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2 2023

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Optimal retrofit strategy using viscous dampers between adjacent RC and SMRFs prone to earthquake‑induced pounding

Asgarkhani N., Kazemi F., Jankowski R.

Archives of Civil and Mechanical Engineering

2023

Vol. 23, no. 1

art. no. e7, 2023

Nowadays, retrofitting-damaged buildings is an important challenge for engineers. Finding the optimal placement of Viscous Dampers (VDs) between adjacent structures prone to earthquake-induced pounding can help designers to implement VDs with optimizing the cost of construction and achieving higher performance levels for both structures. In this research, the optimal placement of linear and nonlinear VDs between the 3-story, 5-story, and 9-story Steel and RC Moment-Resisting Frames (SMRFs and RC MRFs) is investigated. It is shown that the pounding phenomenon can significantly affect the seismic performance capacities of buildings during earthquakes, and using VDs can improve the seismic limit-state capacities of buildings for retrofitting purposes. For this goal, the seismic limit-state capacities of both colliding structures were assessed using Incremental Dynamic Analysis (IDA) assuming Near-fault Pulse-Like, Near-fault No-Pulse, and Far-Fault seismic records suggested by FEMA-P695. To perform IDAs, structures were modeled according to the seismic codes using a developed algorithm in Matlab and OpenSees software with the ability to remove a collapsed structure during the analysis. The results present an optimal placement for using VDs between structures and also compare the possible conditions to implement VDs. Using these results, engineers can approximately predict the seismic performance levels of both structures prone to earthquake-induced pounding and their final performance after retrofitting. Finally, retrofitting modification factors were proposed to help designers to predict the limit-state performance levels of retrofitted colliding structures without involving complicated and time-consuming analyses.

Machine learning-based seismic response and performance assessment of reinforced concrete buildings

Kazemi F., Asgarkhani N., Jankowski R.

Archives of Civil and Mechanical Engineering

2023

Vol. 23, no. 2

art. no. e94, 2023

Complexity and unpredictability nature of earthquakes makes them unique external loads that there is no unique formula used for the prediction of seismic responses. Hence, this research aims to implement the most well-known Machine Learning (ML) methods in Python software to propose a prediction model for seismic response and performance assessment of Reinforced Concrete Moment-Resisting Frames (RC MRFs). To prepare 92,400 data points of training dataset for developing data-driven techniques, Incremental Dynamic Analyses (IDAs) were performed considering 165 RC MRFs with two-, to twelve-Story elevations having the bay lengths of 5.0 m, 6.1 m, and 7.6 m assuming near-fault seismic excitations. Then, important structural features were considered in datasets to train and test the ML-based prediction models, which were improved with innovative techniques. The results show that improved algorithms have higher R2 values for estimating the Maximum Interstory Drift Ratio (IDRmax), and two improved algorithms of artificial neural networks and extreme gradient boosting can estimate the Median of IDA curves (M-IDAs) of RC MRFs, which can be used to estimate the seismic limit-state capacity and performance assessment of existing or newly constructed RC buildings. To validate the generality and accuracy of the proposed ML-based prediction model, a five-Story RC building with different input features was used, and the results are promising. Therefore, graphical user interface is introduced as user-friendly tool to help researchers in estimating the seismic limit-state capacity of RC buildings, while reducing the computational cost and analytical efforts.