基于SSA-DBN的隧道爆破效果的預(yù)測(cè)

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Prediction of Tunnel Blasting Outcomes Based on SSA-DBN

SHI Long①， CUI Dayong①， LILong（2） ， CHEN Di③， ZHOU Changchun① ① 1st Engineering Co.，Ltd.，China Railway Construction Bridge Enginering Bureau Group（Liaoning Dalian，16000） （204 ② School of Civil Engineering，Xi'an University of Architecture and Technology（Shaanxi Xi'an，710055） ③ Hubei Jiaotou Yichu Construction Management Co.，Ltd.（Hubei Yichang，443200）

[ABSTRACT]A prediction study on tunnel blasting outcomes was conducted using the Qilinguan Tunnel project as an example.SSA-DBN prediction model based on sparrow search algorithm（SSA）optimized deep belief network （DBN） was used.Using theselected eight parameters thatafect theblastingoutcomes as inputindicators，and theaverageabsoluteerror EMA ，mean square error EMS ，and determination coefficient of R2 as evaluation indicators，a comparative evaluation was conducted on theoutput indicators（maximum linear over excavation，under excavation and fragmentation）of DBN model， principal component analysis（PCA）optimized DBN model（PCA-DBN），and SSA-DBN model.The results show that R2 （204 of the maximum linearover excavation，under excavation，and fragmentationof SSA-DBNmodel is O.9973，0.9977，and 0.998 1，respectively. EMA is 0.461 0， 0.338 0，and 0.360 2，respectively. EMS is 0.297 5， 0.178 2，and 0.175 3，respectively.SSA-DBN model has the highestfiting degree between predicted values andactual values，folowed by DBN model，and PCA-DBN model has the lowest. The sensitivity index r2 of input parameters to blasting outcomes is mainly between 0.6 and O.7. The accuracy and stability of SSA-DBN model have been verified.

[KEYWORDS]blasting engineering；DBN neural network；sparow search algorithm；prediction of blasting outcome

0 引言

山嶺隧道常采用鉆爆法進(jìn)行掘進(jìn)施工，爆破效果是隧道掘進(jìn)效率和安全性的重要體現(xiàn)[1-3]。（剩余10742字）