“…The predicted 3D models (Figs. [9][10][11] show more reliability mainly near the geophysical profiles at shallow depth; however, the resolution decreases with increasing the distance from surveyed lines as well as with increasing the depth. The resolution of electrical resistivity methods is poorer than seismic or GPR methods at large depths.…”
Section: Discussionmentioning
confidence: 95%
“…Three faults or fractures namely F1, F2 and F3 were detected in the investigated site (Figs. [7][8][9][10][11]. All fractures/faults were delineated in northwest-southeast direction.…”
Section: Resultsmentioning
confidence: 99%
“…Generally, we divide the distinct rocks of different quality into two main categories, such as rock mass of good/high quality (RQD greater than 75% and RCI greater than 20) and rock mass of poor/low quality (RQD less than 75% and RCI less than 20) (Figs. [7][8][9][10][11]. The obtained general equations can be used in most of the hard-rock sites to evaluate rock mass quality via RQD/RCI using resistivity data of any geophysical method.…”
Section: Discussionmentioning
confidence: 99%
“…Inadequate characterization of subsurface rock units often leads to structural dilapidation and potential foundation-related failures 3 – 5 . Given the natural heterogeneity and spatial variability in the subsurface environments, detailed geological knowledge of a construction site is necessary in order to propose structural foundations and effective earthworks 1 , 6 – 9 . The necessary bearing competency for engineered structures is provided by the foundation rocks 10 , 11 .…”
Thorough and accurate assessment of rock mass units is important for development of engineering infrastructures and groundwater resources assessments. Rock mass units are widely evaluated by reliable geomechanical parameters namely rock quality designation (RQD) and rock core index (RCI). Conventionally, these parameters are acquired via an extensive number of geotechnical tests. Such tests, however, suffer efficiency for data coverage, cost, equipment and topographic constrictions, and hence cause ambiguity in geological models for a detailed evaluation of rock mass integrity. Conversely, geophysical surveys offer fast, more user-friendly, less invasive, more cost-effective and less time-consuming approach for geological investigations. The past research confirms a useful link between geophysical and geotechnical parameters. But, none of the past studies provides a suitable and generalized relation between these parameters which can reduce geotechnical model uncertainty mostly caused by inadequate data and subsurface heterogeneity. This paper proposes a meaningful and feasible method to obtain geomechanical parameters using a certain number of drillings and geophysical data of four different sites. Based on electrical resistivity obtained from electrical resistivity tomography (ERT) and controlled-source audio-frequency magneto telluric (CSAMT), this research provides the general and adaptable formulas for geotechnical parameter estimation and reduces geological model uncertainty for more detailed 2D/3D imaging of RQD and RCI covering the whole sites where even no drilling data exists. Thus, the investigated sites are assessed laterally and vertically along each geophysical profile via distinct value ranges of geological parameters for a thorough and reliable evaluation of rock mass units in highly heterogeneous setting. Our research reduces the ambiguity caused by structural heterogeneities and scarce data, fills the gap between inadequate well tests and the true geological models, and gives new insights into the rock mass units for proper engineering design and groundwater exploitation.
“…The predicted 3D models (Figs. [9][10][11] show more reliability mainly near the geophysical profiles at shallow depth; however, the resolution decreases with increasing the distance from surveyed lines as well as with increasing the depth. The resolution of electrical resistivity methods is poorer than seismic or GPR methods at large depths.…”
Section: Discussionmentioning
confidence: 95%
“…Three faults or fractures namely F1, F2 and F3 were detected in the investigated site (Figs. [7][8][9][10][11]. All fractures/faults were delineated in northwest-southeast direction.…”
Section: Resultsmentioning
confidence: 99%
“…Generally, we divide the distinct rocks of different quality into two main categories, such as rock mass of good/high quality (RQD greater than 75% and RCI greater than 20) and rock mass of poor/low quality (RQD less than 75% and RCI less than 20) (Figs. [7][8][9][10][11]. The obtained general equations can be used in most of the hard-rock sites to evaluate rock mass quality via RQD/RCI using resistivity data of any geophysical method.…”
Section: Discussionmentioning
confidence: 99%
“…Inadequate characterization of subsurface rock units often leads to structural dilapidation and potential foundation-related failures 3 – 5 . Given the natural heterogeneity and spatial variability in the subsurface environments, detailed geological knowledge of a construction site is necessary in order to propose structural foundations and effective earthworks 1 , 6 – 9 . The necessary bearing competency for engineered structures is provided by the foundation rocks 10 , 11 .…”
