Yibing Xiang

Bridge Remaining Strength Prediction Integrated with Bayesian Network and In Situ Load Testing

This paper proposes a new framework for predicting remaining bridge strength that integrates a Bayesian network and in situ load testing. It discusses the uncertainty of important factors on corrosion damage and develops a stiffness degradation model for corroded beams based on experimental investigations. Following this, the authors develop a Bayesian network that includes corrosion damage, stiffness degradation, load-deflection response, and other factors to predict structural strength degradation. A numerical example using an existing RC bridge demonstrates the general procedures. The comparison between the theoretical and the experimental deflections from load testing shows that the proposed methodology can efficiently improve prediction accuracy and reduce prediction uncertainty.

A novel Bayesian imaging method for probabilistic delamination detection of composite materials

A probabilistic framework for location and size determination for delamination in carbon?carbon composites is proposed in this paper. A probability image of delaminated area using Lamb wave-based damage detection features is constructed with the Bayesian updating technique. First, the algorithm for the probabilistic delamination detection framework using the proposed Bayesian imaging method (BIM) is presented. Next, a fatigue testing setup for carbon?carbon composite coupons is described. The Lamb wave-based diagnostic signal is then interpreted and processed. Next, the obtained signal features are incorporated in the Bayesian imaging method for delamination size and location detection, as well as the corresponding uncertainty bounds prediction. The damage detection results using the proposed methodology are compared with x-ray images for verification and validation. Finally, some conclusions are drawn and suggestions made for future works based on the study presented in this paper.

Probabilistic fatigue damage prognosis of lap joint using Bayesian updating

A general framework for probabilistic prognosis and uncertainty management under fatigue cyclic loading is proposed in this article. First, the general idea using the Bayesian updating in prognosis is introduced. Several sources of uncertainties are discussed and included in the Bayesian updating framework. An equivalent stress level model is discussed for the mechanism-based fatigue crack growth analysis, which serves as the deterministic model for the lap joint fatigue life prognosis. Next, an in situ lap joint fatigue test with pre-installed piezoelectric sensors is designed and performed to collect experimental data. Signal processing techniques are used to extract damage features for crack length estimation. Following this, the proposed methodology is demonstrated using the experimental data under both constant and variable amplitude loadings. Finally, detailed discussion on validation metrics of the proposed prognosis algorithm is given. Several conclusions and future work are drawn based on the proposed study.

Inverse First-Order Reliability Method for Probabilistic Fatigue Life Prediction of Composite Laminates under Multiaxial Loading

A new methodology for concurrent dynamic analysis and structural fatigue prognosis is proposed in this paper. The proposed methodology is based on a novel small timescale formulation of material fatigue crack growth that calculates the incremental crack growth at any arbitrary time within a loading cycle. It defines the fatigue crack kinetics based on the geometric relationship between the crack-tip opening displacement and the instantaneous crack growth rate. The proposed crack growth model can be expressed as a set of first-order differential equations. The structural dynamics analysis and fatigue crack growth model can be expressed as a coupled hierarchical state-space model. The dynamic response (structural level) and the fatigue crack growth (material level) can be solved simultaneously. Several numerical problems with single-degree-of-freedom and multiple-degree-of-freedom cases are used to show the proposed methodology. Model predictions are validated by using coupon testing data from open literatu...

Integrated experimental and numerical investigation for fatigue damage diagnosis in composite plates

An integrated experimental and numerical investigation of fatigue damage diagnosis in composite plates is presented in this study. First, the fatigue testing setup for carbon–carbon composite coupons is described with corresponding health monitoring approach through Lamb wave–based diagnostic data collection. In order to study the effects of degradation evolution, a finite element model is used to simulate the effect on Lamb wave propagation due to fatigue-induced delamination and matrix cracking. Simulation results are compared with the experimental testing to first validate the model and then develop several features as potential damage indicators. A parametric study is conducted on the effects of varying degrees of delamination and matrix cracking on these features. Results from the model simulations are presented along with the data analysis and discussions on the capability and limitations of the approach. Finally, some conclusions are drawn and future work is proposed based on the results obtained so far.

