Debonding detection in CFRP-retrofitted reinforced concrete structures using nonlinear Rayleigh wave

This paper proposes the use of nonlinear Rayleigh wave to inspect debonding in carbon fibre reinforced polymer (CFRP) retrofitted reinforced concrete structures. The proposed method requires a network of transducers that are used to scan the CFRP-retrofitted reinforced concrete structures by sequentially actuating and receiving Rayleigh wave. The nonlinear feature used for the debonding detection is second harmonic generation due to the interaction of Rayleigh wave at the debonding between the CFRP and concrete interfaces. A damage image reconstruction algorithm is proposed to provide a graphical representation for detecting and locating the debonding in the CFRP-retrofitted reinforced concrete structures. In this study, experimental case studies are used to demonstrate the performance of the proposed debonding detection technique. A transducer network with four piezoelectric transducers is used to actuate Rayleigh wave and measure the second harmonic in the experiments. The results show that the proposed debonding detection technique is reliable in detecting and locating the debonding in the CFRP-retrofitted reinforced concrete structures.Ching-Tai Ng, Hasan Mohseni, Heung-Fai La

Analysis of cylindrical shells using generalized differential quadrature

Shock and Vibration43193-198SHVI

Exploiting FPGA-aware merging of custom instructions for runtime reconfiguration

Runtime reconfiguration is a promising solution for reducing hardware cost in embedded systems, without compromising on performance. We present a framework that aims to increase the performance benefits of reconfigurable processors that support full or partial runtime reconfiguration. The proposed framework achieves this by: (1) providing a means for choosing suitable custom instruction selection heuristics, (2) leveraging FPGA-aware merging of custom instructions to maximize the reconfigurable logic block utilization in each configuration, and (3) incorporating a hierarchical loop partitioning strategy to reduce runtime reconfiguration overhead. We show that the performance gain can be improved by employing suitable custom instruction selection heuristics that, in turn, depend on the reconfigurable resource constraints and the merging factor (extent to which the selected custom instructions can be merged). The hierarchical loop partitioning strategy leads to an average performance gain of over 31% and 46% for full and partial runtime reconfiguration, respectively. Performance gain can be further increased to over 52% and 70% for full and partial runtime reconfiguration, respectively, by exploiting FPGA-aware merging of custom instructions.</jats:p

System identification of an enclosure with leakages using a probabilistic approach

This paper presents a model-based method for the system identification of a rectangular enclosure with an unknown number of air leakages subjected to uniform external noise, according to the probabilistic approach. The method aims to identify the number and corresponding locations and sizes of air leakages utilizing a set of measured, interior, sound pressure data in the frequency domain. System identification of an enclosure with an unknown number of air leakages is not trivial. Different classes of acoustic models are required to simulate an enclosure with different numbers of leakages. By following the traditional system of identification techniques, the "optimal" class of models is selected by minimizing the discrepancy between the measured and modeled interior sound pressure. By doing this, the most complicated model class (that is, the one with the highest number of uncertain parameters) will always be selected. Therefore, the traditional system identification techniques found in the literature to date cannot be employed to solve this problem. Our proposed system identification methodology relies on the Bayesian information criterion (BIC) to identify accurately the number of leakages in an enclosure. Unlike all deterministic system identification approaches, the proposed methodology aims to calculate the posterior (updated) probability density function (PDF) of leakage locations and sizes. Therefore, the uncertainties introduced by measurement noise and modeling error can be explicitly addressed. The coefficient of variable (COV) of uncertain parameters, which can be easily calculated from the PDF, provides valuable information about the reliability of the identification results. © 2008 Elsevier Ltd. All rights reserved.H. F. Lam, C. T. Ng, Y. Y. Lee and H. Y. Sunhttp://www.elsevier.com/wps/find/journaldescription.cws_home/622899/description#descriptio

Scale Dependence of the Halo Bias in General Local-Type Non-Gaussian Models I: Analytical Predictions and Consistency Relations

