The Model To Dynamically Predict Rockbursts Proneness Of Hard Rock At Depth And Its Application

The prediction of rockbursts proneness is the basis of preventing and controlling rockbursts disaster. Based on predicting results, the back-devising to rock engineering and the security measurements can be carried out in time, which have very important theoretical and practical meaning to hard rock mining in depth and underground rock engineering constructing in high stress. Aimed at the developing trends and existing shortage of predicting approach of rockbursts proneness, meanwhile combined with the project of Dongguashan rockbursts prediction in high-stress mining, the dynamic predicting models and pre-controlling approaches of rock engineering rockbursts proneness are studied in this Ph.D dissertation by the use of rock engineering system and data-mining approach. the main researches have been studied as the following:(1) The concept to dynamically predict rockbursts proneness is built. Then the approaches to predict hard rock rockbursts at depth based on previous information and data-mining method are brought forward and its flow chart from the jumping-off place applied with system analyzing and system controlling methods to study problems.(2) The dynamic predicting model utilizing RES approaches to predict engineering rockbursts proneness is constructed, in which engineering geologic factors, engineering environmental factors that should be objective or easily measured are inputted into system as parameters. Then, interactive matrix is built to enumerate influencing factors on rockbursts proneness to study their interactive mechanism, which can not only realize the dynamic predicting to engineering rockbursts proneness, but also analyze each factor's relative strength of effect, besides this the main influencing factors induced rockburst can be found out.(3) The approaches to dynamically predict rockbursts proneness based on improved BP neural network are studied, which mainly includes the researches as establishing formula of GRSE and unite formula (G) RSE of (Global) relative strength of effect based on deeply studying the principle of BP algorithms and BP parameters' analyzing , improving BP algorithms to decode the model's interactive matrix by synthetically applying different transfer function, momentum method and self-recognizing learning rate so that the decoding approach of RES is enriched, deducing the algorithmic process of (G)RSE based on improved BP network.(4) The predicting model with three layers by improved BP algorithms in which influencing factors act as input layer and rockbursts proneness acts as output layer is built, which is used to predict and checkout rockbursts proneness taking place in deep stope by VCR mining and in Dongguashan deep tunnel. Meanwhile, within the special samples, the interactive mechanism of influencing factors is studied individually so that their relativestrength of effect, time-space differentiate characteristics and the main influencing factors induced rockbursts are made clearly.(5) With the application of fuzzy mathematic and fuzzy information optimizing theory and approaches , the improved intelligent model to dynamically predict rockbursts proneness is constructed, in which traditional spot to spot mapping is replaced by muster to muster mapping, typical black box learning manner is replaced by IF-THEN expressing manner which is easily accepted by people. Meanwhile the powerful advantage of fuzzy theory integrated with neural is fully utilized. Therefore, the probably problems such as not constringency caused by inconsistency with samples each other, information absence under the complex condition of few samples and difficult in knowledge expressing are settled preferably. As the result, the application range of the model to dynamically predict rockbursts proneness is widened.(6) Based on examples' characteristic, the key approaches to predict rockbursts proneness by fuzzy neural model in deep stope is discussed, which involves the researches as that applying c-clustering integrated with subtractive clustering approach to deal with data kept in previous information under the condition of lacking much more datum. Then in terms of subjective degree of function and the characteristics of clustering samples, the violent degree of rockbursts is finely marked off. Also the fuzzy relationship between influencing factors and rockbursts proneness is studied by applying fuzzy information optimizing theory. More over, a new approach to evaluate each influencing factor's weight is brought forward which fully considers multi-factors' interaction.(7) The fuzzy neural model is adopted to predicting and checkouting rockburst proneness taking place in deep stope by VCR mining, which indicates that the model has active meaning both in mining previous information and directing engineering practicing.(8) The main influencing factors , stress distributing state probably inducing deep stope rockbursts in Dongguashan copper mine is further studied by on-spot experiments, room experiments combined with numerical simulation, from which sti-stress distribution discipline, especially stress newly distributing at wall-rock which is induced by static pressure, semi-static pressure and dynamic pressure at different mining techniques is discovered. So, by back devising, mining schemes are optimized under rockbursts proneness. The study in this Ph.D dissertation offer a important decision-making reference to efficiently pre-control rockbursts probably induced by stope mining in Dongguashan copper mine.