Iterative structural identification framework for evaluation of existing structures
Romain Pasquier,Ian F. C. Smith +1 more
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TL;DR: A new iterative framework for structural identification of complex aging structures based on model falsification and knowledge-based reasoning is proposed, suitable for ill-defined tasks such as structural identification where information is obtained gradually through data interpretation and in-situ inspection.
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About: This article is published in Engineering Structures. The article was published on 01 Jan 2016. and is currently open access.
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Citations
Optimal Multi-Type Sensor Placement for Structural Identification by Static-Load Testing.
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Data-Interpretation methodologies for Non-Linear Earthquake Response Predictions of Damaged Structures
TL;DR: In this paper, three structural identification methodologies that involve probabilistic approaches to data interpretation are compared: error-domain model falsification, Bayesian model updating with traditional assumptions as well as modified Bayesian models updating.
Comparing Structural Identification Methodologies for Fatigue Life Prediction of a Highway Bridge
Sai Ganesh Sarvotham Pai,Sai Ganesh Sarvotham Pai,Alain Nussbaumer,Ian F. C. Smith,Ian F. C. Smith +4 more
TL;DR: It is concluded that the EDMF and modified Bayesian model updating methodologies provide robust prediction of fatigue-life compared with residual minimization and traditional Bayesian models updating in the presence of correlated non-Gaussian uncertainty.
Connection stiffness identification of historic timber buildings using Temperature-based sensitivity analysis
TL;DR: In this article, a temperature-based response sensitivity method is proposed to identify the connection stiffness of the beam-column connection of a typical historical Tibetan building from temperature and strain response measurements, where the temperature is treated as a measurable input and the thermal loading on the structure can be determined from the temperature variation.
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A model-based data-interpretation framework for post-earthquake building assessment with scarce measurement data
TL;DR: In this paper, a framework for model-based data interpretation of measurements of earthquake-damaged structures is presented, which allows engineers to combine ambient-vibration measurements and visual inspection to reduce parametric uncertainty of a high-fidelity model using the error-domain model-falsification methodology.
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