Improving the Quantification and Estimation of Damping for Bridges under Traffic Loading

It is important for engineers and designers to be able to accurately estimate the damping within a structure; however, this is not a trivial task. Simplifications are often made in an effort to make damping estimation easier, but these simplifications rely on assumptions that may not be universally true. One important assumption is that the excitation input for a structure may be modeled as broad-band noise, but traffic loading on a bridge likely violates that assumption. Traffic loads are characterized by the velocities of the vehicles and trains crossing the bridge, which gives the input specific frequency content. This added complexity increases the difficulty in accurately estimating the damping. The problem of traffic crossing a bridge was studied by creating a finite element model of a bridge using a beam system that consisted of a series of stringers resting on top of a larger girder. Traffic loads were then simulated using moving point loads and moving masses to represent cars and trains crossing the bridge. In addition to the traffic loading case, an ambient loading case was conducted using uniform broad-band noise as a means of comparison. The accelerations at several locations along the bridge span were recorded and used as input for a variety of operational modal analysis (OMA) methods. The OMA methods included both frequency domain techniques, such as Frequency Domain Decomposition (FDD), and time domain based identification, such as blind source separation (BSS). The results from the various OMA methods demonstrated how traffic loading creates distortion in the frequency response spectra of the bridge. This distortion had adverse effects for damping ratio estimation and in certain cases led to extreme errors. The mode shape estimates were not found to be affected by the distortion, but that meant that mode shape estimates could not be used to identify potentially erroneous damping estimates. The cause for the distortion was later identified as the driving frequencies produced by the vehicle-bridge interactions. The term ``driving frequency'' refers to the frequency created by a car traveling over a bridge or, by analogy, by a moving load traveling over a beam. This frequency is directly correlated with the speed of the vehicle and the length of the bridge. By considering a single moving point load traveling across the bridge, the responses of the stringers and girder were studied and the effects of the driving frequencies were better quantified in both the time and frequency domains. It was found that peaks in frequency domain appear at the even multiples of each car's driving frequency, and as more cars travel across the bridge the peaks of closely spaced driving frequency multiples begin to merge. As the number of cars increases to a full hour-long simulation and the car velocities become uniformly distributed over a given interval, numerous peaks merge together to form sustained regions of elevated energy in the frequency domain. These regions distort the frequency response spectra of the bridge and obscure the modal information. In order to deal with these distorted regions, a new approach to modal identification was proposed that focused on using partial information from the modal peaks. The peaks in the frequency domain were divided into left- and right-side spectra in order to take advantage of any undistorted portions of the modal peaks. These side spectra were analyzed using a curve-fitting approach based on combining optimization methods with clustering analysis. The presence of distortion presented certain challenges to traditional curve-fitting approaches, such as polynomial least squares, but the optimization algorithm was able to overcome these issues while also adding efficiency to the curve-fitting process. The clustering analysis was used to quickly find the optimal subsets within the optimization-based curve-fitting results. By performing curve-fitting to side spectra, different sets of modal parameters were produced that fit each side. It was found that the modal parameters for the intact or undistorted side compared favorably with the true modal parameters. While this optimization and clustering methodology could not account for all types of distortion, it demonstrated large improvements as compared to traditional OMA approaches for the modes most severely impacted by the distortion. Another potential benefit of this method is that the distributions within the final clusters could be used to provide ranges of possible values for the damping ratios instead of only a single value

Parameter Identification with Unknown Input and Incomplete Measurements

Ph.DDOCTOR OF PHILOSOPH

Instationary modal Analysis for Impulse-type stimulated structures

In order to determine modal parameters, classical experimental modal analysis can be used in engineering application. This method finds a system frequency response function using fast Fourier Transform (FFT). The Fourier Transform is one type of global data analysis method. The frequency resolution is equal to the reciprocal of the total sample time. So applying the FFT is not suitable for any transient signal to reveal local characteristics. However, in modern manufacturing industries, processing forces are rapidly changing. The dynamic behavior may vary rapidly in a short time due to variations in the machining parameters and changes in boundary conditions. These nonlinear and non-stationary dynamic parameters are not constant during machining operations identification using FFT. In this research, an innovative transient signal analysis approach has been developed, which is based on an application of the least squares estimation. The proposed method provides transient information with high resolution and to identify the time-varying modal parameters during machining. Least squares estimation can be augmented with a sliding-window operation (SWLSE) to reveal the actual system dynamic behavior at any moment. The accuracy of this method depends on the window size, the noise ratio and the sampling rate etc. The estimation accuracy of modal parameters is discussed in this work. To examine the efficiency of the SWLSE method experimental tests are performed on a laboratory beam system and the results are compared with the classical experimental modal analysis (CEMA) method. The laboratory beam system is designed and assembled that the stiffness and damping ratio of the structure can be adjusted. Additionally, the proposed method is applied to the identification of the actual modal parameters of machine tools during machining operations. In another application, the proposed method provides also the process varied damping information in a process monitoring

