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A platform for research: civil engineering, architecture and urbanism
Foundation pit deformation prediction method, medium and system
The invention provides a foundation pit deformation prediction method, medium and system, and belongs to the technical field of building foundation pit deformation prediction.The foundation pit deformation prediction method comprises the steps that a preliminary shallow stratum geologic model and an initial finite element model are established, constrained inversion is conducted on the preliminary geologic model, and a first geologic model is obtained; forming a fused seismic attribute profile; inputting a fused seismic attribute profile, and identifying and marking an easy-to-displace region; calculating an easy-to-deform area of the foundation pit and a deformation critical threshold thereof; determining the horizontal displacement of the diaphragm wall corresponding to the plurality of groups of deformation critical thresholds as a neural network training sample by adopting an orthogonal test design method; taking the horizontal displacement of the diaphragm wall as input and the deformation critical threshold as output, training an extreme learning machine neural network prediction model, inputting monitoring data into the trained foundation pit deformation prediction model to obtain a foundation pit deformation critical threshold in the construction process, and inputting the predicted foundation pit deformation critical threshold into an initial finite element model to obtain the foundation pit deformation critical threshold in the construction process. And calculating to obtain and output the deformation and trend of the foundation pit in a future period of time.
本发明提供了一种基坑变形预测方法、介质及系统，属于建筑基坑变形预测技术领域，包括：建立初步浅部地层地质模型和初始有限元模型，对初步地质模型进行约束反演，得到第一地质模型；形成融合地震属性剖面；输入融合地震属性剖面，识别并标记出易位移区域；计算基坑易变形区域及其变形临界阈值；采用正交试验设计法，确定多组变形临界阈值对应的地连墙水平位移作为神经网络训练样本；以地连墙水平位移为输入，变形临界阈值为输出，训练极限学习机神经网络预测模型，将监测数据输已训练好的基坑变形预测模型，得到施工过程中的基坑变形临界阈值，将预测的基坑变形临界阈值输入初始有限元模型，计算得到未来一段时间内基坑的变形量及趋势并输出。
Foundation pit deformation prediction method, medium and system
The invention provides a foundation pit deformation prediction method, medium and system, and belongs to the technical field of building foundation pit deformation prediction.The foundation pit deformation prediction method comprises the steps that a preliminary shallow stratum geologic model and an initial finite element model are established, constrained inversion is conducted on the preliminary geologic model, and a first geologic model is obtained; forming a fused seismic attribute profile; inputting a fused seismic attribute profile, and identifying and marking an easy-to-displace region; calculating an easy-to-deform area of the foundation pit and a deformation critical threshold thereof; determining the horizontal displacement of the diaphragm wall corresponding to the plurality of groups of deformation critical thresholds as a neural network training sample by adopting an orthogonal test design method; taking the horizontal displacement of the diaphragm wall as input and the deformation critical threshold as output, training an extreme learning machine neural network prediction model, inputting monitoring data into the trained foundation pit deformation prediction model to obtain a foundation pit deformation critical threshold in the construction process, and inputting the predicted foundation pit deformation critical threshold into an initial finite element model to obtain the foundation pit deformation critical threshold in the construction process. And calculating to obtain and output the deformation and trend of the foundation pit in a future period of time.
本发明提供了一种基坑变形预测方法、介质及系统，属于建筑基坑变形预测技术领域，包括：建立初步浅部地层地质模型和初始有限元模型，对初步地质模型进行约束反演，得到第一地质模型；形成融合地震属性剖面；输入融合地震属性剖面，识别并标记出易位移区域；计算基坑易变形区域及其变形临界阈值；采用正交试验设计法，确定多组变形临界阈值对应的地连墙水平位移作为神经网络训练样本；以地连墙水平位移为输入，变形临界阈值为输出，训练极限学习机神经网络预测模型，将监测数据输已训练好的基坑变形预测模型，得到施工过程中的基坑变形临界阈值，将预测的基坑变形临界阈值输入初始有限元模型，计算得到未来一段时间内基坑的变形量及趋势并输出。
Foundation pit deformation prediction method, medium and system
一种基坑变形预测方法、介质及系统
YANG WENHUA (author) / LI YANGYANG (author) / WANG CHUNGE (author) / SUN FANGYUAN (author) / YIN JILONG (author) / ZHU DELIANG (author) / PAN WENBANG (author)
2024-10-18
Patent
Electronic Resource
Chinese
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