After selecting real seismic records it is necessary to scale these records to match the intensity of the earthquake expected for the site. Generally, scaling can be made by ground motions uniform scaling in time domain which is simply scaled up or down the ground motions uniformly to best match (in average) the target spectrum within a period range of interest. It’s an engineer’s job to find the best scaling factors to best match the target spectrum, which is a complex task, so we employed the Genetic Algorithm (GA) in finding those scaling factors to achieve the best results.
Genetic Algorithms (GAs) are probably the best-known types of artificial evolution search methods based on natural selection and mechanisms of population genetics. These algorithms are often applied to large, complex problems that are non-linear with multiple local optima.
The power of the genetic algorithms is inherent in its capability to adapt. In natural systems, species adapt to the environment through successive interactions and generations subject to the environment. After several consecutive generations, only those species that can adapt well to the environment survive and the rest disappear. In mathematical terms, individuals are analogous to problem variables and environment is the stated problem. The final generation of the variable strings that can adapt to the problem is the solution.
In this study, basic methodologies of the GA and the scaling procedures are summarized and the scaling criteria of real time history records to satisfy the Syrian design code are discussed. The traditional time domain scaling procedures and the scaling procedures using GA are used to scale a number of the available real records to match the Syrian design spectra. The resulting time histories of the procedures are investigated and compared in terms of meeting criteria.
"Real Records Scaling Factors Optimization to Fit the Syrian Design Spectra using Genetic Algorithm,"
Journal of the Arab American University مجلة الجامعة العربية الامريكية للبحوث: Vol. 2
, Article 7.
Available at: https://digitalcommons.aaru.edu.jo/aaup/vol2/iss2/7