A new approach for prediction of face advance rete (FAR) prior to mining operation and determination of the operation efficiency after mining operation in retreat longwall mining panel is presented based upon the conc...A new approach for prediction of face advance rete (FAR) prior to mining operation and determination of the operation efficiency after mining operation in retreat longwall mining panel is presented based upon the concepts of rock engineering system (RES). For this purpose, six longwall panels considered in Parvadeh-I coal mine. Seven major effective parameters on FAR was selected including coal mine roof rating, gas propagation, safety factor of longwall face, ratio of joint spacing to cutting depth at longwall face, longwall face inclination, panel width, floor rock mass rating. To performance evaluation of the presented model, the relationship between the average vulnerability indexes of advance operation with FAR was determined in considered panels with coefficient of determination (R2) equal to 0.884 that indicate relatively acceptable correlation and compatibility. Investigations of the research indicated that it is possible to determine the actual operation efficiency under fair conditions by a RES-based model. The inevitable reduction of FAR for each longwall panel was determined by presented model that the difference amount between the maximum possible practical face advance rate (FARmpp) and recorded actual face advance rate (FARa) indicate the operation efficiency. Applied approach in this paper can be used to prediction of FAR in retreat longwall mining panel for same conditions that can have many benefits, including better and more accurate planning for the sales market and mine operation. Also, presented method in this paper can be applied as a useful tool to determination of actual operation efficiency for other sections and extraction methods in coal mines.展开更多
The rock mass engineering system （RMES） basically consists ofrock mass engineering （RME）, water system and surroundingecological environments, etc. The RMES is characterized by nonlinearity,occurrence of chaos and...The rock mass engineering system （RMES） basically consists ofrock mass engineering （RME）, water system and surroundingecological environments, etc. The RMES is characterized by nonlinearity,occurrence of chaos and self-organization （Tazaka, 1998;Tsuda, 1998; Kishida, 2000）. From construction to abandonmentof RME, the RMES will experience four stages, i.e. initial phase,development phase, declining phase and failure phase. In thiscircumstance, the RMES boundary conditions, structural safetyand surrounding environments are varied at each phase, so arethe evolution characteristics and disasters （Wang et al., 2014）.展开更多
Classical rock mass classification systems are not applicable to carbonate rocks,especially when these are affected by karst processes.Their applications to such settings could therefore result in outcomes not represe...Classical rock mass classification systems are not applicable to carbonate rocks,especially when these are affected by karst processes.Their applications to such settings could therefore result in outcomes not representative of the real stress-strain behavior.In this study,we propose a new classification of carbonate rock masses for engineering purposes,by adapting the rock engineering system(RES) method by Hudson for fractured and karstified rock masses,in order to highlight the problems of implementation of geomechanical models to carbonate rocks.This new approach allows a less rigid classification for carbonate rock masses,taking into account the local properties of the outcrops,the site conditions and the type of engineering work as well.展开更多
This research describes a quantitative,rapid,and low-cost methodology for debris flow susceptibility evaluation at the basin scale using open-access data and geodatabases.The proposed approach can aid decision makers ...This research describes a quantitative,rapid,and low-cost methodology for debris flow susceptibility evaluation at the basin scale using open-access data and geodatabases.The proposed approach can aid decision makers in land management and territorial planning,by first screening for areas with a higher debris flow susceptibility.Five environmental predisposing factors,namely,bedrock lithology,fracture network,quaternary deposits,slope inclination,and hydrographic network,were selected as independent parameters and their mutual interactions were described and quantified using the Rock Engineering System(RES)methodology.For each parameter,specific indexes were proposed,aiming to provide a final synthetic and representative index of debris flow susceptibility at the basin scale.The methodology was tested in four basins located in the Upper Susa Valley(NW Italian Alps)where debris flow events are the predominant natural hazard.The proposed matrix can represent a useful standardized tool,universally applicable,since it is independent of type and characteristic of the basin.展开更多
In order to effectively cope with exponent increase of the complexity faced to the rock mechanics analysis problems and the large incompatibility existing between the information level required to model the rock mass ...In order to effectively cope with exponent increase of the complexity faced to the rock mechanics analysis problems and the large incompatibility existing between the information level required to model the rock mass and engineering and our obtainable information level at hand,the integrated approaches with intelligent characters are proposed. Many previous standard methods,such as precedent type analysis,rock classification,analytic method stress-based,basic numerical methods (BEM,FEM,DEM,hybrid),and their extended numerical methods (fully coupled) to be developed,can be selected respectively or integrated accordingly. It is alternative to develop basic/fully integrated system,and internet-based approaches. These novel methods can also be selected or integrated each other or with the standard methods to perform rock mechanics analysis. Some key techniques to develop these alternative methods are discussed. It may focus in future on developing fully integrated systems and internet-based approaches. Developing an environmental,virtual facility/space shall be firstly done for this collaborative research on internet.展开更多