Novel chemometric approaches towards handling biospectroscopy datasets

Chemometrics allows one to identify chemical patterns using spectrochemical information of biological materials, such as tissues and biofluids. This has fundamental importance to overcome limitations in traditional bioanalytical analysis, such as the need for laborious and extreme invasive procedures, high consumption of reagents, and expensive instrumentation. In biospectroscopy, a beam of light, usually in the infrared region, is projected onto the surface of a biological sample and, as a result, a chemical signature is generated containing the vibrational information of most of the molecules in that material. This can be performed in a single-spectra or hyperspectral imaging fashion, where a resultant spectrum is generated for each position (pixel) in the surface of a biological material segment, hence, allowing extraction of both spatial and spectrochemical information simultaneously. As an advantage, these methodologies are non-destructive, have a relatively low-cost, and require minimum sample preparation. However, in biospectroscopy, large datasets containing complex spectrochemical signatures are generated. These datasets are processed by computational tools in order to solve their signal complexity and then provide useful information that can be used for decision taking, such as the identification of clustering patterns distinguishing disease from healthy controls samples; differentiation of tumour grades; prediction of unknown samples categories; or identification of key molecular fragments (biomarkers) associated with the appearance of certain diseases, such as cancer. In this PhD thesis, new computational tools are developed in order to improve the processing of bio-spectrochemical data, providing better clinical outcomes for both spectral and hyperspectral datasets

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

Design a CPW antenna on rubber substrate for multiband applications

This paper presents a compact CPW monopole antenna on rubber substrate for multiband applications. The multi band applications (2.45 and 3.65 GHz) is achieved on this antenna design with better antenna performances. Specially this antenna focused on ISM band application meanwhile some of slots (S1, S2, S3) have been used and attained another frequency band at 3.65 GHz for WiMAX application. The achievement of the antenna outcomes from this design that the bandwidth of 520 MHz for first band, the second band was 76 MHz for WiMAX application and the radiation efficiency attained around 90%. Moreover, the realized gain was at 4.27 dBi which overcome the most of existing design on that field. CST microwave studio has been used for antenna simulation

Vibration-based damage identification in beam structures through wavelet analysis

Nowadays the topic of damage identification in structures is of primary interest in the field of civil, aerospace, mechanical engineering research. Indeed, due to the increasing use of advanced structural systems (e.g. airplanes, space shuttles, wind turbines, etc.) and to the aging of old structures (e.g. buildings, towers, bridges, etc.), the safety and reliability of structures have to be guaranteed to avoid catastrophic events and loss of human lives. The present thesis is focused on vibration-based damage identification in beam structures through wavelet analysis. The dissertation is arranged in six chapters. Chapter 1 introduces the topic of the thesis through a broad presentation of the state of the art of damage identification methods for structural health monitoring and control, with particular attention to vibration-based structural damage identification methods. In Chapter 2, the time-frequency technique, named wavelet analysis, is firstly theoretically presented and its application, available in the literature particularly for beam-like structures, as a damage detection tool both in time and in space domains is discussed. In Chapter 3, the mechanical models of homogeneous and fiber-reinforced cracked beams are presented. The models are used to simulate the real static and dynamic responses of beam structures for successive damage detection through wavelet analysis. The last three chapters of the thesis are devoted to the original findings of the present research. Chapter 4 focuses on the issue of border distortions in damage detection by continuous wavelet transform. To tackle the problem, a new polynomial padding method is proposed and compared with the most effective padding methods commonly used in the literature. In Chapter 5 the effect of spatial sampling in damage detection of cracked beams by continuous wavelet transform is thoroughly investigated through a parametric study. From the outcomes, some general indications on the optimal number of sampling intervals for an effective damage detection are obtained. Finally in Chapter 6, a new health structural monitoring method based on time-spatial wavelet analysis is presented to control the static and dynamic, elastic-plastic behaviour of a cracked fiber-reinforced beam. The capability of the method is discussed particularly with respect to scale of the analysing wavelet, the noise level and the spatial sampling interval, considering a small crack

Sustainability of Olive Oil System

Sustainability, defined as ‘meeting current needs without compromising the future’, is a widely accepted goal across many sectors of society. Sustainability’s criteria and indicators often only regard sustaining present conditions through increased resilience, intended as a system’s capacity to experience shocks while retaining essentially the same functions and structures. However, new sustainability concepts, sometimes referred to as “sustainagility”, also consider the properties and assets of a system that sustains the ability (agility) of agents to adapt and meet their needs in new ways, preparing for future unpredictability and unforeseen changes. Therefore, resilience must coexist with adaptive capacity for real, long-term sustainability. Consumers are paying increasing attention to the sustainability of the food supply chain; thus, sustainable development is necessary for all food processes. Since the olive oil sector has a well-established historical tradition, any change and innovation that aims to obtain a sustainable development not only needs to be analyzed in terms of environmental, economic, and social aspects, it should also be significantly improved and closely monitored. Thus, this Special Issue is a collection of papers that can increase sustainability knowledge in the olive-oil-processing chain, to take a significant step forward in future developments