Prediction and analysis model for ground peak acceleration based on XGBoost and SHAP

QI Wanwan; SUN Rui; ZHENG Tong; QI Jinlei

doi:10.11779/CJGE20220417

Volume 45 Issue 9

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Chinese Journal of Geotechnical Engineering > 2023 > 45(9): 1934-1943. > DOI: 10.11779/CJGE20220417

QI Wanwan, SUN Rui, ZHENG Tong, QI Jinlei. Prediction and analysis model for ground peak acceleration based on XGBoost and SHAP[J]. Chinese Journal of Geotechnical Engineering, 2023, 45(9): 1934-1943. DOI: 10.11779/CJGE20220417

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Prediction and analysis model for ground peak acceleration based on XGBoost and SHAP

QI Wanwan^{1, 2,},
SUN Rui^{1, 2},
ZHENG Tong^{1, 2, ,},
QI Jinlei^{1, 2}

1.
Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, China
2.
Key Laboratory of Earthquake Disaster Mitigation, Ministry of Emergency Management, Harbin 150080, China

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Received Date: April 06, 2022
Available Online: September 06, 2023

Graphical Abstract

Abstract

Abstract

In order to establish a prediction method for the ground peak acceleration (PGA) that does not depend on the soil constitutive model but only on the ground motion and site characteristics, six characteristic parameters are chosen through the feature selection based on 3104 groups of bedrock and surface seismic records collected from the KiK-net strong-motion seismograph network of Japan. Then, the input ground motion characteristics are characterized through the peak bedrock acceleration and predominant frequency, and the site characteristics are characterized by the soil depth at shear wave velocity of 800 m/s, site fundamental period, bedrock shear wave velocity and surface shear wave velocity. The XGBoost model in machine learning is used to establish the prediction models for the PGA based on the above six characteristics. It is shown that the prediction results of the XGBoost prediction model are stable and can be used to predict the PGA better by comparing the records and one-dimensional numerical simulation methods. The coefficients of determination of the training set and the test set are greater than 0.925, and the mean absolute percentage errors are about 20%, which is obviously better than the one-dimensional numerical simulation methods. At the same time, the SHAP is introduced to analyze the influence and dependence between the input characteristics and the predicted results, which enhances the interpretability of the model and provides reliability support for the predicted results.
- machine learning,
- XGBoost,
- prediction of PGA,
- SHAP,
- interpretability

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References

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