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An efficient methodology is proposed to find optimal shape of arch dams on the basis of constrained natural frequencies. The optimization is carried out by virtual sub population (VSP) evolutionary algorithm employing real values of design variables. In order to reduce the computational cost of the optimization process, the arch dam natural frequencies are predicted by properly trained back propagation (BP) and wavelet back propagation (WBP) neural networks. The WBP network provides better generalization compared with the standard BP network. The numerical results demonstrate the computational merits of the proposed methodology for optimum design of arch dams.

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Endurance Time Acceleration Functions are specially predesigned intensifying excitation functions that their amplitude increases with time. On the other hand, wavelet transform is a mathematical tool that indicates time variations of frequency in a signal. In this paper, an approach is presented for generating endurance time acceleration functions (ETAFs) whose response spectrum is compatible with the European Code regulations (EC8) elastic spectrum. Method applied is a modification of data in time and frequency domain. For this purpose, wavelet transform has been used to decompose a series of random points to several levels such that each level covers a special range of frequency, then every level is divided into the numbers of equal time intervals and each interval of time is multiplied by a variable. Subsequently, the mathematical unconstrained optimization algorithm is used to calculate the variables and minimize error between response and target spectra. The prosed procedure is used in two methods. Then with two methods, two different acceleration functions are produced.

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With the development of the technology and increase of human dependency on structures, healthy structures play an important role in people lives and communications. Hence, structural health monitoring has been attracted strongly in recent decades. Improvement of measuring instruments made signal processing as a powerful tool in structural heath monitoring. Wavelet transform invention causes a great evolution in signal processing. Wavelet transform decomposes a signal into several groups based on scaled and translated basic functions. In this study, a novel methodology based on wavelet transform using complex Morlet wavelet has been introduced for system identification. This process includes a multivariable constrained optimization problem for selecting suitable complex Morlet wavelet. Using selected wavelet, modal parameters and flexibility matrix of structure can be estimated properly. Because of small modal participation of higher mode using finite number of modes leads to flexibility matrix with acceptable accuracy. Since damages cause change in structural properties, a damage index based on flexibility matrix has been applied and its performance has been investigated in some structures.

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In  this  paper  a  new  method  is  presented  for  structural  damage  identification.  First,  the damaged structure is  excited by short  duration impact acceleration  and then, the  recorded structural displacement time history responses under free vibration conditions are analyzed by Continuous Wavelet Transform (CWT) and Wavelet Residual Force (WRF) is calculated. Finally, an effective damage-sensitive index is proposed to localize structural damage with a high  level  of  accuracy.  The  presented  method  is  applied  to  three  numerical  examples, namely  a  fifteen-story  shear  frame,  a  concrete  cantilever  beam  and  a  four-story,  two-bay plane steel frame, under different damage patterns, to detect structural damage either in free noise or noisy states. In addition, some comparative studies are carried out to compare the presented  index  with  other  relative  indices.  Obtained  results,  not  only  illustrate  the  good performance of the presented approach for damage identification in engineering structures, but  also  introduce  it  as  a  stable  and  viable  strategy  especially  when  the  input  data  are contaminated with different levels of random noises.

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Cumulative absolute velocity (CAV), Arias intensity (AI), and characteristic intensity (CI) are measurable characteristics to show collapse potential of structures, evaluation of earth movement magnitude, and detection of structural failure in an earthquake. In this paper, parameters which describe three characteristics of ground motion have been investigated by using wavelet transforms (WT). In fact, in this paper, a series of twenty eight earthquake records (ER) are decomposed to a pre-defined certain levels by the use of WT. The high and low frequencies are separated. Since higher frequencies do not have any significant effect on the ER, then the low frequencies of ER have been used. For this purpose, each ER is decomposed into 5 levels. Then, for low frequencies of ER, the CAV, AI, and CI are calculated for each level and the results are compared with the values of CAV, AI, and CI which have been computed for the original ER. The results indicate that the value of error is less than 1 percent in the first and second level and this value is less than 10 percent for the third level. In addition, this value is more than 15 percent for the fourth and fifth levels. If the acceptable value for error is considered to be less than 10 percent, it is recommended to use the third level of decomposition for determining these parameters, since the value of error is low and also, the required time is reduced.

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Damage assessment is one of the crucial topics in the operation of structures. Multiplicities of structural elements and joints are the main challenges about damage assessment of space structure. Vibration-based damage evaluation seems to be effective and useful for application in industrial conditions and the low-cost. A method is presented to detect and assess structural damages from changes in mode shapes. First, the mechanism of using two-dimensional continuous wavelet transform is applied for damage localization. Second, finite element model updating technique is utilized as an inverse optimization problem by applying the charged system search algorithm to assess the damage in each element sited in the first stage. The study indicates the potentiality of the developed code to assess the damages of space structures without concerning about the size and shape of structure. A series of numerical examples with different damage scenarios have been carried out in the double layer space structures and the results confirm the reliability and applicability of introduced method.

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Seismic analysis of structures is a process for estimating the response of structures subjected to earthquakes. For this purpose, the earthquake ­is analyzed using the wavelet theory. In this paper, the primary signal of the earthquake is decomposed through a discrete wavelet transform, and their corresponding response spectrum is obtained. Then, the percentage difference between the decomposed signals and the main one is computed. Therefore, for different earthquakes, a comparison between the response spectrum is studied in various types of dams. The acceleration, velocity, and displacement responses are computed and compared to achieve an appropriate level of decomposition, which can be used instead of the primary signal. Therefore, the decomposition process leads to attaining acceptable accuracy as well as low computational cost. The investigation revealed that the acceleration, velocity, and displacement responses spectrum are suitable up to the third level of decomposition for the small and medium dams, whereas for large dams, up to the fifth level of decomposition is suitable.