8 research outputs found
Investigation on The Seismic Behavior of Steel MRF with Shape Memory Alloy Equipped Connections
No full textAbstractShape Memory Alloys (SMA) are among the new passive control devices that have gained a large attention due to its inherent features, i.e., recovering the induced residual strains upon unloading (superelastic effect) or by heating (shape memory effect). In this work, the seismic behavior of a set of steel structural models with different number of stories and eccentricities equipped with a type of fixed SMA connections is investigated. Considering an existing SMA connection model in austenite phase, the related moment-rotation behavior is verified through numerical simulation. Then, extensive nonlinear dynamic analyses are performed using a number of 3, 6, 9, and 12 story structural models with 0, 5%, 10%, and 15% eccentricities subjected to different bi-directional earthquake components. The PGA of the earthquake records is scaled to 0.4g and 0.6g to examine the energy dissipation capability of the SMA materials. Similar analyses performed using the same set of structural models, but with all beam-tocolumn connections equipped with SMA connections. The obtained results indicate that while the reduction in drifts of the SMA equipped models with respect to the fixed connection models was not noticeable, their base shears were considerably reduced. The OpenSees program is used for modeling of the connections with SMA materials and performing the numerical analyses
On the Performance of Passivr TMDs in Reducing the Damage in 2-D Concrete Structural Models
No full textAbstractPozzolanic materials, either naturally occurring or artificially made, have long been in practice since the early civilization. In recent years, the utilisation of pozzolanic materials in concrete construction has become increasingly widespread, and this trend is expected to continue in the years ahead because of technological, economical and ecological advantages of the materials. One of the latest additions to the ash family is palm oil fuel ash, a waste material obtained on burning of palm oil husk and palm kernel shell as fuel in palm oil mill boilers, which has been identified as a good pozzolanic material. This paper highlights test results on the performance behavior of palm oil fuel ash (POFA) in reducing the heat of hydration of concrete. Two concrete mixes namely OPC concrete i.e. concrete with 100% OPC as control, and POFA concrete i.e. concrete with 30% POFA and 70% OPC were prepared, and the temperature rise due to heat of hydration in both the mixes was recorded. It has been found that palm oil fuel ash not only reduced the total temperature rise but also delayed the time at which the peak temperature occurred. The results obtained and the observation made clearly demonstrate that the partial replacement of cement by palm oil fuel ash is advantageous, particularly for mass concrete where thermal cracking due to excessive heat rise is of great concern
Numerical study of an innovative SMA based beam-column connection in reducing the seismic response of steel MRF structures
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A New Online Bayesian Approach for the Joint Estimation of State and Input Forces using Response-only Measurements
No full textIn this paper, a recursive Bayesian-filtering technique is presented for the joint estimation of the state and input forces. By introducing new prior distributions for the input forces, the direct transmission of the input into the state is eliminated, which allows removing low-frequency error components from the predictions and estimations. Eliminating such errors is of practical significance to the emerging fatigue monitoring methodologies. Furthermore, this new technique does not require a priori knowledge of the input covariance matrix and provides a powerful method to update the noise covariance matrices in a real-time manner. The performance of this algorithm is demonstrated using one numerical example and compared it with the state-of-the-art algorithms. Contrary to the present methods which often produce unreliable and inaccurate estimations, the proposed method provides remarkably accurate estimations for both the state and input.Financial support from the Hong Kong research grants councils under grant numbers 16234816 and 16212918 is gratefully appreciated. The last author gratefully acknowledges the European Commission for its support of the Marie Sklodowska Curie program through the ETN DyVirt project (GA 764547).
This paper is completed as a part of the second authors PhD dissertation conducted jointly at Sharif University of Technology and the Hong Kong University of Science and Technology. The second author would like to gratefully appreciate kind support and supervision of Professor Fayaz R. Rofooei at Sharif University of Technology
Quantification of Aleatory Uncertainty in Modal Updating Problems using a New Hierarchical Bayesian Framework
No full textIdentification of structural damage requires reliable assessments of damage-sensitive quantities, including natural frequencies, mode shapes, and damping ratios. Lack of knowledge about the correct value of these parameters introduces a particular sort of uncertainty often referred to as epistemic uncertainty. This class of uncertainty is reducible in a sense that it can be decreased by enhancing the modeling accuracy and collecting new information. On the contrary, such damage-sensitive parameters might also have intrinsic randomness arising from unknown phenomena and effects, which gives rise to an irreducible category of uncertainty often referred to as aleatory uncertainty. The present Bayesian modal updating methodologies can produce reasonable quantification of the epistemic uncertainties, while they often fail to account for the aleatory uncertainties. In this paper, a new multilevel (hierarchical) probabilistic modeling framework is proposed to bridge this significant gap in uncertainty quantification and propagation of structural dynamics inverse problems. Since multilevel model calibration schemes establish a complicated model structure associated with additional parameters and variables, their computational costs are often considerable, if not prohibitive. To reduce the computational costs, the modal updating procedure is simplified using a second-order Taylor expansion approximation. This approximation is combined with a Markov chain Monte-Carlo (MCMC) sampling method to compute marginal posterior distributions of quantities of interest. The proposed framework is illustrated using one simple experimental example. As a result, it is demonstrated that the proposed framework surpasses the present Bayesian modal updating methods as it accounts for both the aleatory and epistemic uncertainties.Financial support from the Hong Kong research grants councils under grant numbers 16234816 and 16212918 is gratefully appreciated. The last author gratefully acknowledges the European Commission for its support of the Marie Sklodowska Curie program through the ETN DyVirt project (GA 764547).
This paper is completed as a part of the second authors PhD dissertation conducted jointly at Sharif University of Technology and the Hong Kong University of Science and Technology. The second author would like to gratefully appreciate kind support and supervision of Professor Fayaz R. Rofooei at Sharif University of Technology.
We would also like to express our sincere appreciation to Professor Chih-chen Chang for generously sharing sensors, prototypes, and laboratory facilities