We investigate the clustering of halos in cosmological models starting with general local-type non-Gaussian primordial fluctuations. We employ multiple Gaussian fields and add local-type non-Gaussian corrections at arbitrary order to cover a class of models described by frequently-discussed f_nl, g_nl and \tau_nl parameterization. We derive a general formula for the halo power spectrum based on the peak-background split formalism. The resultant spectrum is characterized by only two parameters responsible for the scale-dependent bias at large scale arising from the primordial non-Gaussianities in addition to the Gaussian bias factor. We introduce a new inequality for testing non-Gaussianities originating from multi fields, which is directly accessible from the observed power spectrum. We show that this inequality is a generalization of the Suyama-Yamaguchi inequality between f_nl and \tau_nl to the primordial non-Gaussianities at arbitrary order. We also show that the amplitude of the scale-dependent bias is useful to distinguish the simplest quadratic non-Gaussianities (i.e., f_nl-type) from higher-order ones (g_nl and higher), if one measures it from multiple species of galaxies or clusters of galaxies. We discuss the validity and limitations of our analytic results by comparison with numerical simulations in an accompanying paper.Comment: 25 pages, 3 figures, typo corrected, Appendix C updated, submitted to JCA

Guided wave damage characterisation in beams utilising probabilistic optimisation

This paper introduces a probabilistic optimisation approach to the characterisation of damage in beams using guided waves. The proposed methodology not only determines the multivariate damage characteristics, but also quantifies the associated uncertainties of the predicted values, thus providing essential information for making decisions on necessary remedial work. The damage location, length and depth and the Young's modulus of the material are treated as unknown model parameters. Characterisation is achieved by applying a two-stage optimisation process that uses simulated annealing to guarantee that the solution is close to the global optimum, followed by a standard simplex search method that maximises the probability density function of a damage scenario conditional on the measurement data. The proposed methodology is applied to characterise laminar damage and is verified through a comprehensive series of numerical case studies that use spectral finite element wave propagation modelling with the consideration of both measurement noise and material uncertainty. The methodology is accurate and robust, and successfully detects damage even when the fault is close to the end of the beam and its length and depth are small. The particularly valuable feature of the proposed methodology is its ability to quantify the uncertainties associated with the damage characterisation results. The effects of measurement noise level, damage location, length and depth on the uncertainties in damage detection results are studied and discussed in detail. © 2009 Elsevier Ltd. All rights reserved.C.T. Ng, M. Veidt and H.F. Lamhttp://www.sciencedirect.com/science/journal/0141029

Experimental characterization of multiple cracks in a cantilever beam utilizing transient vibration data following a probabilistic approach

This paper puts forward a practical method for detecting multiple cracks on beams by utilizing transient vibration data. To explicitly address the uncertainty that is induced by measurement noise and modeling error, the Bayesian statistical framework is followed in the proposed crack detection method, which consists of two stages. In the first stage the number of cracks is identified by a computationally efficient algorithm that utilizes the Bayesian model class selection method. In the second stage, the posterior probability density function (PDF) of crack characteristics (i.e., the crack locations and crack depths) are determined by the Bayesian model updating method. The feasibility of the proposed methodology is experimentally demonstrated using a cantilever beam with one and two artificial cracks with depths between 0% and 50% of the beam height. The experimental data consists of transient vibration time histories that are collected at a single location using a laser Doppler vibrometer measurement system and impact excitations at three locations along the beam. The results show that the two-stage procedure enables the identification of the correct number of cracks and corresponding locations and extents, together with the coefficient of variation (COV).H.F. Lam, C.T. Ng, M. Veid

A probabilistic method for the detection of obstructed cracks of beam-type structures using spatial wavelet transform

This paper reports both the theoretical development and the numerical verification of a practical wavelet-based crack detection method, which identifies first the number of cracks and then the corresponding crack locations and extents. The value of the proposed method lies in its ability to detect obstructed cracks when measurement at or close to the cracked region is not possible. In such situations, most nonmodel-based methods, which rely on the abnormal change of certain indicators (e.g., curvature and strain mode shapes) at or close to the cracks, cannot be used. Most model-based methods follow the model updating approach. That is, they treat the crack location and extent as model parameters and identify them by minimizing the discrepancy between the modelled and measured dynamic responses. Most model-based methods in the literature can only be used in single- or multi-crack cases with a given number of cracks. One of the objectives of this paper is to develop a model-based crack detection method that is applicable in a general situation when the number of cracks is not known in advance. To explicitly handle the uncertainties associated with measurement noise and modelling error, the proposed method uses the Bayesian probabilistic approach. In particular, the method aims to calculate the posterior (updated) probability density function (PDF) of the crack locations and the corresponding extents. The proposed wavelet-based crack detection method is verified and demonstrated through a comprehensive series of numerical case studies, in which noisy data were generated by a Bernoulli-Euler beam with semi-rigid connections. The results show that the method can correctly identify the number of cracks even when the crack extent is small. The effects of the number of cracks and the crack extents on the results of crack detection are also studied and discussed in this paper. © 2007 Elsevier Ltd. All rights reserved.H.F. Lam, C.T. N