Prediction of Rockburst Intensity Grade in Deep Underground Excavation Using Adaptive Boosting Classifier

Umesh

Intl Journal of Climatology

2023

The popularity of cutting‐edge machine learning ensemble approaches has solved many climate change research and prediction issues. The six top‐performing GCMs obtained from Technique for Order Preference by Similarity to an Ideal Solution were ensembled using seven machine learning ensemble methods such as Random Forest Regressor (RFR), Support Vector Regressor (SVR), Linear Regression (LR), Adaptive Boosting Regressor (AdaBoost), Extreme Gradient Boosting Regressor (XGBR), Extra Tree Regressor (ETR), Multi‐Layer Perceptron neural network (MLP) and simple Arithmetic Mean (AM) over the diverse geo‐climatic basins. Precipitation is best simulated by EC‐Earth3 and BCC‐CSM2‐MR. Maximum temperature by MPI‐ESM1‐2‐HR, EC‐Earth3‐Veg, INM‐CM5‐0 and MPI‐ESM1‐2‐LR. Minimum temperature by INM‐CM5‐0 and MPI‐ESM1‐2‐LR model. The MME of XGBR and RFR stand out for their superior performance across all six basins, with exceptional performance over the per‐humid basins, while AdaBoost, SVR and the AM underperform. Examining the interseasonal variability of the simulated MMEs over the basins highlights the reliability of these MME models. The anticipated change in maximum and minimum temperature in the SSP245 and SSP585 in the future horizon corroborates the undeniable rise in temperature by all the MMEs with a dramatic change in future temperature in AM and AdaBoost in precipitation with a factor of two rises in the far future over the recent past. Though climate change is expected to increase precipitation, atmospheric stabilization over the Ghats will affect the spatiotemporal distribution of precipitation. We recommend a comprehensive testing and validation approach to generate ensembles in regional investigations involving complicated and diverse precipitation mechanisms.

Section: Methodsmentioning

confidence: 99%

The effectiveness of machine learning‐based multi‐model ensemble predictions of CMIP6 in Western Ghats of India

Umesh

Intl Journal of Climatology

2023

“…Another important aspect of underground projects is the rockburst incident. Researchers are proposed various prediction model to predict the rockburst hazard [18][19][20] . Robert et al 21 conducted both field and numerical study to obtain the response of underground water pipes subjected to traffic loads.…”

Section: Openmentioning

confidence: 99%

Protection of pipeline below pavement subjected to traffic induced dynamic response

Chaudhuri

Choudhury

2023

Sci Rep

Failure of pipelines below road pavement results to the disruption of both the traffic movement and the consumers of the pipelines. Intermediate safeguard layer can be used to protect the pipeline from heavy traffic loads. The present study proposed analytical solutions to obtain the dynamic response of buried pipe below road pavement with and without considering safeguard based on the concept of triple and double beam system respectively. Pavement layer, safeguard and the pipeline are considered as Euler Bernoulli’s beam. Advanced soil model is used (viscoelastic foundation with shear interaction between springs) to model the surrounding soil. Self-weight of soil is also considered in the present study. The obtained governing coupled differential equations are solved adopting finite sine Fourier transform, Laplace transform and their inverse transformation. The proposed formulation is initially verified with the past numerical and analytical studies and then validated with the three-dimensional finite element based numerical analysis. From parametric study it is perceived that the stability of the pipe can be significantly increased by providing intermediate barrier. Further, pipe deformation is increases with increasing traffic loads. At very high-speed range (> 60 m s−1), pipe deformation is significantly rises with increasing traffic speed. The present study can be useful in preliminary design stage before performing rigorous and expensive numerical or experimental study.

“…On the basis of microseismic monitoring data, Zhao et In deep underground projects, a self-organizing map and fuzzy c-mean clustering techniques were used to cluster rockbursts events (33). Even though several rockburst estimation models have been described and compared by previous researchers (34)(35)(36)(37)(38)(39)(40), developing an accurate and reliable predictive model still poses a significant challenge for the ground, which is likely to experience frequent rock bursts. Further, many other models for forecasting rockbursts can be considered valuable and efficient tools for geological and mining engineering applications.…”

Section: Introductionmentioning

confidence: 99%

Application of KNN-based isometric mapping and fuzzy c-means algorithm to predict short-term rockburst risk in deep underground projects

Kamran

Ullah

Ahmad

et al. 2022

Front. Public Health

Self Cite

The rockburst phenomenon is the major source of the high number of casualties and fatalities during the construction of deep underground projects. Rockburst poses a severe hazard to the safety of employees and equipment in subsurface mining operations. It is a hot topic in recent years to examine and overcome rockburst risks for the safe installation of deep urban engineering designs. Therefore, for a cost-effective and safe underground environment, it is crucial to determine and predict rockburst intensity prior to its occurrence. A novel model is presented in this study that combines unsupervised and supervised machine learning approaches in order to predict rockburst risk. The database for this study was built using authentic microseismic monitoring occurrences from the Jinping-II hydropower project in China, which consists of 93 short-term rockburst occurrences with six influential features. The prediction process was succeeded in three steps. Firstly, the original rockburst database's magnification was reduced using a state-of-the-art method called isometric mapping (ISOMAP) algorithm. Secondly, the dataset acquired from ISOMAP was categorized using the fuzzy c-means algorithm (FCM) to reduce the minor spectral heterogeneity impact in homogenous areas. Thirdly, K-Nearest neighbor (KNN) was employed to anticipate different levels of short-term rockburst datasets. The KNN's classification performance was examined using several performance metrics. The proposed model correctly classified about 96% of the rockbursts events in the testing datasets. Hence, the suggested model is a realistic and effective tool for evaluating rockburst intensity. Therefore, the proposed model can be employed to forecast the rockburst risk in the early stages of underground projects that will help to minimize casualties from rockburst.