(北京科技大學(xué) 土木與資源工程學(xué)院，北京 100083)
摘 要: 礦山充填系統(tǒng)在試運(yùn)行期間的充填料漿配合比變化較大，如何快速準(zhǔn)確地獲得井下各采場的充填體強(qiáng)度對相鄰采場的安全開采來說至關(guān)重要。本文首先以料漿濃度(質(zhì)量分?jǐn)?shù))、水泥摻量、人工砂尾砂比和養(yǎng)護(hù)時間作為輸入因子，以室內(nèi)實驗充填體單軸抗壓強(qiáng)度SL作為輸出因子，建立了一種ANN-PSO預(yù)測模型。然后定義了充填體強(qiáng)度預(yù)測折減系數(shù)k的概念，并通過對比大量相同配合比下的室內(nèi)實驗充填體強(qiáng)度值SL和實際生產(chǎn)充填體強(qiáng)度值SE，計算獲得了兩者之間的k值。該模型對室內(nèi)實驗充填體強(qiáng)度值SL的預(yù)測性能較好，在預(yù)測時其平均相對誤差為2.41%，可決系數(shù)R2為0.992。采用所建模型并聯(lián)合強(qiáng)度折減系數(shù)k，成功預(yù)測并分析了某礦山井下263條進(jìn)路內(nèi)充填體的實際生產(chǎn)測定值SE，為開采充填采場相鄰礦體的支護(hù)工作提供了及時有效的指導(dǎo)。
關(guān)鍵字: 充填體強(qiáng)度；智能預(yù)測；人工神經(jīng)網(wǎng)絡(luò)；粒子群算法; 強(qiáng)度折減
（School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China）
Abstract:Backfill mix proportion changes greatly during the test running of backfill system, thus, obtaining the backfill strength of particular stopes accurately and quickly plays an important role in the safety of mining in adjacent stopes. This paper firstly established an ANN-PSO intelligent prediction model by taking slurry density, cement dosage, ratio of artificial aggregate and tailings and curing time as input factors, and uniaxial compressive strength of laboratory backfill as output factor. Subsequently, the concept of predicted strength reduction coefficient of backfill was defined, and the strength reduction coefficient k was obtained by comparing the backfill strength of laboratory experiments and backfill strength of actual production under the same mix proportion. The results show that the model reveals a good prediction performance for the backfill strength of laboratory experiments, with a mean relative error (EMR) of 2.41% and a determination coefficient (R2) of 0.992. Based on the ANN-PSO model and strength reduction coefficient k, the backfill strengths of actual production of 263 access during the running period are predicted and analyzed, which provide timely and effective guidance for the support works of mining in adjacent stopes.
Key words: strength of backfilling body; intelligent prediction; artificial neural network; particle swarm optimization; strength reduction
