Frequency response function based structural damage detection using artificial neural networks

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, few attempts have been made to explore the structure damage with frequency response functions (FRFs). This paper illustrates the damage identification and condition assessment of a beam structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). In practice, usage of all available FRF data as an input to artificial neural networks makes the training and convergence impossible. Therefore one of the data reduction techniques Principal Component Analysis (PCA) is introduced in the algorithm. In the proposed procedure, a large set of FRFs are divided into sub-sets in order to find the damage indices for different frequency points of different damage scenarios. The basic idea of this method is to establish features of damaged structure using FRFs from different measurement points of different sub-sets of intact structure. Then using these features, damage indices of different damage cases of the structure are identified after reconstructing of available FRF data using PCA. The obtained damage indices corresponding to different damage locations and severities are introduced as input variable to developed artificial neural networks. Finally, the effectiveness of the proposed method is illustrated and validated by using the finite element modal of a beam structure. The illustrated results show that the PCA based damage index is suitable and effective for structural damage detection and condition assessment of building structures.

Frequency response function based damage identification using principal component analysis and pattern recognition technique

Pattern recognition is a promising approach for the identification of structural damage using measured dynamic data. Much of the research on pattern recognition has employed artificial neural networks (ANNs) and genetic algorithms as systematic ways of matching pattern features. The selection of a damage-sensitive and noise-insensitive pattern feature is important for all structural damage identification methods. Accordingly, a neural networks-based damage detection method using frequency response function (FRF) data is presented in this paper. This method can effectively consider uncertainties of measured data from which training patterns are generated. The proposed method reduces the dimension of the initial FRF data and transforms it into new damage indices and employs an ANN method for the actual damage localization and quantification using recognized damage patterns from the algorithm. In civil engineering applications, the measurement of dynamic response under field conditions always contains noise components from environmental factors. In order to evaluate the performance of the proposed strategy with noise polluted data, noise contaminated measurements are also introduced to the proposed algorithm. ANNs with optimal architecture give minimum training and testing errors and provide precise damage detection results. In order to maximize damage detection results, the optimal architecture of ANN is identified by defining the number of hidden layers and the number of neurons per hidden layer by a trial and error method. In real testing, the number of measurement points and the measurement locations to obtain the structure response are critical for damage detection. Therefore, optimal sensor placement to improve damage identification is also investigated herein. A finite element model of a two storey framed structure is used to train the neural network. It shows accurate performance and gives low error with simulated and noise-contaminated data for single and multiple damage cases. As a result, the proposed method can be used for structural health monitoring and damage detection, particularly for cases where the measurement data is very large. Furthermore, it is suggested that an optimal ANN architecture can detect damage occurrence with good accuracy and can provide damage quantification with reasonable accuracy under varying levels of damage.

In situ investigation of explosive crystallization in a-Ge: Experimental determination of the interface response function using dynamic transmission electron microscopy

The crystallization of amorphous semiconductors is a strongly exothermic process. Once initiated the release of latent heat can be sufficient to drive a self-sustaining crystallization front through the material in a manner that has been described as explosive. Here, we perform a quantitative in situ study of explosive crystallization in amorphous germanium using dynamic transmission electron microscopy. Direct observations of the speed of the explosive crystallization front as it evolves along a laser-imprinted temperature gradient are used to experimentally determine the complete interface response function (i.e., the temperature-dependent front propagation speed) for this process, which reaches a peak of 16 m/s. Fitting to the Frenkel-Wilson kinetic law demonstrates that the diffusivity of the material locally/immediately in advance of the explosive crystallization front is inconsistent with those of a liquid phase. This result suggests a modification to the liquid-mediated mechanism commonly used to describe this process that replaces the phase change at the leading amorphous-liquid interface with a change in bonding character (from covalent to metallic) occurring in the hot amorphous material.