An equivalent stress level model for efficient fatigue crack growth prediction

A general probabilistic fatigue crack growth prediction methodology under random variable loading is developed using a novel equivalent stress level model and the inverse first-orderreliability method (IFORM). The proposed equivalent stress level model is based on the equivalent transformation of a random variable loading to constant amplitude loading, which avoid cycle-by-cycle calculation. An inverse first-order reliability method (IFORM) is used to evaluate the fatigue crack growth at any arbitrary reliability level. Inverse FORM method reduces the number of function evaluations and the computational cost is significantly reduced. The proposed method is very suitable for real-time damage prognosis and on-line decision making. Numerical examples are used to demonstrate the proposed method. Various experimental data under variable amplitude loading are collected and model predictions are compared with experimental data for model validation.

Uncertainty propagation in fatigue crack growth analysis using dimension reduction technique

This paper proposes a general probabilistic methodology for uncertainty propagation in fatigue crack growth analysis under both constant amplitude and variable amplitude loadings. A recently developed small time scale model is used to predict the deterministic fatigue crack growth curve (a-N curve). The dimension reduction technique is used for uncertainty propagation in fatigue crack growth analysis. The basic idea is to avoid direct simulations and focuses on the statistical moment behaviour of output random variables. A modified Chebyshev algorithm is presented to improve the approximation accuracy when calculating the integral points and associated weights for an arbitrary probabilistic distribution. Uncertainties of some material properties are considered as input random variables and propagated through the mechanical model. Prediction result of the proposed methodology is compared with the direct Monte Carlo Simulation (MCS) and is verified using experimental data of aluminium alloys under constant amplitude loading and block loading.

Equivalent Stress Transformation for Efficient Probabilistic Fatigue-Crack Growth Analysis under Variable Amplitude Loadings

AbstractA general probabilistic fatigue-crack growth prediction methodology for accurate and efficient damage prognosis is proposed in this paper. The methodology is based on an equivalent stress transformation and the inverse first-order reliability method (IFORM). The equivalent stress transformation aims to transform the random variable amplitude loading to an equivalent constant amplitude loading spectrum. The proposed transformation avoids the cycle-by-cycle calculation under general random variable amplitude loadings. An IFORM is used to evaluate the probabilistic fatigue-crack growth behavior and to further enhance the computational efficiency. The computational cost of the proposed study is significantly reduced compared with the direct Monte Carlo simulation. Thus, the proposed method is very suitable for real-time damage prognosis because of its high computational efficiency. Numerical examples are used to demonstrate the proposed method. Various experimental data under variable amplitude loadin...

Probabilistic fatigue life prediction using Subset Simulation

A general simulation-based methodology is proposed for efficient and accurate probabilistic fatigue life prediction. The proposed methodology is based on an Equivalent Initial Flaw Size (EIFS) crack growth model and Subset Simulation. The original Subset Simulation algorithm is modified to be suitable for probabilistic figure life prediction, which is an inverse reliability problem. The simulation results are compared with direct Monte Carlo simulation and experimental data for model verification and validation.

Inverse FORM method for probabilistic fatigue prognosis

Fatigue crack growth is a random process and various uncertainties affect the remaining useful life of engineering materials and structures. A general probabilistic life prediction methodology for accurate and efficient fatigue prognosis is proposed in this paper. The proposed methodology is based-on an inverse first-order reliability method (FORM) to evaluate the fatigue life at an arbitrary reliability level. An efficient searching algorithm for fatigue life prediction is developed and a numerical example is demonstrated. The prediction results are compared with direct Monte Carlo simulation for validation. Various experimental data are collected for model validation. Very good agreements are observed between model predictions and experimental data.