Adaptive neural networks for model updating of structures
Adaptive neural networks for model updating of structures - datinglatevgo ru
However, this assumption is rarely satisfied for civil infrastructure (Pasquier et al., 2014).
Unfortunately, there are many studies and theoretical proposals found in the literature (Ben-Haim and Hemez, 2012) that have not involved testing with full-scale systems.
Replacement of all aging infrastructure is expensive, unsustainable, and often not necessary.
Models that are used for design of civil infrastructure are justifiably conservative.
Therefore, most structures possess reserve capacity and can last well beyond their design working lives (referred to as service lives in this paper) (Brühwiler, 2012; Smith, 2016).
The challenge lies in quantifying this reserve capacity to enable asset-management decision making such as repair, retrofit, and extension.
Such inaccuracy can result in misinformed asset-management decisions.
The success of data-interpretation methodologies is best measured on full-scale examples. (2011) have noted difficulties in transfer of technology from the laboratory to the field.
A detailed 3D finite-element plate and beam model of the bridge and weigh-in-motion data are used to obtain the time–stress response at a fatigue critical location along the bridge span.
The time–stress response, presented as a histogram, is compared to measured strain responses either to update prior knowledge of model parameters using residual minimization and Bayesian methodologies or to obtain candidate model instances using the EDMF methodology.
However, analytical models of civil infrastructure systems possess large modeling uncertainty, including significant systematic errors and unknown correlations between measurement locations (Jiang and Mahadevan, 2008).
These conditions have lead to recent studies of uncertainties and development of data-interpretation methodologies that are robust to incomplete knowledge (Goulet and Smith, 2013).
Kuok and Yuen (2016) have studied modal identification of the Ting Kau Bridge, which is monitored with more than 230 sensors of various types.