LM-CDBN超高层变形预测模型的构建与应用

邱冬炜; 王彤; 段明旭; 罗德安; 王来阳

doi:10.3969/j.issn.0258-2724.20180293

LM-CDBN超高层变形预测模型的构建与应用

doi: 10.3969/j.issn.0258-2724.20180293

邱冬炜^1,2,,
王彤^1,,
段明旭¹,
罗德安^1,2, ,,
王来阳¹

基金项目: 国家重点研发计划资助项目（2017YFB0503700）；中国住房和城乡建设部科学技术计划资助项目（2015-K8-050）

详细信息

作者简介:
邱冬炜（1978—），男，副教授，博士，研究方向为建构筑物变形监测与结构检测、精密工程测量与数据处理，E-mail：qiudw@bucea.edu.cn

通讯作者:
罗德安（1968—），男，教授，博士，研究方向为工程测量、智能测绘技术，E-mail：luodean@bucea.edu.cn

中图分类号: P258
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- 被引次数: 0
出版历程
- 收稿日期: 2018-05-15
- 修回日期: 2018-06-06
- 网络出版日期: 2018-07-08
- 刊出日期: 2020-04-01

Construct and Application of LM-CDBN Deformation Prediction Model for Supertall Buildings

QIU Dongwei^{1,2
,},
WANG Tong^1
,,
DUAN Mingxu¹,
LUO Dean^{1,2
, ,},
WANG Laiyang¹

摘要

摘要: 为提高超高层建筑变形预测精度，对附有条件的深度信念网络（conditional deep belief network，CDBN）模型中权值及阈值调整方法进行了改进，使用LM （Levenberg-Marquardt）算法作为新的模型定权机制，构建了LM-CDBN网络模型；将构建的LM-CDBN超高层变形预测模型应用于一座298 m超高层建筑中；然后用训练误差、预测值拟合度、预测结果稳定性组成的综合评价体系对模型进行了评价；最后，将LM-CDBN模型分别与深度信念模型（deep belief network，DBN）、极限学习机（extreme learning machine，ELM）、基于无迹卡尔曼滤波的支持回归向量机（unscented Kalman filter-support vector regression，UKF-SVR）进行了预测结果对比. 结果表明：在超高层建筑的变形预测中，相比DBN、ELM和UKF-SVR，LM-CDBN预测精度分别提升了32%、55%及24%，模型的信息提取稳定性及处理时变系统非线性问题的泛化能力得到了提高.
- 深度信念网络 /
- 变形 /
- 预测 /
- 数据处理 /
- 参数估计
Abstract: In order to improve the prediction accuracy of supertall building deformation, the method of adjusting the weight and threshold in the conditional deep belief network (CDBN) model was improved. The LM (Levenberg-Marquardt) algorithm was used as a weighting method to construct the LM-CDBN network model. This method was applied to the deformation prediction of a 298 m supertall building. Then, the model was fully evaluated in terms of training error, goodness of fit, and prediction stability. Finally, the prediction results of LM-CDBN model, deep belief network (DBN) model, extreme learning machine (ELM) and unscented Kalman filter-support vector regression (UKF-SVR) were compared. The result shows that the prediction performance of LM-CDBN was 32%, 55% and 24% higher than three other models respectively. LM-CDBN model improves in the information extraction stability and generalization ability of solving nonlinear problems in time-varying systems.
- deep belief networks /
- deformation /
- prediction /
- data processing /
- parameter estimation

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图 1 第1层隐含层重构误差

Figure 1. Reconstruction error of the first hidden layer

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图 2 第2～4层重构误差

Figure 2. Reconstruction errors of the second to the fourth hidden layers

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图 3 评价标准误差统计

Figure 3. Evaluation standard errors

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图 4 R统计

Figure 4. Summary of R

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表 1 A1办公楼监测数据表

Table 1. Monitoring data of A1 office building

序号	时间	位移变形值/mm	温度/℃	风速/（m•s⁻¹）	光照强度/Lx
1	2016-08-23 00：00	28.281	24.1	5.9	2.351
2	2016-08-23 06：00	27.556	23.3	5.4	20.307
3	2016-08-23 12：00	28.428	24.2	5.5	434.243
4	2016-08-23 18：00	29.779	24.9	5.4	267.839
5	2016-08-24 00：00	30.250	24.8	4.9	4.299
6	2016-08-24 06：00	27.895	24.1	5.2	37.456
$\vdots $	$\vdots $	$\vdots $	$\vdots $	$\vdots $	$\vdots $
139	2016-09-26 12：00	49.753	31.9	6.7	780.412
140	2016-09-26 18：00	49.341	27.6	7.5	413.485

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表 2 预测结果对比表

Table 2. Prediction comparison

观测时间	$\hat y/{\rm{mm}}$	CDBN		LM-CDBN		ELM		UKF- SVR
观测时间	$\hat y/{\rm{mm}}$	预测值/mm	X_RE/%	预测值/mm	X_RE/%	预测值/mm	X_RE/%	预测值/mm	X_RE/%
2016-08-24 12：00	47.510	45.369	4.51	46.214	2.73	41.535	12.58	52.683	10.89
2016-08-24 18：00	46.653	45.173	3.17	45.537	2.39	40.083	14.08	44.297	5.05
2016-08-25 00：00	46.025	44.345	3.65	45.027	2.17	43.629	5.21	46.913	1.93
2016-08-25 06：00	46.769	44.220	5.45	45.626	2.44	45.756	2.17	48.587	3.89
2016-08-25 12：00	46.385	44.342	4.40	45.315	2.31	49.168	6.00	45.179	2.60
2016-08-25 18：00	48.308	46.040	4.69	46.829	3.06	47.051	2.60	50.576	4.69
2016-08-26 00：00	49.739	48.576	2.34	47.891	3.72	44.774	9.98	52.940	6.44
2016-08-26 06：00	52.424	47.736	8.94	49.742	5.12	46.998	10.35	51.630	1.51
2016-08-26 12：00	49.753	47.547	4.43	47.906	3.71	48.372	2.78	48.568	2.38
2016-08-26 18：00	49.341	47.360	4.01	47.603	3.52	47.187	4.37	50.018	1.37
X_ARE/%			4.56		3.12		7.01		4.08

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表 3 预测结果评价表

Table 3. Evaluation for different prediction results

评价标准	CDBN	LM-CDBN	ELM	UKF-SVR
X_MSE/mm	4.397 0	2.559 0	15.559 0	5.570 0
X_MRE/mm	0.023 9	0.004 7	0.059 1	0.031 4
X_MAE/mm	0.014 0	0.003 5	0.033 3	0.017 5
X_RMSE/mm	0.017 1	0.009 5	0.041 3	0.021 3
R%	97	98	92	96

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