Performance Analysis of Structures under Extreme Environmental Conditions
Implementation plan:
Step 1: Initially, we will generate, collect and load the building CSV dataset.
Step 2: Then, we pre-process the data to improve its quality and usability.
Step 3: Next, we extract the features from the pre-processed data using Hyperclusternet Method with “Hyper-Parameter Tuning of Deep Neural Networks” and “Deep-Learning Based Architectures”
Step 4: Next, we integrate ground-level LiDAR data to accurately model building textures and materials, improving resilience evaluation.
Step 5: Next, we implement the “Controlled rocking steel braced frames (CRSBFs)” to handle the structural analysis and assess performance under these conditions.
Step 6: Next we implement Performance-based multi-criteria decision-making (PBMCDM) to find climate change effects on building performance in extreme conditions.
Step 7: Next, we track concrete strength and other extreme environmental conditions using Real-Time Insight Mesh (RTIM)
Step 8: Finally, we plot performance for the following metrics:
8.1 No. of Epochs Vs. Accuracy(%)
8.2 No. of Epochs Vs. Recall(%)
8.3 No. of Buildings Vs. Structural stability index
8.4 No. of Buildings Vs. Climate Exposure Vulnerability
8.5 No. of Buildings Vs. Environmental Impact Index
Software Requirement:
1. Development Tool: Python – 3.11.4 or above
2. Operating System: Windows 10 (64-bit) or above
Note:
1) If the above plan does not satisfy your requirement, please provide the processing details, like the above step-by-step.
2) Please note that this implementation plan does not include any further steps after it is put into implementation.
3) If the above plan satisfies your requirement please confirm with us.
4) We develop simulation based projects only, not in real time
We perform the EXISTING Approach based on the Reference 1 Title:-High rise office building makeovers—Exploiting architectural and engineering factors in designing sustainable buildings in different climate zones.