Using of model updating considering damped frequency response function

The search for better performance in the structural systems has been taken to more refined models, involving the analysis of a growing number of details, which should be correctly formulated aiming at defining a representative model of the real system. Representative models demand a great detailing of the project and search for new techniques of evaluation and analysis. Model updating is one of this technologies, it can be used to improve the predictive capabilities of computer-based models. This paper presents a FRF-based finite element model updating procedure whose the updating variables are physical parameters of the model. It includes the damping effects in the updating procedure assuming proportional and none proportional damping mechanism. The updating parameters are defined at an element level or macro regions of the model. So, the parameters are adjusted locally, facilitating the physical interpretation of the adjusting of the model. Different tests for simulated and experimental data are discussed aiming at defining the characteristics and potentialities of the methodology.

The temporal impulse response function in infantile nystagmus.

Despite rapid to-and-fro motion of the retinal image that results from their incessant involuntary eye movements, persons with infantile nystagmus (IN) rarely report the perception of motion smear. We performed two experiments to determine if the reduction of perceived motion smear in persons with IN is associated with an increase in the speed of the temporal impulse response. In Experiment 1, increment thresholds were determined for pairs of successively presented flashes of a long horizontal line, presented on a 65-cd/m2 background field. The stimulus-onset asynchrony (SOA) between the first and second flash varied from 5.9 to 234 ms. In experiment 2, temporal contrast sensitivity functions were determined for a 3-cpd horizontal square-wave grating that underwent counterphase flicker at temporal frequencies between 1 and 40 Hz. Data were obtained for 2 subjects with predominantly pendular IN and 8 normal observers in Experiment 1 and for 3 subjects with IN and 4 normal observers in Experiment 2. Temporal impulse response functions (TIRFs) were estimated as the impulse response of a linear second-order system that provided the best fit to the increment threshold data in Experiment 1 and to the temporal contrast sensitivity functions in Experiment 2. Estimated TIRFs of the subjects with pendular IN have natural temporal frequencies that are significantly faster than those of normal observers (ca. 13 vs. 9 Hz), indicating an accelerated temporal response to visual stimuli. This increase in response speed is too small to account by itself for the virtual absence of perceived motion smear in subjects with IN, and additional neural mechanisms are considered.

Resin tapping in Pinus pinaster: effects on growth and response function to climate

Dendrocronología sobre Pinus pinaster, como la recolección de resina y los factores climáticos han afectado a su crecimiento.

Design of ultra-narrow WDM filters using the transmission response function of fibre Bragg gratings

We have demonstrated the feasibility of error-free DWDM 8×40 Gb/s transmission over an 800 km SMF/DCF link with 0.8 bit/s/Hz spectral efficiency without polarization multiplexing.

Structural damage detection using frequency response functions and neural networks

This paper illustrates the damage identification and condition assessment of a three story bookshelf structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). A major obstacle of using measured frequency response function data is a large size input variables to ANNs. This problem is overcome by applying a data reduction technique called principal component analysis (PCA). In the proposed procedure, ANNs with their powerful pattern recognition and classification ability were used to extract damage information such as damage locations and severities from measured FRFs. Therefore, simple neural network models are developed, trained by Back Propagation (BP), to associate the FRFs with the damage or undamaged locations and severity of the damage of the structure. Finally, the effectiveness of the proposed method is illustrated and validated by using the real data provided by the Los Alamos National Laboratory, USA. The illustrated results show that the PCA based artificial Neural Network method is suitable and effective for damage identification and condition assessment of building structures. In addition, it is clearly demonstrated that the accuracy of proposed damage detection method can also be improved by increasing number of baseline datasets and number of principal components of the baseline dataset.

