| 临近基坑建筑物沉降神经网络动态预报 |
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| 引用本文： | 张尚根,李刻铭,陈玲.临近基坑建筑物沉降神经网络动态预报[J].低温建筑技术,2012,34(11):127-129. |
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| 作者姓名： | 张尚根 李刻铭 陈玲 |
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| 作者单位： | 解放军理工大学工程兵工程学院,南京,210007 |
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| 摘 要： | 将BP神经网络与遗传算法结合,建立了建筑物沉降的动态预报模型。实例分析表明所建立的模型预测精度较高,预报值与实测值吻合较好,该方法对建筑物沉降的实时预报有一定的实用性。
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| 关 键 词： | 建筑物沉降 BP神经网络 遗传算法 全局优化 |
DYNAMICAL PREDICTIONG OF BUILDING SUBSIDENCE COURSE USING ARTIFICIAL NEURAL NETWORKS IN DEEP FOUNDATION EXCAVATION |
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| ZHANG Shang-gen
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LI Ke-ming
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CHEN Ling.DYNAMICAL PREDICTIONG OF BUILDING SUBSIDENCE COURSE USING ARTIFICIAL NEURAL NETWORKS IN DEEP FOUNDATION EXCAVATION[J].Low Temperature Architecture Technology,2012,34(11):127-129. |
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| Authors: | ZHANG Shang-gen LI Ke-ming CHEN Ling |
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| Affiliation: | ( Engi. Institute of Corps of Engi. , PLA Univ. of Science and Technology, Nanjing 210007, China) |
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| Abstract: | A dynamical predication model for building subsidence course in deep excavation and an effective predicting method is put forward by improved artificial neural networks combining BP neural network with genetic algorithm. The resuh indicates that the model proposed here has fast approximation and high precision, the predicted values agree well with the measured ones. The proposed method is a useful tool for deformation prediction. |
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| Keywords: | building subsidence course BP neural network genetic algorithm global optimal |
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