in indices related to short-term rainfall (STR), effective water inputs (EWI, including rainfall and snow melting), wet-dry cycles (WD) and freeze-thaw cycles (FT). Climate indices and rockfall occurrence time series were paired. Critical thresholds relating rockfall occurrence to climate indices not-ordinary values (>75th percentile) were derived through a statistical analysis. As summary variables for the susceptibility analysis, the mean annual threshold exceedance frequency for each index was calculated. Model optimization consisted in stepwise modifications of the model settings in order to handle issues related to inventory bias, physical significance of climatic predictors and concurvity (i.e., predictors collinearity in GAMs). The starting point was a “blind model”, i.e., a susceptibility model created without awareness of the rockfall inventory characteristics and of the physical processes potentially influencing susceptibility. To reduce the inventory bias, “visibility” masks were produced so to limit the modelling domain according to the rockfall collection procedures adopted by administrations. Thirdly, models were optimized according to the physical plausibility of climatic predictors, analysed through the smooth functions relating them to susceptibility. Finally, to reduce concurvity, a Principal Component Analysis (PCA) including climatic and snow-related predictors was carried out. Subsequently, the obtained principal components were used to replace the climatic predictors in the susceptibility model. The key results were: (i) the 95% of the rockfalls occurred in severe (or not ordinary) conditions for at least one among the EWI, WD and FT indices; (ii) ignoring inventory bias led to excellent model performance (0.80≤AUROC ≤0.90) but physically implausible outputs; (iii) the selection of non-rockfall points inside the “visibility mask” was a valuable approach to manage the inventory bias influence on outputs; (iv) the inclusion of climate predictors resulted in an improvement of the susceptibility model performance (AUROC up to 3%) in comparison to a topographic-based model; (v) the most important physically plausible climate predictors were EWI, WD, with a deviance explained varying from 5% to 10% each, followed by the maximum cumulated snow melting with a deviance explained varying from 3% to 5%. The effect of FT was masked by elevation. (vi) When the climate and snow related predictors were inserted in the susceptibility model as principal components, concurvity was efficiently reduced. The inclusion of climate processes as non-stationary predictors (i.e., considering climate change) could be a valuable approach both to derive long-term rockfall susceptibility future scenarios and in combination with short-term weather forecasts to adapt susceptibility models to an early warning system for Civil Protection purpose.