Constructing statistical models for arch dam deformation

被引:209
作者
Mata, Juan [1 ]
de Castro, Antonio Tavares [1 ]
da Costa, Jose Sa [2 ]
机构
[1] Natl Lab Civil Engn, Monitoring Div, Concrete Dams Dept, P-1700066 Lisbon, Portugal
[2] Univ Tecn Lisboa, IST, P-1049001 Lisbon, Portugal
关键词
structural safety control; concrete dam behavior; thermal effect; principal component analysis; quantitative interpretation model;
D O I
10.1002/stc.1575
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
In its lifetime, a dam can be exposed to significant water level variations and seasonal environmental temperature changes. The structural safety control of a concrete dam is supported by monitoring activities and is based on models. In practice, the interpretation of recorded concrete dam displacements is usually based on HST (hydrostatic, seasonal, time) statistical models. These models are widely used and consider that the thermal effect can be represented by a seasonal function. The main purpose of this paper is to present an HTT (hydrostatic, thermal, time) statistical model to interpret recorded concrete dam displacements. The idea is to replace the seasonal function with the use of recorded temperatures that better represent the thermal effect on dam behavior. Two new methodologies are presented for constructing HTT statistical models, both based on principal component analysis applied to recorded temperatures in the concrete dam body. In the first method, principal component analysis is used to choose the thermometers for the construction of the HTT model. In the second method, the thermal effect is represented by the principal components of temperature of selected thermometers. The advantage of these methods is that the thermal effect is represented by real temperature measured in the concrete dam body. The HTT statistical models proposed are applied to the 110m high Alto Lindoso arch dam, and the results are compared with the HST displacement model. Copyright (c) 2013 John Wiley & Sons, Ltd.
引用
收藏
页码:423 / 437
页数:15
相关论文
共 35 条
[21]   Hydrostatic, temperature, time-displacement model for concrete dams [J].
Leger, Pierre ;
Leclerc, Martin .
JOURNAL OF ENGINEERING MECHANICS-ASCE, 2007, 133 (03) :267-277
[22]  
Lombardi G., 2004, Advanced data interpretation for diagnosis of concrete dams
[23]   Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models [J].
Mata, J. .
ENGINEERING STRUCTURES, 2011, 33 (03) :903-910
[24]  
Montgomery DC., 2010, Applied statistics and probability for engineers
[25]   Multivariate statistics process control for dimensionality reduction in structural assessment [J].
Mujica, L. E. ;
Vehi, J. ;
Ruiz, M. ;
Verleysen, M. ;
Staszewski, W. ;
Worden, K. .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2008, 22 (01) :155-171
[26]   Damage diagnosis under environmental and operational variations using unsupervised support vector machine [J].
Oh, Chang Kook ;
Sohn, Hoon .
JOURNAL OF SOUND AND VIBRATION, 2009, 325 (1-2) :224-239
[27]  
Perner F, 2001, 6 BENCHM WORKSH NUM, P10
[28]  
Perner F, 2010, FRONTIERS ARCHITECTU, V4
[29]  
Saitta S., 2008, Data mining methodologies for supporting engineers during system identification
[30]  
Silveira A.F., 1961, Temperature variation in dams