• Smart Structures and Systems
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• v.16 no.1
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• pp.141-162
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• 2015

The magneto-rheological visco-elastomer (MRVE) is used as a smart core to control the stochastic micro-vibration of a sandwich plate with supported mass. The micro-vibration response of the sandwich plate with MRVE core and supported mass under stochastic support motion excitations is studied and compared to evaluate the vibration suppression capability. The effects of the supported mass and localized magnetic field on the stochastic micro-vibration response of the MRVE sandwich plate are taken into account. The dynamic characteristics of the MRVE core in micro-vibration are described by a non-homogeneous complex modulus dependent on vibration frequency and controllable by applied magnetic fields. The partial differential equations for the coupled transverse and longitudinal motions of the MRVE sandwich plate with supported mass are derived from the dynamic equilibrium, constitutive and geometric relations. The simplified ordinary differential equations are obtained for the transverse vibration of the MRVE sandwich plate under localized magnetic fields. A frequency-domain solution method for the stochastic micro-vibration response of sandwich plates with supported mass is developed based on the Galerkin method and random vibration theory. The expressions of frequency-response functions, response power spectral densities and root-mean-square velocity responses of the plate in terms of the one-third octave frequency band are obtained for micro-vibration evaluation. Finally, numerical results are given to illustrate the large response reduction capacity of the MRVE sandwich plate with supported mass under stochastic support motion excitations, and the influences of MRVE parameters, supported mass and localized magnetic field placement on the micro-vibration response.

• Wind and Structures
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• v.17 no.6
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• pp.609-627
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• 2013

The Yingxian wooden tower in China is currently the tallest wooden tower in the world. It was built in 1056 AD and is 65.86 m high. Field measurements of wind speed and wind-induced response of this tower are conducted. The wind characteristics, including the average wind speed, wind direction, turbulence intensity, gust factor, turbulence integral length scale and velocity spectrum are investigated. The power spectral density and the root-mean-square wind-induced acceleration are analyzed. The structural modal parameters of this tower are identified with two different methods, including the Empirical Mode Decomposition (EMD) combined with the Random Decrement Technique (RDT) and Hilbert transform technique, and the stochastic subspace identification (SSI) method. Results show that strong wind is coming predominantly from the West-South of the tower which is in the same direction as the inclination of the structure. The Von Karman spectrum can describe the spectrum of wind speed well. Wind-induced torsional vibration obviously occurs in this tower. The natural frequencies identified by EMD, RDT and Hilbert Transform are close to those identified by SSI method, but there is obvious difference between the identified damping ratios for the first two modes.

• Earthquakes and Structures
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• v.23 no.3
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• pp.259-269
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• 2022

In this study, we introduce a canonical correlation analysis method to accurately assess the tunnel damage potential of ground motion. The proposed method can retain information relating to the initial variables. A total of 100 ground motion records are used as seismic inputs to analyze the dynamic response of three different profiles of tunnels under deep and shallow burial conditions. Nine commonly used ground motion parameters were selected to form the canonical variables of ground motion parameters (GMP_CCA). Five structural dynamic response parameters were selected to form canonical variables of structural dynamic response parameters (DRP_CCA). Canonical correlation analysis is used to maximize the correlation coefficients between GMP_CCA and DRP_CCA to obtain multivariate ground motion parameters that can be used to comprehensively assess the tunnel damage potential. The results indicate that the multivariate ground motion parameters used in this study exhibit good stability, making them suitable for evaluating the tunnel damage potential induced by ground motion. Among the nine selected ground motion parameters, peck ground acceleration (PGA), peck ground velocity (PGV), root-mean-square acceleration (RMSA), and spectral acceleration (Sa) have the highest contribution rates to GMP_CCA and DRP_CCA and the highest importance in assessing the tunnel damage potential. In contrast to univariate ground motion parameters, multivariate ground motion parameters exhibit a higher correlation with tunnel dynamic response parameters and enable accurate assessment of tunnel damage potential.

• International Journal of High-Rise Buildings
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• v.1 no.1
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• pp.15-19
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• 2012

Occupant footfalls are often the most critical source of floor vibration on upper floors of buildings. Floor motions can degrade the performance of imaging equipment, disrupt sensitive research equipment, and cause discomfort for the occupants. It is essential that low-vibration environments be provided for functionality of sensitive spaces on floors above grade. This requires a sufficiently stiff and massive floor structure that effectively resists the forces exerted from user traffic. Over the past 25 years, generic vibration limits have been developed, which provide frequency dependent sensitivities for wide classes of equipment, and are used extensively in lab design for healthcare and research facilities. The same basis for these curves can be used to quantify acceptable limits of vibration for human comfort, depending on the intended occupancy of the space. When available, manufacturer's vibration criteria for sensitive equipment are expressed in units of acceleration, velocity or displacement and can be specified as zero-to-peak, peak-to-peak, or root-mean-square (rms) with varying frequency ranges and resolutions. Several approaches to prediction of floor vibrations are currently applied in practice. Each method is traceable to fundamental structural dynamics, differing only in the level of complexity assumed for the system response, and the required information for use as model inputs. Three commonly used models are described, as well as key features they possess that make them attractive to use for various applications. A case study is presented of a tall building which has fitness areas on two of the upper floors. The analysis predicted that the motions experienced would be within the given criteria, but showed that if the floor had been more flexible, the potential exists for a locked-in resonance response which could have been felt over large portions of the building.

• Geomechanics and Engineering
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• v.34 no.6
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• pp.697-726
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• 2023

Brittleness as an important property of rock plays a crucial role both in the failure process of intact rock and rock mass response to excavation in engineering geological and geotechnical projects. Generally, rock brittleness indices are calculated from the mechanical properties of rocks such as uniaxial compressive strength, tensile strength and modulus of elasticity. These properties are generally determined from complicated, expensive and time-consuming tests in laboratory. For this reason, in the present research, an attempt has been made to predict the rock brittleness indices from simple, inexpensive, and quick laboratory test results namely dry unit weight, porosity, slake-durability index, P-wave velocity, Schmidt rebound hardness, and point load strength index using multiple linear regression, exponential regression, support vector machine (SVM) with various kernels, generating fuzzy inference system, and regression tree ensemble (RTE) with boosting framework. So, this could be considered as an innovation for the present research. For this purpose, the number of 39 rock samples including five igneous, twenty-six sedimentary, and eight metamorphic were collected from different regions of Iran. Mineralogical, physical and mechanical properties as well as five well known rock brittleness indices (i.e., B₁, B₂, B₃, B₄, and B₅) were measured for the selected rock samples before application of the above-mentioned machine learning techniques. The performance of the developed models was evaluated based on several statistical metrics such as mean square error, relative absolute error, root relative absolute error, determination coefficients, variance account for, mean absolute percentage error and standard deviation of the error. The comparison of the obtained results revealed that among the studied methods, SVM is the most suitable one for predicting B₁, B₂ and B₅, while RTE predicts B₃ and B₄ better than other methods.