For better rock mass characterization and support design, 3D engineering geological mapping was carried for the heading portion of the under construction 200.00 m long, 68.75 m high and 20.20 m wide underground additi...For better rock mass characterization and support design, 3D engineering geological mapping was carried for the heading portion of the under construction 200.00 m long, 68.75 m high and 20.20 m wide underground additional surge pool cavern of a Pranahitha-Chevella Sujala Sravanthi lift irrigation scheme package 8, India. To study cavern behavior, 3D geologic mapping of heading portion is very important for large cavern for predicting geologic conditions in benching down up to invert level, planning support system, selecting inclination for best location of supplemental rock bolt and choosing strategic locations for various types of instrumentation. The assessment of Tunnel Quality Index “Q” and Geomechanics classification for the granitic rock mass was done based on the information available of the rock joints and their nature and 3D geological logging. Hoek-Brown parameters were also determined by the statistical analysis of the results of a set of triaxial tests on core samples. On basis of geological characteristics and NMT Q-system chart, support system is recommended which includes rock bolt, steel fibre reinforced shotcrete and grouting. To evaluate the efficacy of the proposed support system, the capacity of support system is determined.展开更多
This study aimed to develop a clinical Decision Support Model (DSM) which is software that provides physicians and other healthcare stakeholders with patient-specific assessments and recommendation in aiding clinical ...This study aimed to develop a clinical Decision Support Model (DSM) which is software that provides physicians and other healthcare stakeholders with patient-specific assessments and recommendation in aiding clinical decision-making while discharging Breast cancer patient since the diagnostics and discharge problem is often overwhelming for a clinician to process at the point of care or in urgent situations. The model incorporates Breast cancer patient-specific data that are well-structured having been attained from a prestudy’s administered questionnaires and current evidence-based guidelines. Obtained dataset of the prestudy’s questionnaires is processed via data mining techniques to generate an optimal clinical decision tree classifier model which serves physicians in enhancing their decision-making process while discharging a breast cancer patient on basic cognitive processes involved in medical thinking hence new, better-formed, and superior outcomes. The model also improves the quality of assessments by constructing predictive discharging models from code attributes enabling timely detection of deterioration in the quality of health of a breast cancer patient upon discharge. The outcome of implementing this study is a decision support model that bridges the gap occasioned by less informed clinical Breast cancer discharge that is based merely on experts’ opinions which is insufficiently reinforced for better treatment outcomes. The reinforced discharge decision for better treatment outcomes is through timely deployment of the decision support model to work hand in hand with the expertise in deriving an integrative discharge decision and has been an agreed strategy to eliminate the foreseeable deteriorating quality of health for a discharged breast cancer patients and surging rates of mortality blamed on mistrusted discharge decisions. In this paper, we will discuss breast cancer clinical knowledge, data mining techniques, the classifying model accuracy, and the Python web-based decision support model that predicts avoidable re-hospitalization of a breast cancer patient through an informed clinical discharging support model.展开更多
文摘A new approach for prediction of face advance rete (FAR) prior to mining operation and determination of the operation efficiency after mining operation in retreat longwall mining panel is presented based upon the concepts of rock engineering system (RES). For this purpose, six longwall panels considered in Parvadeh-I coal mine. Seven major effective parameters on FAR was selected including coal mine roof rating, gas propagation, safety factor of longwall face, ratio of joint spacing to cutting depth at longwall face, longwall face inclination, panel width, floor rock mass rating. To performance evaluation of the presented model, the relationship between the average vulnerability indexes of advance operation with FAR was determined in considered panels with coefficient of determination (R2) equal to 0.884 that indicate relatively acceptable correlation and compatibility. Investigations of the research indicated that it is possible to determine the actual operation efficiency under fair conditions by a RES-based model. The inevitable reduction of FAR for each longwall panel was determined by presented model that the difference amount between the maximum possible practical face advance rate (FARmpp) and recorded actual face advance rate (FARa) indicate the operation efficiency. Applied approach in this paper can be used to prediction of FAR in retreat longwall mining panel for same conditions that can have many benefits, including better and more accurate planning for the sales market and mine operation. Also, presented method in this paper can be applied as a useful tool to determination of actual operation efficiency for other sections and extraction methods in coal mines.
基金funded by the National Natural Science Foundation of China(Grant Nos.51274110,51304108,U1361211)
文摘The rock mass engineering system （RMES） basically consists ofrock mass engineering （RME）, water system and surroundingecological environments, etc. The RMES is characterized by nonlinearity,occurrence of chaos and self-organization （Tazaka, 1998;Tsuda, 1998; Kishida, 2000）. From construction to abandonmentof RME, the RMES will experience four stages, i.e. initial phase,development phase, declining phase and failure phase. In thiscircumstance, the RMES boundary conditions, structural safetyand surrounding environments are varied at each phase, so arethe evolution characteristics and disasters （Wang et al., 2014）.
