"透明工作面"是实现智能化无人开采的关键,但现阶段的煤层三维地质模型精度较低,无法满足构建高精度煤层地理信息系统的要求。为此,提出了以采煤机历史截割数据和煤层三维地质模型数据根据不同方式划分出2种数据组合,利用长..."透明工作面"是实现智能化无人开采的关键,但现阶段的煤层三维地质模型精度较低,无法满足构建高精度煤层地理信息系统的要求。为此,提出了以采煤机历史截割数据和煤层三维地质模型数据根据不同方式划分出2种数据组合,利用长短期记忆(Long-Short Term Memory,LSTM)神经网络挖掘煤层厚度的变化规律并预测煤层厚度分布。基于LSTM神经网络和编码——解码长短期记忆(Encoder-Decoder Long-Short Term Memory,Encoder-Decoder LSTM)神经网络分别建立了煤层厚度预测模型。结果表明:在超参数未优化时,2种模型的煤层厚度预测结果误差均较大;通过优化两种模型的超参数,并以均方根误差(Root Mean Square Error,RMSE)作为煤层厚度预测的评估标准。在第1种数据组合方式下,LSTM模型和Encoder-Decoder LSTM模型的煤厚预测RMSE分别为0.05、0.044 m;在第2种数据组合方式下,2种模型的煤厚预测RMSE分别为0.051、0.049 m。为进一步对比2种模型预测结果,引入绝对误差,求取预测范围内各点的煤厚预测值与真实值的差值。最后得出,2种数据组合方式下,Encoder-Decoder LSTM模型的预测误差在各较小误差范围内的占比始终优于LSTM模型,Encoder-Decoder LSTM预测模型在预测煤层厚度上表现较好,精度较高,其预测的煤层厚度能够修正煤层地质模型。展开更多
The present work consists of dynamic detection of damages in reinforced concrete bridges by using a MMUM （mathematical model updating method） from incomplete test data. A well suited finite element model of a repair...The present work consists of dynamic detection of damages in reinforced concrete bridges by using a MMUM （mathematical model updating method） from incomplete test data. A well suited finite element model of a repaired bridge is carried out. The diagnosis enables us to locate and detect the damage in a reinforced concrete bridge. Thus, developments of analytical predictions have been checked by modal testing techniques. Besides, the FTCS （finite time centered space） scheme is developed to solve the set of equations which can easily handle finite element matrices of a bridge model. It is shown in this study that the method is applied to detect damages as well as existing cracks in real time of a repaired bridge. To check the efficiency of the method, the repaired bridge of OuedOumazer in Algeria has been selected. It is proven that identification methods have been able to detect the exact location of damage areas to be corrected avoiding the inaccuracy from the finite element model for the mass, stiffness and loading.展开更多
This paper presents an effective approach for updating finite element dynamic model from incomplete modal data identified from ambient vibration measurements.The proposed method is based on the relationship between th...This paper presents an effective approach for updating finite element dynamic model from incomplete modal data identified from ambient vibration measurements.The proposed method is based on the relationship between the perturbation of structural parameters such as stiffness and mass changes and the modal data measurements of the tested structure such as measured mode shape readings.Structural updating parameters including both stiffness and mass parameters are employed to represent the differences in structural parameters between the finite element model and the associated tested structure.These updating parameters are then evaluated by an iterative solution procedure,giving optimised solutions in the least squares sense without requiring an optimisation technique.In order to reduce the influence of modal measurement uncertainty,the truncated singular value decomposition regularization method incorporating the quasi-optimality criterion is employed to produce reliable solutions for the structural updating parameters.Finally,the numerical investigations of a space frame structure and the practical applications to the Canton Tower benchmark problem demonstrate that the proposed method can correctly update the given finite element model using the incomplete modal data identified from the recorded ambient vibration measurements.展开更多
文摘"透明工作面"是实现智能化无人开采的关键,但现阶段的煤层三维地质模型精度较低,无法满足构建高精度煤层地理信息系统的要求。为此,提出了以采煤机历史截割数据和煤层三维地质模型数据根据不同方式划分出2种数据组合,利用长短期记忆(Long-Short Term Memory,LSTM)神经网络挖掘煤层厚度的变化规律并预测煤层厚度分布。基于LSTM神经网络和编码——解码长短期记忆(Encoder-Decoder Long-Short Term Memory,Encoder-Decoder LSTM)神经网络分别建立了煤层厚度预测模型。结果表明:在超参数未优化时,2种模型的煤层厚度预测结果误差均较大;通过优化两种模型的超参数,并以均方根误差(Root Mean Square Error,RMSE)作为煤层厚度预测的评估标准。在第1种数据组合方式下,LSTM模型和Encoder-Decoder LSTM模型的煤厚预测RMSE分别为0.05、0.044 m;在第2种数据组合方式下,2种模型的煤厚预测RMSE分别为0.051、0.049 m。为进一步对比2种模型预测结果,引入绝对误差,求取预测范围内各点的煤厚预测值与真实值的差值。最后得出,2种数据组合方式下,Encoder-Decoder LSTM模型的预测误差在各较小误差范围内的占比始终优于LSTM模型,Encoder-Decoder LSTM预测模型在预测煤层厚度上表现较好,精度较高,其预测的煤层厚度能够修正煤层地质模型。
文摘The present work consists of dynamic detection of damages in reinforced concrete bridges by using a MMUM （mathematical model updating method） from incomplete test data. A well suited finite element model of a repaired bridge is carried out. The diagnosis enables us to locate and detect the damage in a reinforced concrete bridge. Thus, developments of analytical predictions have been checked by modal testing techniques. Besides, the FTCS （finite time centered space） scheme is developed to solve the set of equations which can easily handle finite element matrices of a bridge model. It is shown in this study that the method is applied to detect damages as well as existing cracks in real time of a repaired bridge. To check the efficiency of the method, the repaired bridge of OuedOumazer in Algeria has been selected. It is proven that identification methods have been able to detect the exact location of damage areas to be corrected avoiding the inaccuracy from the finite element model for the mass, stiffness and loading.
文摘This paper presents an effective approach for updating finite element dynamic model from incomplete modal data identified from ambient vibration measurements.The proposed method is based on the relationship between the perturbation of structural parameters such as stiffness and mass changes and the modal data measurements of the tested structure such as measured mode shape readings.Structural updating parameters including both stiffness and mass parameters are employed to represent the differences in structural parameters between the finite element model and the associated tested structure.These updating parameters are then evaluated by an iterative solution procedure,giving optimised solutions in the least squares sense without requiring an optimisation technique.In order to reduce the influence of modal measurement uncertainty,the truncated singular value decomposition regularization method incorporating the quasi-optimality criterion is employed to produce reliable solutions for the structural updating parameters.Finally,the numerical investigations of a space frame structure and the practical applications to the Canton Tower benchmark problem demonstrate that the proposed method can correctly update the given finite element model using the incomplete modal data identified from the recorded ambient vibration measurements.