Thorough and accurate assessment of rock mass units is important for development of engineering infrastructures and groundwater resources assessments. Rock mass units are widely evaluated by reliable geomechanical parameters namely rock quality designation (RQD) and rock core index (RCI). Conventionally, these parameters are acquired via an extensive number of geotechnical tests. Such tests, however, suffer efficiency for data coverage, cost, equipment and topographic constrictions, and hence cause ambiguity in geological models for a detailed evaluation of rock mass integrity. Conversely, geophysical surveys offer fast, more user-friendly, less invasive, more cost-effective and less time-consuming approach for geological investigations. The past research confirms a useful link between geophysical and geotechnical parameters. But, none of the past studies provides a suitable and generalized relation between these parameters which can reduce geotechnical model uncertainty mostly caused by inadequate data and subsurface heterogeneity. This paper proposes a meaningful and feasible method to obtain geomechanical parameters using a certain number of drillings and geophysical data of four different sites. Based on electrical resistivity obtained from electrical resistivity tomography (ERT) and controlled-source audio-frequency magneto telluric (CSAMT), this research provides the general and adaptable formulas for geotechnical parameter estimation and reduces geological model uncertainty for more detailed 2D/3D imaging of RQD and RCI covering the whole sites where even no drilling data exists. Thus, the investigated sites are assessed laterally and vertically along each geophysical profile via distinct value ranges of geological parameters for a thorough and reliable evaluation of rock mass units in highly heterogeneous setting. Our research reduces the ambiguity caused by structural heterogeneities and scarce data, fills the gap between inadequate well tests and the true geological models, and gives new insights into the rock mass units for proper engineering design and groundwater exploitation.
“…These monitoring points are utilized for prediction and evaluation using the EEMD-Prophet, Prophet, and ARMA models. Figure 13 is a visual representation of ground settlement generated through Kriging interpolation 26 after predicting the monitoring points in the Taigu East Station area. Figure 12 Distribution of InSAR monitoring points at Taigu East Station.…”
Environmental changes and ground subsidence along railway lines are serious concerns during high-speed railway operations. It is worth noting that AutoRegressive Integrated Moving Average (ARMA), Long Short-Term Memory (LSTM), and other prediction methods may present limitations when applied to predict InSAR time series results. To address this issue, this study proposes a prediction method that decomposes the nonlinear settlement time series of feature points obtained through InSAR technology using Ensemble Empirical Mode Decomposition (EEMD). Subsequently, multiple Intrinsic Mode Functions (IMFs) are generated, and each IMF is individually predicted using the Prophet forecasting model. Finally, we employ an equal-weight superimposition method to combine the results, resulting in the prediction of the InSAR settlement time series. The predicted values of each component are subsequently weighted equally and combined to derive the final prediction outcome. This paper selects InSAR monitoring data along a high-speed railway in inland China and uses the proposed method and ARMA and Prophet models to carry out comparative experiments. The experimental results show that compared with the ARMA and Prophet models, the method in this paper improves the root mean square error by 58.01% and 32.3%, and increases the mean absolute error by 62.69% and 33.78%, respectively. The predicted settlement values generated by our method exhibit better agreement with the actual InSAR monitoring values.
The overarching goal of the study was the development of a potentially dynamic rockfall susceptibility model by including climate predictors. The work is based on previously defined critical thresholds relating three climate indices — effective water inputs (EWI), wet-dry cycles (WD) and freeze–thaw cycles (FT) — and rockfall occurrence. The pilot area is located in the Aosta Valley region (Italian Western Alps). The susceptibility model settings were optimized through a stepwise procedure, carried out by means of generalized additive models (GAM). Predictors included topographic, climatic and additional snow-related variables. As climatic predictors, the mean annual threshold exceedance frequency was calculated for each index. All models were developed including an automatic penalization of statistically non-significant variables (i.e. shrinkage). The initial susceptibility model was set without considering potential inventory bias. Secondly, a “visibility mask” was produced to limit the modelling domain according to the rockfall event census procedures. Thirdly, GAMs functional relationships were analysed to verify the physical plausibility of predictors. Finally, to reduce concurvity, a principal component analysis (PCA) including climatic and snow-related predictors was carried out. Key findings were as follows: (i) ignoring inventory bias led to excellent model performance but to physically implausible outputs; (ii) the selection of non-rockfall points inside a “visibility mask” is effective in managing inventory bias influence on outputs; (iii) the inclusion of climate predictors resulted in an improvement of the physical interpretability of the associated models and susceptibility maps, being EWI, WD and the maximum cumulated snow melting the most important physically plausible climate predictors; (iv) the PCA strategy can efficiently reduce model concurvity.
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