Efficacy of Hilbert and Wavelet Transforms for Time-Frequency Analysis

Two independently emerging time-frequency transformations in Civil Engineering, namely, the wavelet transform and empirical mode decomposition with Hilbert transform (EMD+HT), are discussed in this study. Their application to a variety of nonstationary and nonlinear signals has achieved mixed results, with some comparative studies casting significant doubt on the wavelet’s suitability for such analyses. Therefore, this study shall revisit a number of applications of EMD+HT in the published literature, offering a different perspective to these commentaries and highlighting situations where the two approaches perform comparably and others where one offers an advantage. As this study demonstrates, much of the differing performance previously observed is attributable to EMD+HT representing nonlinear characteristics solely through the instantaneous frequency, with the wavelet relying on both this measure and the instantaneous bandwidth. Further, the resolutions utilized by the two approaches present a secondary factor influencing performance.     

Wind Response Control of Building with Variable Stiffness Tuned Mass Damper Using Empirical Mode Decomposition/Hilbert Transform

The effectiveness of a novel semiactive variable stiffness-tuned mass damper (SAIVS-TMD) for the response control of a wind-excited tall benchmark building is investigated in this study. The benchmark building considered is a proposed 76-story concrete office tower in Melbourne, Australia. It is a slender building 306 m tall with a height to width ratio of 7.3; hence, it is wind sensitive. Across wind load data from wind tunnel tests are used in the present study. The objective of this study is to evaluate the new SAIVS-TMD system, that has the distinct advantage of continuously retuning its frequency due to real time control and is robust to changes in building stiffness and damping. In comparison, the passive tuned mass damper (TMD) can only be tuned to a fixed frequency. A time varying analytical model of the tall building with the SAIVS-TMD is developed. The frequency tuning of the SAIVS-TMD is achieved based on empirical mode decomposition and Hilbert transform instantaneous frequency algorithm developed by the writers. It is shown that the SAIVS-TMD can reduce the structural response substantially, when compared to the uncontrolled case, and it can reduce the response further when compared to the case with TMD. Additionally, it is shown the SAIVS-TMD reduces response even when the building stiffness changes by ±15% and is robust; whereas, the TMD loses its effectiveness under such building stiffness variations. It is also shown that SAIVS-TMD can reduce the response similar to an active TMD; however, with an order of magnitude less power consumption.     

Nonlinear Identification of Lumped-Mass Buildings Using Empirical Mode Decomposition and Incomplete Measurement

Most structures exhibit some degrees of nonlinearity such as hysteretic behavior especially under damage. It is necessary to develop applicable methods that can be used to characterize these nonlinear behaviors in structures. In this paper, one such method based on the empirical mode decomposition (EMD) technique is proposed for identifying and quantifying nonlinearity in damaged structures using incomplete measurement. The method expresses nonlinear restoring forces in semireduced-order models in which a modal coordinate approach is used for the linear part while a physical coordinate representation is retained for the nonlinear part. The method allows the identification of parameters from nonlinear models through linear least-squares. It has been shown that the intrinsic mode functions (IMFs) obtained from the EMD of a response measured from a nonlinear structure are numerically close to its nonlinear modal responses. Hence, these IMFs can be used as modal coordinates as well as provide estimates for responses at unmeasured locations if the mode shapes of the structure are known. Two procedures are developed for identifying nonlinear damage in the form of nonhysteresis and hysteresis in a structure. A numerical study on a seven-story shear-beam building model with cubic stiffness and hysteretic nonlinearity and an experimental study on a three-story building model with frictional magnetoreological dampers are performed to illustrate the proposed method. Results show that the method can quite accurately identify the presence as well as the severity of different types of nonlinearity in the structure.     

Dynamic Response Solution in Transient State of Viscoelastic Road under Moving Load and Its Application

In order to study the dynamic response of an asphalt road, a dynamic model of the road under a moving load is proposed, in which the viscoelastic characteristics of the base and pavement are all considered, the pavement is regarded as an infinite beam on a Kelvin viscoelastic base. By using Green’s functions, Laplace transforms, and Fourier transforms, the analytical solution in transient is deduced. As the viscosity of the pavement is included in the model, the analytical solution can be used to investigate more of the factors that affect the dynamic response, such as vehicle speed, temperature, and road material properties. Using this analytical solution, some numerical calculations are given to illustrate the effects of vehicles’ speeds and different damping on the deflection with the displacement.     

Model for Predicting Financial Performance of Development and Construction Corporations

Performance forecasting is central to aligning an organization’s operations with its strategic direction. Despite the panoply of approaches to performance predictions, relatively few published studies address model development of financial performance predictions for the construction industry. By analyzing the preceding relationship between financial and economic variables and financial performance, this paper proposes an innovative approach to predicting firm financial performance. First, hypothesis tests using data for 42 development and construction corporations listed in the construction sector of the Taiwan Stock Exchange between 1997 Q1 and 2006 Q4 uncover useful relationships between financial performance and financial and economic variables. Second, based on these relationships, a three-stage mathematical modeling procedure is used for cross-sectional model estimation, which is subsequently refined to create firm-specific financial performance-forecasting models for four sample firms. The out-of-sample forecasting accuracy is evaluated using mean absolute percentage error (MAPE). The results show that the cross-sectional model explains 78.9% of the variation in the cross-sectional performance data, and the MAPE values in the forecasting models range from 9.54 to 19.69%.