Staff members at Los Alamos National Laboratory (LANL) produced a summary of the structural health monitoring literature in 1995. This presentation will summarize the outcome of an updated review covering the years 1996 2001. The updated review follows the LANL statistical pattern recognition paradigm for SHM, which addresses four topics: 1. Operational Evaluation; 2. Data Acquisition and Cleansing; 3. Feature Extraction; and 4. Statistical Modeling for Feature Discrimination. The literature has been reviewed based on how a particular study addresses these four topics. A significant observation from this review is that although there are many more SHM studies being reported, the investigators, in general, have not yet fully embraced the well-developed tools from statistical pattern recognition. As such, the discrimination procedures employed are often lacking the appropriate rigor necessary for this technology to evolve beyond demonstration problems carried out in laboratory setting.

A review of structural health monitoring literature 1996-2001

Guided Lamb waves for identification of damage in composite structures: A review

Structural damage detection using neural network with learning rate improvement

Functionalized composite structures for new generation airframes: a review

It is proposed in this paper a novel two-stage structural damage detection approach using fuzzy neural networks (FNNs) and data fusion techniques. The method is used for structural health monitoring and damage detection, particularly for cases where the measurement data is enormous and with uncertainties. In the first stage of structural damage detection, structural modal parameters derived from structural vibration responses are fed into an FNN as the input. The output values from the FNN are defuzzified to produce a rough structural damage assessment. Later, in the second stage, the values output from three different FNN models are input directly to the data fusion center where fusion computation is performed. The final fusion decision is made by filtering the result with a threshold function, hence a refined structural damage assessment of superior reliability. The proposed approach has been applied to a 7-degree of freedom building model for structural damage detection, and proves to be feasible, efficient and satisfactory. Furthermore, the simulation result also shows that the identification accuracy can be boosted with the proposed approach instead of FNN models alone.

Two-stage structural damage detection using fuzzy neural networks and data fusion techniques

A structural damage detection method based on parameter identification using an iterative neural network (NN) technique is proposed in this study. The NN model is first trained off-line using an initial training data set that consists of assumed structural parameters as outputs and their corresponding dynamic characteristics as inputs. The structural parameters are assumed with different levels of reduction to simulate various degrees of structural damage. The concept of orthogonal array is adopted to generate the representative combinations of parameter changes, which can significantly reduce the number of training data while maintaining the data completeness. A modified back-propagation learning algorithm is proposed which can overcome possible saturation of the sigmoid function and speed up the training process. The trained NN model is used to predict the structural parameters by feeding in measured dynamic characteristics. The predicted structural parameters are then used in the FE model to calculate ...

M. L. Wang

Papers

Structural damage detection using an iterative neural network