Comparing model-based adaptive LMS filters and a model-free hysteresis loop analysis method for structural health monitoring

2017 ◽  
Vol 84 ◽  
pp. 384-398 ◽  
Author(s):  
Cong Zhou ◽  
J. Geoffrey Chase ◽  
Geoffrey W. Rodgers ◽  
Chao Xu
Keyword(s):  
Structural Health Monitoring ◽  
Hysteresis Loop ◽  
Health Monitoring ◽  
Analysis Method ◽  
Loop Analysis ◽  
Structural Health ◽  
Model Based ◽  
Model Free ◽  
Adaptive Lms

A new pinched nonlinear hysteretic structural model for automated creation of digital clones in structural health monitoring

2020 ◽  
pp. 147592172092064 ◽  
Author(s):  
Cong Zhou ◽  
J Geoffrey Chase
Keyword(s):  
Decision Making ◽  
Structural Health Monitoring ◽  
Hysteresis Loop ◽  
Health Monitoring ◽  
Average Error ◽  
Support Vector ◽  
Model Parameters ◽  
Loop Analysis ◽  
Structural Health ◽  
Vector Machines

Optimizing risk treatment of structures in post-event decision-making is extremely difficult due to the lack of information on building damage/status after an event, particularly for nonlinear structures. This work develops an automated, no human intervention, modeling approach using structural health monitoring results to create accurate digital building clones of nonlinear structures for collapse prediction assessment and optimized decision-making. Model-free hysteresis loop analysis structural health monitoring method provides accurate structural health monitoring results from which model parameters of a nonlinear computational foundation model are identified. A new identifiable nonlinear smooth hysteretic model capturing essential structural dynamics and deterioration is developed to ensure robust parameter identification using support vector machines. Method performance is validated against both numerical and experimental data of a scaled 12-story reinforced concrete nonlinear structure. Results of numerical validation show an average error of 1.5% across 18 structural parameters from hysteresis loop analysis and an average error of 2.0% over 30 identified model parameters from support vector machines in the presence of 10% added root-mean-square noise. Validation using experimental data of the scale test reinforced concrete structure also shows a good match of identified hysteresis loop analysis and predicted nonlinear stiffness changes using the digital clones created with an average difference of 1.4%. More importantly, the predicted response using the digital clones for the highly nonlinear pinched hysteretic behavior matches the measured response well, with the average correlation coefficient Rcoeff = 0.92 and average root-mean-square error of 4.6% across all cases. The overall approach takes structural health monitoring from a tool providing retrospective damage data into automated prospective prediction analysis by “cloning” the structure using computational modeling, which in turn allows optimized decision-making using existing risk analyses and tools.

Improving robustness of damage imaging in model-based structural health monitoring of complex structures

2015 ◽  
Vol 138 (3) ◽  
pp. 1766-1766 ◽  
Author(s):  
Patrice Masson ◽  
Nicolas Quaegebeur ◽  
Pierre-Claude Ostiguy ◽  
Peyman Y. Moghadam
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Complex Structures ◽  
Structural Health ◽  
Model Based ◽  
Damage Imaging

Propagative broad learning for nonparametric modeling of ambient effects on structural health indicators

2020 ◽  
pp. 147592172091692 ◽  
Author(s):  
Sin-Chi Kuok ◽  
Ka-Veng Yuen ◽  
Stephen Roberts ◽  
Mark A Girolami
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Health Indicators ◽  
Operating Conditions ◽  
Learning Approach ◽  
Nonparametric Modeling ◽  
Structural Health ◽  
Model Free ◽  
Free Data

In this article, a novel propagative broad learning approach is proposed for nonparametric modeling of the ambient effects on structural health indicators. Structural health indicators interpret the structural health condition of the underlying dynamical system. Long-term structural health monitoring on in-service civil engineering infrastructures has demonstrated that commonly used structural health indicators, such as modal frequencies, depend on the ambient conditions. Therefore, it is crucial to detrend the ambient effects on the structural health indicators for reliable judgment on the variation of structural integrity. However, two major challenging problems are encountered. First, it is not trivial to formulate an appropriate parametric expression for the complicated relationship between the operating conditions and the structural health indicators. Second, since continuous data stream is generated during long-term structural health monitoring, it is required to handle the growing data efficiently. The proposed propagative broad learning provides an effective tool to address these problems. In particular, it is a model-free data-driven machine learning approach for nonparametric modeling of the ambient-influenced structural health indicators. Moreover, the learning network can be updated and reconfigured incrementally to adapt newly available data as well as network architecture modifications. The proposed approach is applied to develop the ambient-influenced structural health indicator model based on the measurements of 3-year full-scale continuous monitoring on a reinforced concrete building.

Model-based structural health monitoring of naval ship hulls

2011 ◽  
Vol 200 (9-12) ◽  
pp. 1137-1149 ◽  
Author(s):  
Christopher J. Stull ◽  
Christopher J. Earls ◽  
Phaedon-Stelios Koutsourelakis
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Health ◽  
Ship Hulls ◽  
Model Based ◽  
Naval Ship

scholarly journals Structural Health Monitoring using Adaptive LMS Filters

2008 ◽  
Author(s):  
Mostafa Nayyerloo ◽  
J Geoffrey Chase ◽  
Gregory A MacRae ◽  
XiaoQi Chen ◽  
Christopher E Hann
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Health ◽  
Adaptive Lms
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