Aims: The aim of the present paper is to evaluate the behavior of slit friction hybrid dampers (SFHD) on steel structures. Therefore, the behavior moment resisting steel frames of structures in original stats and structures equipped with hybrid damper with two different types of behavior was analyzed and evaluated. Background: The recent study evaluated the combined effect of shear-friction dampers and slit dampers with measurements of non-uniform strips in seismic protection for different levels of energy. The recent study was carried out a about hybrid dampers, consisting of friction and split dampers in response to small and large earthquakes. Previous results have shown the ability of inactive hybrid systems in improving the reaction of structures to traditional lateral-systems. Kim and Shin showed that structures consisted of hybrid dampers needed less repair cost and time. Methods: Pushover and time history were carried out on original structures and structures equipped with dampers, in 5 and 10 stories structures. Results: Analysis about the probability of collapse showed about 30% and 84%. Conclusion: According to the result, by adding the SFHD, increased stiffness by 17% in retrofitted structures such as drift and displacement of roof decreases by 27% and 20% in push over analysis, respectively. Also, displacement in time history analysis up to 55% reduces in average. Also, the results of the IDA show that adding the SFHD to structures significantly increases by 55% the spectral acceleration capacity in structures.
Deterioration components (DCs) of reinforced columns (RC) are important for predication the seismic behavior and performance of RC structures. Theses DCs parameters include: Plastic chord rotation from yield to cap (θp), post capping plastic rotation capacity from the cap to point of zero strength (θpc) and normalized energy dissipation capacity relation between deterioration components of RC columns with different properties(λ). This paper investigates several machine learning (ML) algorithms for the prediction of DCs, referred to as ML-DCs, based on the results of 255 experimental reinforced concrete columns tests conducted from 1973 to 2002. The performance of the models are considered using regression metrics. In this regard, machine learning algorithms such as Least Squares Support Vector Machine (Lssvm), AdaBoost, Artificial Neural Network (ANN), Random Forest (RF), Support Vector Regression (SVR) and XGBoost are applied and finally the results obtained from the models are compared with experimental relationships. The XGBoost algorithm provides enhanced accuracy of 95% for θp, 84% for θpc, and 93% for λ comparing to the others. Also, the results of machine learning algorithms indicate that the results obtained from the machine learning models are more effective than the empirical relationships achieved by the test results.
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