基于数据挖掘的房屋建筑健康态势预测
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摘要
为了预防和降低房屋沉降不均等因素造成房屋坍塌事件,需要对房屋建筑健康态势进行预测。采用传统预测方法对房屋建筑健康态势进行预测时,忽略了房屋建筑沉降值的修正,导致预测结果准确率低、预测效率低的问题。针对以上问题,提出一种基于数据挖掘的房屋建筑健康态势预测方法。采用预测模型对房屋建筑各个部分的沉降进行组合预测,通过BP神经网络对组合模型预测得到的沉降值进行修正,根据修正后的房屋建筑沉降值完成对房屋建筑健康态势预测。实验结果表明,所提方法预测结果准确率高、预测效率高。
In order to prevent and reduce the collapse of houses caused by uneven settlement of houses,it is necessary to predict the health situation of building construction. When traditional forecasting methods predict the health of building construction,Ignore the correction of the settlement value of the building,lead to low accuracy of prediction results,poor prediction efficiency. Aiming at the above problems,this paper proposes a method prediction of housing construction health based on data mining. The prediction model is used to combination prediction the settlement of each part of the building. The settlement value predicted by the combined model is corrected by BP neural network. According to the revised settlement value of the building,the health situation prediction of the building is completed.The experimental results show that the proposed method has high accuracy and high prediction efficiency.
In order to prevent and reduce the collapse of houses caused by uneven settlement of houses,it is necessary to predict the health situation of building construction. When traditional forecasting methods predict the health of building construction,Ignore the correction of the settlement value of the building,lead to low accuracy of prediction results,poor prediction efficiency. Aiming at the above problems,this paper proposes a method prediction of housing construction health based on data mining. The prediction model is used to combination prediction the settlement of each part of the building. The settlement value predicted by the combined model is corrected by BP neural network. According to the revised settlement value of the building,the health situation prediction of the building is completed.The experimental results show that the proposed method has high accuracy and high prediction efficiency.
引文
[1]章红兵,范凡,胡昊.考虑基坑开挖空间效应的邻近建筑物沉降预测方法[J].上海交通大学学报,2016,50(4):641-646.
[2]丁智,朱少杰,秦建设,等.邻近建筑物盾构隧道开挖引起的地表沉降预测研究[J].现代隧道技术,2016,53(4):77-83.
[3]杨彪,李慧民,孟海,等.基于GM-ANN模型的建筑物沉降量变化趋势预测方法[J].中国安全生产科学技术,2016,12(10):149-153.
[4]朱军桃,李亚威,熊东旭,等.建筑物沉降监测中的改进灰色模型[J].测绘科学,2017,42(11):85-91.
[5]戴永祥.基于ESMD与ELM组合模型的地基沉降预测[J].建筑技术,2017,48(9):980-982.
[6]傅鹤林,李鲒,王洪涛,等.基于随机介质理论的浅埋偏压隧道沉降预测[J].铁道工程学报,2017,34(9):70-76.
[7]曹净,丁文云,赵党书,等.基于LSSVM-ARMA模型的复合地基沉降时间序列预测[J].控制工程,2016,23(2):190-196.
[2]丁智,朱少杰,秦建设,等.邻近建筑物盾构隧道开挖引起的地表沉降预测研究[J].现代隧道技术,2016,53(4):77-83.
[3]杨彪,李慧民,孟海,等.基于GM-ANN模型的建筑物沉降量变化趋势预测方法[J].中国安全生产科学技术,2016,12(10):149-153.
[4]朱军桃,李亚威,熊东旭,等.建筑物沉降监测中的改进灰色模型[J].测绘科学,2017,42(11):85-91.
[5]戴永祥.基于ESMD与ELM组合模型的地基沉降预测[J].建筑技术,2017,48(9):980-982.
[6]傅鹤林,李鲒,王洪涛,等.基于随机介质理论的浅埋偏压隧道沉降预测[J].铁道工程学报,2017,34(9):70-76.
[7]曹净,丁文云,赵党书,等.基于LSSVM-ARMA模型的复合地基沉降时间序列预测[J].控制工程,2016,23(2):190-196.