A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Earthquake Damage Prediction and Rapid Assessment of Building Damage Using Machine Learning.
A quick forecast of the region’s structural damage following an earthquake is crucial for community relief and rescue efforts. Following an earthquake, getting detailed information about buildings in seismic disaster zones is frequently impossible. The information available is essential to estimate seismic damage to urban structures accurately. To address the aforementioned issues, we propose a novel method for quickly assessing building damage through extensive use of field research and data collected from earth observation. A model is created and trained to predict building damage from an earthquake. The algorithm used to indicate the building damage from the quake is Elastic net regression. The elastic net is a regularized regression method used to predict building damage from an earthquake. The proposed Elastic net regression is validated with standard accuracy and effectiveness compared with the Random forest and decision tree methods. The proposed model’s outcomes show that elastic net regression is the most effective predictive model for predicting building damage.
Earthquake Damage Prediction and Rapid Assessment of Building Damage Using Machine Learning.
A quick forecast of the region’s structural damage following an earthquake is crucial for community relief and rescue efforts. Following an earthquake, getting detailed information about buildings in seismic disaster zones is frequently impossible. The information available is essential to estimate seismic damage to urban structures accurately. To address the aforementioned issues, we propose a novel method for quickly assessing building damage through extensive use of field research and data collected from earth observation. A model is created and trained to predict building damage from an earthquake. The algorithm used to indicate the building damage from the quake is Elastic net regression. The elastic net is a regularized regression method used to predict building damage from an earthquake. The proposed Elastic net regression is validated with standard accuracy and effectiveness compared with the Random forest and decision tree methods. The proposed model’s outcomes show that elastic net regression is the most effective predictive model for predicting building damage.
Earthquake Damage Prediction and Rapid Assessment of Building Damage Using Machine Learning.
Natarajan, Yuvaraj (author) / Wadhwa, Gitanjali (author) / Ranganathan, Preethi Akshaya (author) / Natarajan, Karthika (author)
2023-01-24
548872 byte
Conference paper
Electronic Resource
English
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