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吳   菡

 

（ 鎮江市京口區農業農村局 ，江蘇 鎮江 212000)

摘   要 ：隨著采礦 、水利、交通等領域向著深部發展，工程中巖爆問題頻發 ，嚴重危害人員安全， 因此建立準確有效的巖爆預測模型至關重要。結合多種優化算法得到模型最優參數，構建了3 種基于支持向量機 (SVR)  理論的巖爆預測模型。依157 組國內外實測巖爆案例，以模型預測準確率為識別框架，綜合新的模型預測結果評價指標（平均偏差）分析預測模型性能 ，使用數值模擬和工程應用兩種方式，驗證模型有效性。結果表明： 隨著輸入參數類別的增加，模型預測準確率也隨著增加，遺傳算法  (GA)  是 、粒子群算法  (PSO) 和麻雀搜索算法(SSA)對支持向量機有一定優化效果 ，麻雀搜索算法優化效果最佳，故基于優化的 SVR 巖爆預測模型是可靠有效的。

關鍵詞：地下工程 ；巖爆 ；遺傳算法  (GA) ;  粒子群算法  (PSO) ;  麻雀搜索算法  (SSA) ;  支持向量機

中圖分類號：TU45           

文獻標志碼：A            

文章編號： 1005- 8249   (2024)  04- 0092- 06

 DOI:10. 19860/j.cnki.issn1005 - 8249.2024.04.016

 

WU Han

(Agricultural and Rural Bureau of Jingkou District ,  Zhenjiang    212000 ,  China)

Abstract：With the development of mining, water conservancy, transportation and other fields toward deeper areas, rock burst problems occur frequently in engineering, which seriously endanger the safety of personnel, so it is crucial to establish an accurate and effective rock burst prediction model. A rock burst prediction model based on support vector machine theory is constructed, and the optimal parameters of the model are obtained by combining the sparrow search algorithm. Relying on 157 groups of domestic and foreign measured rock burst cases, the model prediction accuracy is used as the identification framework, and the new model prediction results evaluation index (mean deviation) is integrated to analyze the prediction model performance, and both numerical simulation and engineering application are used to verify the effectiveness of the SSA-SVR model. The results show that the model prediction accuracy increases with the increase of input parameter categories. Genetic algorithm (GA), particle swarm algorithm (PSO) and sparrow search algorithm (SSA) have some optimization effect on support vector machines ,so the optimized SVR rock burst prediction model is reliable and effective.

Keywords： underground space；rock burst;genetic algorithm (GA)；particle swarm optimization (PSO)；sparrow search algorithm (SSA)；support vector machine 

作者簡介： 吳    菡  (1996—) ,   女 ，碩士 ，助理工程師 ，研 究方向：水利工程管理。

收稿日期：2022- 12- 21