Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation J. Amutha, Sandeep Sharma, Sanjay Kumar Sharma; Reliability of clustering algorithm in wireless sensor networks using supervised machine learning classification approaches. AIP Conf. Proc. 7 May 2024; 2853 (1): 020263. https://doi.org/10.1063/5.0197752 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search

Providing a suitable protective water barrier pillar (PWBP) is common to reduce inundation hazards in underground coal mines. The imperative factors influencing its performance include the water head acting on the pillar, cover depth, pillar width, strength properties, and permeability characteristics. The mechanical failure of such pillar is a stress-controlled phenomenon, whereas the hydraulic failure is a strain-based phenomenon. A finite-difference numerical modeling approach was developed to study the hydro-mechanical coupled behavior of protective water barrier pillars. The mechanical stability was evaluated in terms of the percentage of failed (ZoF) and intact zones. The influence of the strain-controlled weakening on the permeability of the flow medium was studied through the coupling of the mechanical and hydraulic effects. The coupled steady-state model was used to estimate the outflow rate and its hydraulic stability. The adequacy of the protective pillar was also investigated by assessing mechanical stability and capability to resist hydraulic pressure against the maximum expected water head. A seepage rate-based classification system has also been proposed to evaluate the seepage potential and assess the hydraulic stability. The model has been validated for two case studies at the cover depth of 136–189.5 m and the existing pillar width of 16–42 m against the water head of 25–141 m.

The drill bit or cutting tool isa very important part of the rock drilling operation, it is directly associated with the rate of penetration. Bit wearing has significant involvement of time, labor, and wealth. By identifying the effect of various machine and rock parameters on bit wearing, the optimum working condition can be adopted to achieve high drilling efficiency. In this experimental work, the rate of penetration and parameters of drilling fluids (i.e. type, additives, and concentration, etc.) were optimized to identify their respective impact on the bit wearing of impregnated core diamond bit. The study has been conducted on a laboratory rotary drilling setup. The role of aqueous solutions of carboxymethyl cellulose (CMC), guar gum (GG), and polyacrylamide (PAM) has been examined as polymeric drilling fluid additives on sandstone rock samples and compared with the results of tap water alone as drilling fluid. A significant decrement in bit wearing has been noticed with all additives and the maximum reduction of 70.02% was achieved with CMC fluid additive.

Prediction of pillar stability is one of the most critical tasks in underground mining industries. This pillar stability analysis requires many input parameters and some of them are difficult to be determined. Various statistical based analysis is presented in literature for assessing pillar stability successfully. In the present work, the data from three mines had been to determine the factor of safety. A total of 63 pillar cases had been collected from the mines. Principal component analysis (PCA) and Stepwise selection and elimination (SSE) models were developed by using multi variate linear regression (MLR) on 45 data sets and subsequently the proposed models were validated on 18 different data sets. The value of coefficient of determination (R2) is 0.86 and 0.84 for PCA and SSE respectively. The root mean square error for PCA and SSE are found to be 0.112 and 0.123 respectively. On validation of the proposed model developed by PCA and SSE, the PCA model provided a better validation results. Hence, PCA is recommended for modelling pillar stability.