Active learning structural model updating of a multisensory system based on Kriging method and Bayesian inference

Abstract

Model updating techniques are often applied to calibrate the numerical models of bridges using structural health monitoring data. The updated models can facilitate damage assessment and prediction of responses under extreme loading conditions. Some researchers have adopted surrogate models, for example, Kriging approach, to reduce the computations, while others have quantified uncertainties with Bayesian inference. It is desirable to further improve the efficiency and robustness of the Kriging-based model updating approach and analytically evaluate its uncertainties. An active learning structural model updating method is proposed based on the Kriging method. The expected feasibility learning function is extended for model updating using a Bayesian objective function. The uncertainties can be quantified through a derived likelihood function. The case study for verification involves a multisensory vehicle-bridge system comprising only two sensors, with one installed on a vehicle parked temporarily on the bridge and another mounted directly on the bridge. The proposed algorithm is utilized for damage detection of two beams numerically and an aluminum model beam experimentally. The proposed method can achieve satisfactory accuracy in identifying damage with much less data, compared with the general Kriging model updating technique. Both the computation and instrumentation can be reduced for structural health monitoring and model updating.

Predicting early‐age stress evolution in restrained concrete by thermo‐chemo‐mechanical model and active ensemble learning

“…In the field of civil engineering, relevant studies on GP-based active learning are still rare. In recent years, studies (Tomar & Burton, 2021;Yuan et al, 2022) of GP-based active learning in reliability analysis have started to emerge, in which learning functions like expected improvement function (Jones et al, 1998), expected feasibility function (EFF; Bichon et al, 2008) and U function (Echard et al, 2011) have been implemented to quantify the potential contribution of a certain sample point to the improvement of the prediction performance of the surrogate model. Such potential contribution is mainly quantified by the distance of the sample point to the limit state boundary and the uncertainty.…”

“…These learning functions are built to achieve the exploitation–exploration tradeoff: (1) “exploitation” means that samples located near the limit state will be preferred; (2) “exploration” means that samples with high uncertainty will be preferred. The exploitation–exploration tradeoff guarantees that a surrogate model for reliability analysis can be obtained to achieve high accuracy within only dozens of learning iterations (Tomar & Burton, 2021; Yuan et al., 2022). However, as this paper aims to predict the EAS evolution over a certain time range (0∼672 h), the main problem is a global prediction rather than a local prediction that only focuses on a certain boundary (i.e., a limit state).…”

“…In other words, the sample points that are more likely to induce structural failure and the sample points on which the surrogate model has high uncertainty will be selected so as to achieve the exploitation–exploration tradeoff. Studies (Tomar & Burton, 2021; Yuan et al., 2022) have exemplified that GP‐based active learning can achieve high accuracy in structural reliability analysis with only dozens of data samples in the training set.…”

Hyper‐reduced order models for accelerating parametric analyses on reinforced concrete structures subjected to earthquakes

“…Combining such models with kriging methods (Yuan et al., 2023), machine learning algorithms, or neural networks to build response surfaces could significantly improve the existing optimization and structural monitoring methods. Modal and push‐over analyses are already used with machine learning algorithms that are supplied by measured data for damage detection (Ozdaghli & Koutsoukos, 2019; Wen et al., 2023) and structural health monitoring (Hwang et al., 2022).…”

Damage scenario analysis of bridges using crowdsourced smartphone data from passing vehicles

“…Various dynamic response characteristics have been estimated in prior work, including bridge frequency (Sitton, Rajan, et al, 2020;Sitton, Zeinali, et al, 2020;Yang et al, 2004) and bridge mode shapes (Malekjafarian & O'Brien, 2017b;O'Brien & Malekjafarian, 2016;Yang et al, 2014). These dynamic response characteristics may be used for model updating (Yuan et al, 2023), system identification (O'Brien et al, 2014), damage identification (Sitton, 2023), and damage characterization (Khorram et al, 2012). Comprehensive reviews of research on bridge dynamic response extraction using passing vehicles can be found in Malekjafarian et al (2015), Zhu and Law (2015), , and Wang et al (2022).…”