Method And Decision Support System For Evaluating TBM Selection And Tunneling Adaptability Based On Artificial Intelligence

Limited to the environment,topography and geological conditions,from the three aspects of construction period,cost and technological progress,TBM(Tunnel Boring Machine)construction method is the first choice for deep-buried long tunnel construction.Under complex geological conditions,unfavorable geology such as inrush water,soft rock deformation,fault fracture zone,rockburst will affect the applicability of the TBM method.Therefore,whether the TBM method can be applied needs to evaluate the adaptability of TBM.Due to the lack of systematic theory and quantitative analysis method in TBM selection and tunneling adaptability evaluation at present,therefore,TBM selection and tunneling adaptability evaluation under complex geological conditions have become an important issue to be solved urgently in the application of TBM engineering method.On the basis of relevant research at home and abroad,this paper makes a systematic and in-depth study on TBM selection and tunneling adaptability evaluation under complex geological conditions by means of fuzzy mathematics,mathematical statistics,artificial intelligence and computer programming.The main work and research results are as follows:(1)Based on the theory of fuzzy mathematics,the definition and determination method of TBM system fitness(including comprehensive fitness)are given,and the grading criteria of TBM adaptability evaluation are proposed;Combining fuzzy mathematics theory with intelligent decision_making design theory,this paper proposes a TBM selection and tunneling adaptive evaluation analysis method based on fuzzy multi-agent and case-based reasoning;The system framework and structure of IEDSS-TBMSAT are constructed by combining fiuzzy multi-agent and case-based reasoning TBM selection and excavation adaptive evaluation methods.(2)The main factors affecting TBM selection and tunneling adaptability are selected.Based on the method of fuzzy mathematics analysis,the evaluation index and system of TBM selection and tunneling adaptability are constructed,The membership functions of each evaluation index are determined.The weights of three different TBM type selection and tunneling adaptability evaluation indicators are determined by using the weight auxiliary calculation program.Based on the TBM selection and tunneling knowledge acquired,the decision-making knowledge base of TBM adaptability evaluation and the reasoning tree of evaluation index knowledge are constructed.(3)Based on case-based reasoning method,the structure,function design and evaluation decision-making process of case-based reasoning module(CBR module)in IEDSS-TBMSAT are proposed,and the corresponding analysis and calculation program is developed.(4)According to the functional requirement of TBM adaptability evaluation and the objective requirement of evaluation decision,the overall framework of IEDSS-TBMSAT and the functions of each component module are determined.IEDSS-TBMSAT evaluation decision process,decision support system model and related knowledge base,database and case base are designed.IEDSS-TBMSAT is developed by Java language programming.(5)The IEDSS-TBMSAT developed in this paper is verified by practical TBM engineering case,the recommended TBM model is reasonable and the evaluation result of tunneling adaptability is in accordance with the actual engineering,which indicates the feasibility and validity of IEDSS-TBMSAT.In addition,there is a positive correlation between the evaluation results of the target case and the source case,which indicatesthat IEDSS-TBMSAT has preliminary self-leaning ability.In conclusion,IEDSS-TBMSAT can be used to quantify TBM selection and tunneling adaptability under complex geological conditions,and has good engineering practical value.