The use of an instrumented vehicle in a wavelet based damage detection approach for bridge structures

Periodic monitoring of structures such as bridges is necessary as their condition can deteriorate due to environmental conditions and ageing, causing the bridge to become unsafe. This monitoring - so called Structural Health Monitoring (SHM) - can give an early warning if a bridge becomes unsafe. This paper investigates an alternative wavelet-based approach for the monitoring of bridge structures which consists of the use of a vehicle fitted with accelerometers on its axles. A simplified vehicle-bridge interaction model is used in theoretical simulations to examine the effectiveness of the approach in detecting damage in the bridge. The accelerations of the vehicle are processed using a continuous wavelet transform, allowing a time-frequency analysis to be performed. This enables the identification of both the existence and location of damage from the vehicle response. Based on this analysis, a damage index is established. A parametric study is carried out to investigate the effect of parameters such as the bridge span length, vehicle speed, vehicle mass, damage level, signal noise level and road surface roughness on the accuracy of results. In addition, a laboratory experiment is carried out to validate the results of the theoretical analysis and assess the ability of the approach to detect changes in the bridge response.

Experimental investigation of a wavelet based drive-by bridge inspection system incorporating pattern recognition

This paper presents the results of an experimental investigation, carried out in order to verify the feasibility of a ‘drive-by’ approach which uses a vehicle instrumented with accelerometers to detect and locate damage in a bridge. In theoretical simulations, a simplified vehicle-bridge interaction model is used to investigate the effectiveness of the approach in detecting damage in a bridge from vehicle accelerations. For this purpose, the accelerations are processed using a continuous wavelet transform and damage indicators are evaluated and compared. Alternative statistical pattern recognition techniques are incorporated to allow for repeated vehicle passes. Parameters such as vehicle speed, damage level, location and road roughness are varied in simulations to investigate the effect. A scaled laboratory experiment is carried out to assess the effectiveness of the approach in a more realistic environment, considering a number of bridge damage scenarios.

Wavelet Based Damage Detection Approach for Bridge Structures Utilising Vehicle Vibration

This paper investigates a wavelet-based damage detection approach for bridge structures. By analysing the continuous wavelet transform of the vehicle response, the approach aims to identify changes in the bridge response which may indicate the existence of damage. A numerical vehicle-bridge interaction model is used in simulations as part of a sensitivity study. Furthermore, a laboratory experiment is carried out to investigate the effects of varying vehicle configuration, speed and bridge damping on the ability of the vehicle to detect changes in the bridge response. The accelerations of the vehicle and bridge are processed using a continuous wavelet transform, allowing time-frequency analysis to be carried out on the responses of the laboratory vehicle-bridge interaction system. Results indicate the most favourable conditions for successful implementation of the approach.

VEDA:Variation-aware energy-efficient discrete Wavelet Transform architecture

In this paper, we present a unified approach to an energy-efficient variation-tolerant design of Discrete Wavelet Transform (DWT) in the context of image processing applications. It is to be noted that it is not necessary to produce exactly correct numerical outputs in most image processing applications. We exploit this important feature and propose a design methodology for DWT which shows energy quality tradeoffs at each level of design hierarchy starting from the algorithm level down to the architecture and circuit levels by taking advantage of the limited perceptual ability of the Human Visual System. A unique feature of this design methodology is that it guarantees robustness under process variability and facilitates aggressive voltage over-scaling. Simulation results show significant energy savings (74% - 83%) with minor degradations in output image quality and avert catastrophic failures under process variations compared to a conventional design. © 2010 IEEE.

Low Complexity Spectral Analysis of Heart-Rate-Variability through a Wavelet based FFT

In this paper, a low complexity system for spectral analysis of heart rate variability (HRV) is presented. The main idea of the proposed approach is the implementation of the Fast-Lomb periodogram that is a ubiquitous tool in spectral analysis, using a wavelet based Fast Fourier transform. Interestingly we show that the proposed approach enables the classification of processed data into more and less significant based on their contribution to output quality. Based on such a classification a percentage of less-significant data is being pruned leading to a significant reduction of algorithmic complexity with minimal quality degradation. Indeed, our results indicate that the proposed system can achieve up-to 45% reduction in number of computations with only 4.9% average error in the output quality compared to a conventional FFT based HRV system.

Hierarchical demand response using dantzig-wolfe decomposition

Demand Side Management (DSM) programmes are designed to shift electrical loads from peak times. Demand Response (DR) algorithms automate this process for controllable loads. DR can be implemented explicitly in terms of Peak to Average Ratio Reduction (PARR), in which case the maximum peak load is minimised over a prediction horizon by manipulating the amount of energy given to controllable loads at different times. A hierarchical predictive PARR algorithm is presented here based on Dantzig-Wolfe decomposition. © 2013 IEEE.

An indirect bridge inspection method incorporating a wavelet-based damage indicator and pattern recognition

In recent years, there has been a move towards the development of indirect structural health monitoring (SHM)techniques for bridges; the low-cost vibration-based method presented in this paper is such an approach. It consists of the use of a moving vehicle fitted with accelerometers on its axles and incorporates wavelet analysis and statistical pattern recognition. The aim of the approach is to both detect and locate damage in bridges while reducing the need for direct instrumentation of the bridge. In theoretical simulations, a simplified vehicle-bridge interaction model is used to investigate the effectiveness of the approach in detecting damage in a bridge from vehicle accelerations. For this purpose, the accelerations are processed using a continuous wavelet transform as when the axle passes over a damaged section, any discontinuity in the signal would affect the wavelet coefficients. Based on these coefficients, a damage indicator is formulated which can distinguish between different damage levels. However, it is found to be difficult to quantify damage of varying levels when the vehicle’s transverse position is varied between bridge crossings. In a real bridge field experiment, damage was applied artificially to a steel truss bridge to test the effectiveness of the indirect approach in practice; for this purpose a two-axle van was driven across the bridge at constant speed. Both bridge and vehicle acceleration measurements were recorded. The dynamic properties of the test vehicle were identified initially via free vibration tests. It was found that the resulting damage indicators for the bridge and vehicle showed similar patterns, however, it was difficult to distinguish between different artificial damage scenarios.

