Pang Haotian, Qi Wenyue, Song Hongqi, Pang Haowei, Liu Xiaotian, Chen Junzhi, Chen Zhiwei
Hebei Province Engineering Research Center for Harmless Synergistic Treatment and Recycling of Municipal Solid Waste, Yanshan University, Qinhuangdao 066004, China.
Key Laboratory of Xinjiang Coal Resources Green Mining, Ministry of Education, Xinjiang Institute of Engineering, Urumqi 830023, China.
Materials (Basel). 2025 Mar 11;18(6):1236. doi: 10.3390/ma18061236.
This study utilizes machine learning (ML) techniques to predict the performance of slag-based cemented tailings backfill (CTB) activated by soda residue (SR) and calcium carbide slag (CS). An experimental database consisting of 240 test results is utilized to thoroughly evaluate the accuracy of seven ML techniques in predicting the properties of filling materials. These techniques include support vector machine (SVM), random forest (RF), backpropagation (BP), genetic algorithm optimization of BP (GABP), radial basis function (RBF) neural network, convolutional neural network (CNN), and long short-term memory (LSTM) network. The findings reveal that the RBF and SVM models demonstrate significant advantages, achieving a coefficient of determination () of approximately 0.99, while the for other models ranges from 0.86 to 0.98. Additionally, a dynamic growth model to predict strength is developed using ML techniques. The RBF model accurately predicts the time required for filling materials to reach a specified strength. In contrast, the BP, SVM, and CNN models show delays in predicting this curing age, and the RF, GABP, and LSTM models tend to overestimate the strength of the filling material when it approaches or fails to reach 2 MPa. Finally, the RBF model is employed to perform coupling analysis on filling materials with various mix ratios and curing ages. This analysis effectively predicts the changes in filling strength over different curing ages and raw material contents, offering valuable scientific support for the design of filling materials.
本研究利用机器学习（ML）技术预测由碱渣（SR）和电石渣（CS）激发的矿渣基胶结尾矿充填体（CTB）的性能。利用一个由240个测试结果组成的实验数据库，全面评估了七种ML技术预测充填材料性能的准确性。这些技术包括支持向量机（SVM）、随机森林（RF）、反向传播（BP）、BP的遗传算法优化（GABP）、径向基函数（RBF）神经网络、卷积神经网络（CNN）和长短期记忆（LSTM）网络。研究结果表明，RBF和SVM模型具有显著优势，决定系数（）约为0.99，而其他模型的决定系数在0.86至0.98之间。此外，利用ML技术建立了预测强度的动态增长模型。RBF模型准确预测了充填材料达到规定强度所需的时间。相比之下，BP、SVM和CNN模型在预测该养护龄期时出现延迟，而RF、GABP和LSTM模型在充填材料强度接近或未达到2 MPa时往往高估其强度。最后，采用RBF模型对不同配合比和养护龄期的充填材料进行耦合分析。该分析有效地预测了不同养护龄期和原材料含量下充填强度的变化，为充填材料的设计提供了有价值的科学支持。
