Meta heuristic active and semi-active control systems of high-rise building

Lavasani, Seyed Hossein Hosseini; Doroudi, Rouzbeh

doi:10.1504/ijstructe.2020.108529

Cited by 17 publications

(2 citation statements)

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“…Since this strategy creates an additional search space, it is expected to increase the random search capacity of the algorithm. 24 In other words, the OTLBO includes three phases: teacher, observer, and learners, which has increased the efficiency of the TLBO algorithm for local and global search in the search space using the observer phase. Principal details of OTLBO are provided by Shahrouzi et al 23 These steps are explained in the following.…”

Section: Tuned the Parameters Of Svr Through Otlbomentioning

confidence: 99%

“…[20][21][22] Among them, observer-teacher-learner-based optimization (OTLBO) is selected here as it has already been successful in several engineering tasks. [23][24][25][26] Furthermore, in order to decrease computational burden, the acceleration signals are separated into various time-related components, and the best part is chosen for investigation by taking into account the proper tower frequency identification. The flowchart of the proposed model is shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

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Predicting acceleration response of super‐tall buildings by support vector regression

Doroudi,

Hosseini Lavassani,

Shahrouzi

2023

Structural Design Tall Build

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SummaryRecovering missing data of defective sensors is an important challenge for reliability of structural health monitoring systems and misjudgment of structural conditions. The present study concerns predicting corrupted data of lost sensors by support vector regression (SVR). The method is tuned via optimizing their parameters by observer–teacher–learner‐based optimization as a powerful meta‐heuristic algorithm. Their performances are compared in predicting the acceleration responses of two real‐world super‐tall buildings: Milad Tower, located in Tehran, and Canton Tower in Guangzhou. Also the minimum required of sensors to predict the acceleration responses are investigated. The results are evaluated by five statistical indices exhibiting that the optimized SVR has sufficient capacity to predict acceleration responses of both towers with limited number of sensors. The proposed method is of practical interest as it does not require finite element modeling of the structure to derive its dynamic responses.

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Section: Tuned the Parameters Of Svr Through Otlbomentioning

confidence: 99%