Thermal decomposition mechanism of levoglucosan during cellulose pyrolysis

Levoglucosan (1,6-anhydro-β-d-glucopyranose) decomposition is an important step during cellulose pyrolysis and for secondary tar reactions. The mechanism of levoglucosan thermal decomposition was studied in this paper using density functional theory methods. The decomposition included direct CO bond breaking, direct CC bond breaking, and dehydration. In total, 9 different pathways, including 16 elementary reactions, were studied, in which levoglucosan serves as a reactant. The properties of the reactants, transition states, intermediates, and products for every elementary reaction were obtained. It was found that 1-pentene-3,4-dione, acetaldehyde, 2,3-dihydroxypropanal, and propanedialdehyde can be formed from the CO bond breaking decomposition reactions. 1,2-Dihydroxyethene and hydroxyacetic acid vinyl ester can be formed from the CC bond breaking decomposition reactions. It was concluded that CO bond breaking is easier than CC bond breaking due to a lower activation energy and a higher released energy. During the 6 levoglucosan dehydration pathways, one water molecule which composed of a hydrogen atom from C3 and a hydroxyl group from C2 is the preferred pathway due to a lower activation energy and higher product stability. © 2012 Elsevier B.V. All rights reserved.

Density Functional Study on Reaction Activity of Steam Decomposition Products Adsorbed on Carbonaceous Surface

A systematic theoretical study on the adsorption of steam and its thermal decomposition products on carbon both zigzag and armchair surface was performed to provide molecular-level understanding of the reaction activity of all these reactants in biomass steam gasification process. All the calculations were carried out using density functional theory (DFT) at the B3LYP/6-31+g(d,p) level. The structures of carbonaceous surfaces, all reactants and surface complexes were optimized and characterized. Based on the value of adsorption heat been obtained from the calculation, the activity of all reactants can be ordered as: O > O2 >H2 >H >OH >H2O for both zigzag and armchair surface, and the adsorption style is physisorption to water molecule and chemisorption to the other dissociated components.

Using Brain Data for Sentiment Analysis

We present the results of exploratory experiments using lexical valence extracted from brain using electroencephalography (EEG) for sentiment analysis. We selected 78 English words (36 for training and 42 for testing), presented as stimuli to 3 English native speakers. EEG signals were recorded from the subjects while they performed a mental imaging task for each word stimulus. Wavelet decomposition was employed to extract EEG features from the time-frequency domain. The extracted features were used as inputs to a sparse multinomial logistic regression (SMLR) classifier for valence classification, after univariate ANOVA feature selection. After mapping EEG signals to sentiment valences, we exploited the lexical polarity extracted from brain data for the prediction of the valence of 12 sentences taken from the SemEval-2007 shared task, and compared it against existing lexical resources.

The photocatalytic decomposition of microcystin-LR using selected titanium dioxide materials

Microcystins (cyclic heptapeptides) produced by a number of freshwater cyanobacteria are a potential cause for concern in potable water supplies due to their acute and chronic toxicity. TiO₂ photocatalysis is a promising technology for removal of these toxins from drinking water. It is, however, necessary to have a sufficient knowledge of how the catalyst materials cause the degradation of the toxins through the photocatalytic process. The present study reports microcystin degradation products of the photocatalytic oxidation by using a number of commercial TiO₂ powder (P25, PC50, PC500 and UV100) and granular (KO1, KO3, TiCat-C, TiCat-S) materials, so aiding the mechanistic understanding of this process. Liquid chromatography-mass spectrometry analysis demonstrated that the major destruction pathway of microcystin for all the catalysts tested followed almost the same pathway, indicating the physical properties of the catalysts had little effects on the degradation pathway of microcystin-LR. 

Mechanistic studies of the photocatalytic oxidation of microcystin-LR: An investigation of byproducts of the decomposition process

Microcystins (cyclic heptapeptides) are produced by a number of freshwater cyanobacteria and cause concern in potable water supplies due to their acute and chronic toxicity. The present study reports the structural characterization of the degradation products of the photocatalytic oxidation of microcystin-LR, so aiding the mechanistic understanding of this process. TiO₂ photocatalysis is a promising technology for removal of these toxins from drinking water. However, before it can be adopted in any practical application it is necessary to have a sufficient knowledge of degradation byproducts and their potential toxicity. Liquid chromatography-mass spectrometry analysis demonstrated that the major destruction pathway of microcystin appears to be initiated via three mechanisms: UV irradiation, hydroxyl radical attack, and oxidation. UV irradiation caused geometrical isomerization of microcystin converting the (4E), (6E) of the Adda configuration to (4E), 6(Z) or 4(Z), 6(E). Hydroxyl radical attack on the conjugated diene structure of Adda moiety produced dihyroxylated products. Further oxidation cleaved the hydroxylated 4-5 and/or 6-7 bond of Adda to form aldehyde or ketone peptide residues, which then were oxidized into the corresponding carboxylic acids. Photocatalysis also hydrolyzed the peptide bond on the ring structure of microcystin to form linear structures although this appeared to be a minor pathway.