| 摘要: |
| 针对胶凝砂砾石（CSG）抗压强度试验周期长、耗材大等问题，运用极度梯度提升树-长短期记忆网络（XGBoost-LSTM）组合模型对CSG抗压强度进行预测. 先选取相关性较强的“水泥含量”和“砂率”这2个输入变量代入XGBoost模型进行预测，并将结果与原特征一起代入LSTM模型；再采用94组抗压强度数据进行训练和验证. 结果表明：与基础模型XGBoost和LSTM相比，XGBoost-LSTM组合模型的决定系数分别提高5.6%和3.5%. 说明通过XGBoost模型构造新特征具有可行性，且XGBoost-LSTM组合模型能够对CSG抗压强度进行精准预测. |
| 关键词: 极度梯度提升树 长短期记忆网络 胶凝砂砾石 抗压强度 |
| DOI：10.3969/j.issn.1007-9629.2023.06.008 |
| 分类号:TV41 |
| 基金项目:“十四五”国家重点研发计划项目（2021YFC3001000）；国家自然科学基金资助项目（52109154） |
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| Prediction of Compressive Strength of Cementitious Sand and Gravel by XGBoost-LSTM |
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GUO Lei1,2, GAO Hang1, TIAN Qingqing1,3, GUO Lixia1,2, LI Zexuan1
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1.School of Water Conservancy， North China University of Water Resources and Electric Power， Zhengzhou 450046， China;2.Henan Key Laboratory of Water Environment Simulation and Treatment， North China University of Water Resources and Electric Power，Zhengzhou 450002， China;3.Water Resources Institute， China Institute of Water Resources and Hydropower Research， Beijing 100038， China
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| Abstract: |
| To solve the problems of too long compressive strength test cycle and too much material of cementitious sand and gravel （CSG） consumed， the model of extreme gradient boosting tree combined with long short-term memory network （XGBoost-LSTM） was used to predict the compressive strength of CSG. Firstly， the two input variables of “cement content” and “sand ratio” with strong correlation were selected into the XGBoost model for prediction， and the results were substituted into the LSTM model together with the original features. Another 94 sets of compressive strength data were used for training and validation. The results show that compared with the basic models XGBoost and LSTM， the coefficient of determination of the XGBoost-LSTM combined model is increased by 5.6% and 3.5% respectively. It has shown to be feasible to construct new features by the XGBoost model， and the XGBoost-LSTM combined model can accurately predict the compressive strength of CSG. |
| Key words: extreme gradient boosting tree （XGBoost） long short-term memory network（LSTM） cementitious sand and gravel （CSG） compressive strength |
