I need you to validate the ″Seismic fragility analysis of generalized MDOF syste

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I need you to validate the ″Seismic fragility analysis of generalized MDOF syste

I need you to validate the ″Seismic fragility analysis of generalized MDOF systems using machine learning″ study by following the steps outlined below. Please carefully read and follow these instructions:
Step 1: Develop Non-linear Steel Moment Frames
1. Develop 6 or fewer simple non-linear steel moment frames using ETABS software. These frames do not need to be as complex as those in the original study.
o Structural Configurations and Load Combinations: Use structural configurations and load combinations sourced from ASCE 7-16,AISC 360-16 (2016) or other places (American standard code).
Step 2: Construct Fragility Curve
1. Select one frame (the 6-story, 3-bay steel moment frame mentioned above) and follow the steps in the study to construct its PSDM fragility curve.
o Reference Example: In the study, the fragility curve of a 5-story, 3-bay steel moment frame is constructed. Use this as a reference to construct the fragility curve for the 6-story frame.
Step 3: Generate Training and Testing Datasets
1. For the developed frames, perform non-linear time history analyses using 30-70 ground motions sourced from the NGA-West2 database by PEER.
2. Generate the training and testing datasets for the ML models using the results from these analyses.
Step 4: Develop and Validate ML Models
1. Develop the ML models using the open-source Python package Scikit-learn 0.22.2.
2. Train and test the following models:
o Random Forest (RF)
o Adaptive Boosting (AdaBoost)
o Gradient Boosting Regression Tree (GBRT)
o Extreme Gradient Boosting (XGBoost)
3. Validate the performance of the ML models against the results in the study. Ensure that the results are consistent with those reported.
Step 5: Application of Proposed ML Models
1. Use 5 different steel moment frames (not the ones used for developing the ML models) to validate the prediction accuracy of the proposed ML models (GBRT-PSDMs and XGBoost-PSDMs).
o Compare Results: Compare the fragility curves obtained from the ML-PSDMs to those from conventional PSDMs.

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