Damage Diagnosis of Single-Layer Latticed Shell Based on Temperature-Induced Strain under Bayesian Framework.

Area of Science:

• Structural Engineering
• Mechanical Engineering
• Computational Mechanics

Background:

• Latticed shell structures are susceptible to damage, necessitating reliable diagnostic methods.
• Temperature-induced strain is a key indicator of structural integrity.
• Existing methods may not adequately address uncertainties in monitoring data.

Purpose of the Study:

• To propose a novel probabilistic method for damage diagnosis in latticed shell structures.
• To develop a new damage diagnosis index based on temperature-induced strain and structural parameters.
• To validate the proposed method through finite element analysis and real-world case studies.

Main Methods:

• Bayesian theory framework for probabilistic analysis.
• Development of a new damage diagnosis index correlating temperature-induced strain with structural parameters.
• Application of Markov Chain Monte Carlo (MCMC) for analyzing the diagnosis index.
• Finite element modeling of a latticed shell structure for validation.

Main Results:

• A frequency distribution histogram of the diagnosis index's posterior probability was obtained using MCMC.
• Confidence intervals for damage diagnosis were determined using the posterior distribution of the baseline condition.
• The method successfully diagnosed rod damage and bearing failure in radial, annular, and horizontal directions.
• Uncertainties in the strain response monitoring process were effectively considered.

Conclusions:

• The proposed Bayesian probabilistic method provides a robust approach for damage diagnosis in latticed shell structures.
• The method accurately identifies critical component failures, including rod damage and bearing issues.
• The approach enhances structural health monitoring by incorporating uncertainty quantification.