基金supported by MIUR (Italian Ministry of Education,University and Research Grant 15034/ 2007) under Grant 2010 ex MURST 60%"Modelli geologico-tecnici, idrogeologici e geofisici per la tutela e la valorizzazione delle risorse naturali,ambientali e culturali"(coordinator G.F.Andriani) and Grant 2013 ex MURST 60%"Ricerche stratigrafico-sedimentologiche di base ed applicate per it riconoscimento,la gestione e la tutela delle georisorse e dei beni storico/culturali e geoambientali"(coordinator M.Tropeano)the project Interreg Ⅲ A-"WET SYS B" 200-2006(responsible G.F.Andriani),with the financial contribution by the European Community
文摘Classical rock mass classification systems are not applicable to carbonate rocks,especially when these are affected by karst processes.Their applications to such settings could therefore result in outcomes not representative of the real stress-strain behavior.In this study,we propose a new classification of carbonate rock masses for engineering purposes,by adapting the rock engineering system(RES) method by Hudson for fractured and karstified rock masses,in order to highlight the problems of implementation of geomechanical models to carbonate rocks.This new approach allows a less rigid classification for carbonate rock masses,taking into account the local properties of the outcrops,the site conditions and the type of engineering work as well.
文摘This research describes a quantitative,rapid,and low-cost methodology for debris flow susceptibility evaluation at the basin scale using open-access data and geodatabases.The proposed approach can aid decision makers in land management and territorial planning,by first screening for areas with a higher debris flow susceptibility.Five environmental predisposing factors,namely,bedrock lithology,fracture network,quaternary deposits,slope inclination,and hydrographic network,were selected as independent parameters and their mutual interactions were described and quantified using the Rock Engineering System(RES)methodology.For each parameter,specific indexes were proposed,aiming to provide a final synthetic and representative index of debris flow susceptibility at the basin scale.The methodology was tested in four basins located in the Upper Susa Valley(NW Italian Alps)where debris flow events are the predominant natural hazard.The proposed matrix can represent a useful standardized tool,universally applicable,since it is independent of type and characteristic of the basin.
基金Nature Science Foundation of China under Grant no.50179034.
文摘In order to effectively cope with exponent increase of the complexity faced to the rock mechanics analysis problems and the large incompatibility existing between the information level required to model the rock mass and engineering and our obtainable information level at hand,the integrated approaches with intelligent characters are proposed. Many previous standard methods,such as precedent type analysis,rock classification,analytic method stress-based,basic numerical methods (BEM,FEM,DEM,hybrid),and their extended numerical methods (fully coupled) to be developed,can be selected respectively or integrated accordingly. It is alternative to develop basic/fully integrated system,and internet-based approaches. These novel methods can also be selected or integrated each other or with the standard methods to perform rock mechanics analysis. Some key techniques to develop these alternative methods are discussed. It may focus in future on developing fully integrated systems and internet-based approaches. Developing an environmental,virtual facility/space shall be firstly done for this collaborative research on internet.
文摘For better rock mass characterization and support design, 3D engineering geological mapping was carried for the heading portion of the under construction 200.00 m long, 68.75 m high and 20.20 m wide underground additional surge pool cavern of a Pranahitha-Chevella Sujala Sravanthi lift irrigation scheme package 8, India. To study cavern behavior, 3D geologic mapping of heading portion is very important for large cavern for predicting geologic conditions in benching down up to invert level, planning support system, selecting inclination for best location of supplemental rock bolt and choosing strategic locations for various types of instrumentation. The assessment of Tunnel Quality Index “Q” and Geomechanics classification for the granitic rock mass was done based on the information available of the rock joints and their nature and 3D geological logging. Hoek-Brown parameters were also determined by the statistical analysis of the results of a set of triaxial tests on core samples. On basis of geological characteristics and NMT Q-system chart, support system is recommended which includes rock bolt, steel fibre reinforced shotcrete and grouting. To evaluate the efficacy of the proposed support system, the capacity of support system is determined.
文摘This study aimed to develop a clinical Decision Support Model (DSM) which is software that provides physicians and other healthcare stakeholders with patient-specific assessments and recommendation in aiding clinical decision-making while discharging Breast cancer patient since the diagnostics and discharge problem is often overwhelming for a clinician to process at the point of care or in urgent situations. The model incorporates Breast cancer patient-specific data that are well-structured having been attained from a prestudy’s administered questionnaires and current evidence-based guidelines. Obtained dataset of the prestudy’s questionnaires is processed via data mining techniques to generate an optimal clinical decision tree classifier model which serves physicians in enhancing their decision-making process while discharging a breast cancer patient on basic cognitive processes involved in medical thinking hence new, better-formed, and superior outcomes. The model also improves the quality of assessments by constructing predictive discharging models from code attributes enabling timely detection of deterioration in the quality of health of a breast cancer patient upon discharge. The outcome of implementing this study is a decision support model that bridges the gap occasioned by less informed clinical Breast cancer discharge that is based merely on experts’ opinions which is insufficiently reinforced for better treatment outcomes. The reinforced discharge decision for better treatment outcomes is through timely deployment of the decision support model to work hand in hand with the expertise in deriving an integrative discharge decision and has been an agreed strategy to eliminate the foreseeable deteriorating quality of health for a discharged breast cancer patients and surging rates of mortality blamed on mistrusted discharge decisions. In this paper, we will discuss breast cancer clinical knowledge, data mining techniques, the classifying model accuracy, and the Python web-based decision support model that predicts avoidable re-hospitalization of a breast cancer patient through an informed clinical discharging support model.
