| 基于A-BP神经网络的软土基坑开挖地表沉降预测 |
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| 引用本文： | 黄震, 赵奎, 许宏伟, 王晓军, 钟文. 2017: 基于A-BP神经网络的软土基坑开挖地表沉降预测. 工程地质学报, 25(s1): 445-451. DOI: 10.13544/j.cnki.jeg.2017.s1.069 |
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| 作者姓名： | 黄震 赵奎 许宏伟 王晓军 钟文 |
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| 作者单位： | 1.江西理工大学资源与环境工程学院 赣州 341000;;2.中国矿业大学, 深部岩土力学与地下工程国家重点实验室 徐州 221116;;3.南京大学地球科学与工程学院 南京 210093 |
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| 基金项目： | 中国矿业大学深部岩土力学与地下工程国家重点实验室开放基金项目（SKLGDUEK1703），江西省自然科学基金（20171BAB206022），江西省教育厅科学技术研究项目（GJJ160675），江西理工大学博士启动基金项目（jxxjbs17005）资助 |
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| 摘 要： | 基坑开挖引起的周围地表沉降势必影响周边道路和建筑物等基础设施的安全与稳定，准确预测基坑开挖地表沉降具有重要意义。针对BP神经网络预测方法效率低、精度小等缺陷，采用模拟退火法的全局寻优原理对数据进行处理优化，提高BP神经网络预测方法的效率和精度，在大量软土基坑开挖现场监测数据的基础上，构建了基于模拟退火法的SA-BP神经网络预测模型对软土基坑开挖墙后最大地表沉降进行预测，并结合正态、偏态模式及统计分析计算方法进行对比和验证。研究结果表明：SA-BP神经网络预测方法的结果与实测吻合较好，该方法预测精度较高，可有效地运用于基坑开挖地表沉降的预测分析中。
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| 关 键 词： | 基坑开挖 地表沉降 BP神经网络 统计分析 |
| 收稿时间： | 2017-05-26 |
| 修稿时间： | 2017-07-15 |
PREDICTION OF SURFACE SETTLEMENT INDUCED BY SOFT SOIL EXCAVATION BASED ON SA-BP NEURAL NETWORKS |
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| HUANG Zhen, ZHAO Kui, XU Hongwei, WANG Xiaojun, ZHONG Wen. 2017: PREDICTION OF SURFACE SETTLEMENT INDUCED BY SOFT SOIL EXCAVATION BASED ON SA-BP NEURAL NETWORKS. JOURNAL OF ENGINEERING GEOLOGY, 25(s1): 445-451. DOI: 10.13544/j.cnki.jeg.2017.s1.069 |
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| Authors: | HUANG Zhen ZHAO Kui XU Hongwei WANG Xiaojun ZHONG Wen |
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| Affiliation: | 1.School of Resources and Environment Engineering, Jiangxi University of Science and Technology, Ganzhou 341000;;2.Sate Key Laboratory for Geomechanics & Deep Underground Engineering, China University of Mining and Technology, Xuzhou 221116;;3.School of Earth Sciences and Engineering, Nanjing University, Nanjing 210093 |
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| Abstract: | Based on the facts that BP neural networks have some flaws, such as characteristics of low efficiency and low accuracy. A new BP neural networks was put forward to forecast the surface settlement of pits, which depend on Simulated Annealing. The kernel functions was determined by the theory of optimization of overall situation, which could improve the efficiency and accuracy of BP neural networks. According to the typical pits excavation cases in Shanghai, the SA-BP neural networks that based on Simulated Annealing was built to forecast the surface settlement of pits. And the result was juxtaposed with normal distribution and skew distribution and the results of statistical analysis. The result shown that the results obtained by SA-BP neural networks were closer to reality than others, the error was low. And the method can be used to analysis and forecast the surface settlement of pits. |
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| Keywords: | Pit excavation Surface settlement BP neural networks Statistical analysis |
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