CAJ | 학술논문

矿建工程投资额大、周期长、不确定性大的特点，决定了投资估算快速性和准确性对投资决策至关重要。在分析投资构成的基础上，建立基于10个估算子系统的投资估算模型，以14条井底平巷为例，选取断面大小、支护方式、锚杆消耗等技术、经济指标作为工程特征，对其进行量化和归一化处理，用Matlab提供的神经网络函数构建三层、7个输入指标、一个输出指标的BPNN模型来预测巷道工程投资估算值。结果表明，只要工程特征选取合适及BPNN模型的参数设置准确，神经网络方法能较好较快地达到目标，预测精度在±10％以内，能够满足估算预测快速性和准确性的要求。
광건공정투자액대、주기장、불학정성대적특점，결정료투자고산쾌속성화준학성대투자결책지관중요。재분석투자구성적기출상，건립기우10개고산자계통적투자고산모형，이14조정저평항위례，선취단면대소、지호방식、묘간소모등기술、경제지표작위공정특정，대기진행양화화귀일화처리，용Matlab제공적신경망락함수구건삼층、7개수입지표、일개수출지표적BPNN모형래예측항도공정투자고산치。결과표명，지요공정특정선취합괄급BPNN모형적삼수설치준학，신경망락방법능교호교쾌지체도목표，예측정도재±10％이내，능구만족고산예측쾌속성화준학성적요구。
Mine construction has the characteristics of huge investment ,long cycle ,large uncertainty , which determine the speed and accuracy of the investment estimate for investment decisions is essential . Established investment estimation model based on 10 estimates subsystems , for example bottom drift , selecting the section size , supporting way , rock bolt consumption and other technical and economic indicators as engineering features , making quantification and normalization , provided neural network function with Matlab to build BPNN (Back Propagation Neural Network)model for predicting the roadway project investment ,the model included three layers ,seven input indicators ,one output indicators .The results show that ,as long as the selecting of project characteristics and BPNN model parameter settings were appropriate and accurate ,the neural network method can quickly achieve the target ,the prediction accuracy could reach ± 10% or less ,meeting requirements of estimates predict rapidity and accuracy .