This study explores a super-elastic memory alloy re-centering damper device and investigates its performance in improving the response of steel frame structures subjected to multi-level seismic hazard. The configuration of the device was initially proposed by the authors in a different paper. The proposed super-elastic memory alloy re-centering damper (SMARD) counts on high-performance shape memory alloy (SMA) bars for re-centering capability and employs friction springs to augment its deformation capacity. First of all, this study explores the super-elastic response of NiTiHfPd SMAs under various conditions and illustrates their application into seismic applications. In order to collect experimental data, uniaxial tests are conducted on super-elastic NiTiHfPd SMAs in the temperature range of -35ºC to 25ºC and at the loading frequencies of 0.05 Hz to 1.0 Hz with four different strain amplitudes. The effects of loading rate and temperature on super-elastic characteristics of NiTiHfPd SMAs are examined. Then, an analytical model of six-story and nine-story steel special moment frame buildings with installed SMARDs is developed to determine the dynamic response of the building. Finally, nonlinear response time history analyses are conducted to assess the behavior of controlled and uncontrolled buildings under 44 ground motion records. Results show that SMARDs can enormously mitigate the dynamic response of steel frame structures at different seismic hazard levels and, at the same time, enhance their post-earthquake functionality.

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Hysteresis phenomena have been observed in different branches of physics and engineering sciences. Therefore several models have been proposed for hysteresis simulation in different fields; however, almost neither of them can be utilized universally. In this paper by inspiring of Preisach Neural Network which was inspired from Preisach model that basically stemmed from Madelungs rules and using the learning capability of the neural networks, an adaptive universal model for hysteresis is introduced and called Extended Preisach Neural Network Model (XPNN). It is comprised of input, output and, two hidden layers. The input and output layers contain linear neurons while the first hidden layer incorporates neurons called Deteriorating Stop (DS) neurons, which their activation function follows DS operator. DS operator can generate noncongruent hysteresis loops. The second hidden layer includes Sigmoidal neurons. Adding the second hidden layer, helps neural network learn non-Masing and asymmetric hysteresis loops very smoothly. At the input layer, Besides, x(t) which is input data, ẋ(t), the rate at which x(t) changes, is included as well in order to give XPNN the capability of learning rate-dependent hysteresis loops. Hence, the proposed approach has capability of the simulation of the both rate independent and rate dependent hysteresis with either congruent or noncongruent loops as well as symmetric and asymmetric loops. A new hybridized algorithm has been adopted for training of the XPNN which is based on combination of GA and the optimization method of sub-gradient with space dilatation. The generality of the proposed model has been evaluated by applying it on various hystereses from different areas of engineering with different characteristics. The results show that the model is successful in the identification of the considered hystereses. The proposed neural network shows excellent agreement with experimental data.

PurposeThe purpose is to reduce round-off errors in numerical simulations. In the numerical simulation, different kinds of errors may be created during analysis. Round-off error is one of the sources of errors. In numerical analysis, sometimes handling numerical errors is challenging. However, by applying appropriate algorithms, these errors are manageable and can be reduced. In this study, five novel topological algorithms were proposed in setting up a structural flexibility matrix, and five different examples were used in applying the proposed algorithms. In doing so round-off errors were reduced remarkably.Design/methodology/approachFive new algorithms were proposed in order to optimize the conditioning of structural matrices. Along with decreasing the size and duration of analyses, minimizing analytical errors is a critical factor in the optimal computer analysis of skeletal structures. Appropriate matrices with a greater number of zeros (sparse), a well structure and a well condition are advantageous for this objective. As a result, a problem of optimization with various goals will be addressed. This study seeks to minimize analytical errors such as rounding errors in skeletal structural flexibility matrixes via the use of more consistent and appropriate mathematical methods. These errors become more pronounced in particular designs with ill-suited flexibility matrixes; structures with varying stiffness are a frequent example of this. Due to the usage of weak elements, the flexibility matrix has a large number of non-diagonal terms, resulting in analytical errors. In numerical analysis, the ill-condition of a matrix may be resolved by moving or substituting rows; this study examined the definition and execution of these modifications prior to creating the flexibility matrix. Simple topological and algebraic features have been mostly utilized in this study to find fundamental cycle bases with particular characteristics. In conclusion, appropriately conditioned flexibility matrices are obtained, and analytical errors are reduced accordingly.Findings(1) Five new algorithms were proposed in order to optimize the conditioning of structural flexibility matrices. (2) A JAVA programming language was written for all five algorithms and a friendly GUI software tool is developed to visualize sub-optimal cycle bases. (3) Topological and algebraic features of the structures were utilized in this study.Research limitations/implicationsThis is a multi-objective optimization problem which means that sparsity and well conditioning of a matrix cannot be optimized simultaneously. In conclusion, well-conditioned flexibility matrices are obtained, and analytical errors are reduced accordingly.Practical implicationsEngineers always finding mathematical modeling of real-world problems and make them as simple as possible. In doing so, lots of errors will be created and these errors could cause the mathematical models useless. Applying decent algorithms could make the mathematical model as precise as possible.Social implicationsErrors in numerical simulations should reduce due to the fact that they are toxic for real-world applications and problems.Originality/valueThis is an original research. This paper proposes five novel topological mathematical algorithms in order to optimize the structural flexibility matrix.