A Bayes discriminant analysis method to identify the risky of complicated goaf in mines was presented. Nine factors influencing the stability of goaf risky, including uniaxial compressive strength of rock, elastic mod...A Bayes discriminant analysis method to identify the risky of complicated goaf in mines was presented. Nine factors influencing the stability of goaf risky, including uniaxial compressive strength of rock, elastic modulus of rock, rock quality designation （RQD）, area ratio of pillar, ratio of width to height of pillar, depth of ore body, volume of goaf, dip of ore body and area of goal, were selected as discriminant indexes in the stability analysis of goal. The actual data of 40 goals were used as training samples to establish a discriminant analysis model to identify the stability of goaf. The results show that this discriminant analysis model has high precision and misdiscriminant ratio is 0.025 in re-substitution process. The instability identification of a metal mine was distinguished by using this model and the identification result is identical with that of practical situation.展开更多
A method to forecast the over-excavation of underground opening by using the Bayes discriminant analysis(BDA)theory was presented.The Bayes discriminant analysis theory was introduced.Based on an engineering example,t...A method to forecast the over-excavation of underground opening by using the Bayes discriminant analysis(BDA)theory was presented.The Bayes discriminant analysis theory was introduced.Based on an engineering example,the factors influencing the over-excavation of underground opening were taken into account to build a forecast BDA model,and the prior information about over-excavation of underground opening was also taken into consideration.Five parameters influencing the over-excavation of opening,including 2 groups of joints,1 group of layer surface,extension and space between structure faces were selected as geometric parameters.Engineering data in an underground opening were used as the training samples.The cross-validation method was introduced to verify the stability of BDA model and the ratio of mistake-discrimination was equal to zero after the BDA model was trained.Data in an underground engineering were used to test the discriminant ability of BDA model.The results show that five forecast results are identical with the actual situation and BDA can be used in practical engineering.展开更多
基金Project (2010CB732004) supported by the National Basic Research Program of China
文摘A Bayes discriminant analysis method to identify the risky of complicated goaf in mines was presented. Nine factors influencing the stability of goaf risky, including uniaxial compressive strength of rock, elastic modulus of rock, rock quality designation （RQD）, area ratio of pillar, ratio of width to height of pillar, depth of ore body, volume of goaf, dip of ore body and area of goal, were selected as discriminant indexes in the stability analysis of goal. The actual data of 40 goals were used as training samples to establish a discriminant analysis model to identify the stability of goaf. The results show that this discriminant analysis model has high precision and misdiscriminant ratio is 0.025 in re-substitution process. The instability identification of a metal mine was distinguished by using this model and the identification result is identical with that of practical situation.
基金Project(50490274)supported by the National Natural Science Foundation of China
文摘A method to forecast the over-excavation of underground opening by using the Bayes discriminant analysis(BDA)theory was presented.The Bayes discriminant analysis theory was introduced.Based on an engineering example,the factors influencing the over-excavation of underground opening were taken into account to build a forecast BDA model,and the prior information about over-excavation of underground opening was also taken into consideration.Five parameters influencing the over-excavation of opening,including 2 groups of joints,1 group of layer surface,extension and space between structure faces were selected as geometric parameters.Engineering data in an underground opening were used as the training samples.The cross-validation method was introduced to verify the stability of BDA model and the ratio of mistake-discrimination was equal to zero after the BDA model was trained.Data in an underground engineering were used to test the discriminant ability of BDA model.The results show that five forecast results are identical with the actual situation and BDA can be used in practical engineering.