Structural damage detection method using frequency response functions

Structural damage detection using measured dynamic data for pattern recognition is a promising approach. These pattern recognition techniques utilize artificial neural networks and genetic algorithm to match pattern features. In this study, an artificial neural network–based damage detection method using frequency response functions is presented, which can effectively detect nonlinear damages for a given level of excitation. The main objective of this article is to present a feasible method for structural vibration–based health monitoring, which reduces the dimension of the initial frequency response function data and transforms it into new damage indices and employs artificial neural network method for detecting different levels of nonlinearity using recognized damage patterns from the proposed algorithm. Experimental data of the three-story bookshelf structure at Los Alamos National Laboratory are used to validate the proposed method. Results showed that the levels of nonlinear damages can be identified precisely by the developed artificial neural networks. Moreover, it is identified that artificial neural networks trained with summation frequency response functions give higher precise damage detection results compared to the accuracy of artificial neural networks trained with individual frequency response functions. The proposed method is therefore a promising tool for structural assessment in a real structure because it shows reliable results with experimental data for nonlinear damage detection which renders the frequency response function–based method convenient for structural health monitoring.

Problems in estimating the response functions of natural climatic recording systems [abstract]

EXTRACT (SEE PDF FOR FULL ABSTRACT): High-resolution proxy records of climate, such as varves, ice cores, and tree-rings, provide the opportunity for reconstructing climate on a year-by-year basis. In order to do so it is necessary to approximate the complex nonlinear response function of the natural recording system using linear statistical models. Three problems with this approach were discussed, and possible solutions were suggested. Examples were given from a reconstruction of Santa Barbara precipitation based on tree-ring records from Santa Barbara County.

The frequency response of dynamic friction: Model comparisons

Any linearised theory of the initiation of friction-excited vibration via instability of the state of steady sliding requires information about the dynamic friction force in the form of a frequency response function for sliding friction. Recent measurements of this function for an interface consisting of a nylon pin against a glass disc are used to probe the underlying constitutive law. Results are compared to linearised predictions from the simplest ratestate model of friction, and a ratetemperature model. In both cases the observed variation with frequency is not compatible with the model predictions, although there are some significant points of similarity. The most striking result relates to variation of the normal load: any theory embodying the Coulomb relation F∝N would predict behaviour entirely at variance with the measurements, even though the steady friction force obtained during the same measurements does follow the Coulomb law. © 2011 Elsevier Ltd. All rights reserved.

Transient response of structures with uncertain properties to nonlinear shock loading

A method is presented to predict the transient response of a structure at the driving point following an impact or a shock loading. The displacement and the contact force are calculated solving the discrete convolution between the impulse response and the contact force itself, expressed in terms of a nonlinear Hertzian contact stiffness. Application of random point process theory allows the calculation of the impulse response function from knowledge of the modal density and the geometric characteristics of the structure only. The theory is applied to a wide range of structures and results are experimentally verified for the case of a rigid object hitting a beam, a plate, a thin and a thick cylinder and for the impact between two cylinders. The modal density of the flexural modes for a thick slender cylinder is derived analytically. Good agreement is found between experimental, simulated and published results, showing the reliability of the method for a wide range of situations including impacts and pyroshock applications. © 2013 Elsevier Ltd. All rights reserved.

Measurement of gain spectrum for semiconductor lasers utilizing integrations of product of emission spectrum and a phase function over one mode interval

A technique based on the integrations of the product of amplified spontaneous emission spectrum and a phase function over one mode interval is proposed for measuring gain spectrum for Fabry-Perot semiconductor lasers, and a gain correction factor related to the response function of the optical spectrum analyzer (OSA) is obtained for improving the accuracy of measured gain spectrum. The gain spectra with a difference less than 1.3 cm(-1) from 1500 to 1600 nm are obtained for a 250-mum-long semiconductor laser at the OSA resolution of 0.06, 0.1, 0.2, and 0.5 nm. The corresponding gain correction factor is about 9 cm(-1) at the resolution of 0.5 nm. The gain spectrum measured at the resolution of 0.5 nm has the same accuracy as that obtained by the Hakki-Paoli method at the resolution of 0.06 nm for the laser with the mode interval of 1.3 nm.