Explainable Machine learning on New Zealand strong motion for PGV and PGA

Somala, S.; Chanda, Sarit; Karthikeyan, Karthika; Mangalathu, Sujith

doi:10.1016/j.istruc.2021.10.085

Cited by 23 publications

(5 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method enables interpretability in practical applications of machine-learning models, making explicit the hidden the reasoning behind why a model produces certain predictions. To date, the visualization of variables using the SHAP method has been applied to address the black box problem in machine-learning models, and most of these efforts have achieved excellent results [40][41][42][43][44]. The SHAP method fundamentally decomposes the predictions to indicate the influence of each explanatory variable.…”

Section: Description Of Shapley Additive Explanationsmentioning

confidence: 99%

Prediction and Factor Analysis of Liquefaction Ground Subsidence Based on Machine-Learning Techniques

Karimai,

Liu,

Maruyama

2024

Applied Sciences

View full text Add to dashboard Cite

Liquefaction is a significant challenge in the fields of earthquake risk assessment and soil dynamics, as it has the potential to cause extensive damage to buildings and infrastructure through ground failure. During the 2011 Great East Japan Earthquake, Urayasu City in the Chiba Prefecture experienced severe soil liquefaction, leading to evacuation losses due to the effect of the liquefaction on roads. Therefore, developing quantitative predictions of ground subsidence caused by liquefaction and understanding its contributing factors are imperative in preparing for potential future mega-earthquakes. This research is novel because previous research primarily focused on developing predictive models for determining the presence or absence of liquefaction, and there are few examples available of quantitative liquefaction magnitude after liquefaction has occurred. This research study extracts features from existing datasets and builds a predictive model, supplemented by factor analysis. Using the Cabinet Office of Japan’s Nankai Trough Megathrust Earthquake model, liquefaction-induced ground subsidence was designated as the dependent variable. A gradient-boosted decision-tree (GDBT) prediction model was then developed. Additionally, the Shapley additive explanations (SHAP) method was employed to analyze the contribution of each feature to the prediction results. The study found that the XGBoost model outperformed the LightGBM model in terms of predictive accuracy, with the predicted values closely aligned with the actual measurements, thereby proving its effectiveness in predicting ground subsidence due to liquefaction. Furthermore, it was demonstrated that liquefaction assessments, which were previously challenging, can now be interpreted using SHAP factors. This enables accountable wide-area prediction of liquefaction-induced ground subsidence.

show abstract

Section: Description Of Shapley Additive Explanationsmentioning

confidence: 99%

Prediction and Factor Analysis of Liquefaction Ground Subsidence Based on Machine-Learning Techniques

Karimai,

Liu,

Maruyama

2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…The SHAP algorithm is widely used in order to explain the impact of different input features on the predictions of the machine learning models [40][41][42][43][44][45]. The SHAP methodology is based on an additive feature attribution procedure in which an explanation function g is defined as a linear combination of simplified input values 𝑥 ′ ∈ {0,1} 𝑀 , where M is the total number of simplified input features.…”

Section: Interpretation Of the Machine Learning Models Using Shap App...mentioning

confidence: 99%

Predictive Modeling of Recycled Aggregate Concrete Beam Shear Strength Using Explainable Ensemble Learning Methods

Çakıroğlu

Bekdaş

2023

Sustainability

View full text Add to dashboard Cite

Construction and demolition waste (CDW) together with the pollution caused by the production of new concrete are increasingly becoming a burden on the environment. An appealing strategy from both an ecological and a financial point of view is to use construction and demolition waste in the production of recycled aggregate concrete (RAC). However, past studies have shown that the currently available code provisions can be unconservative in their predictions of the shear strength of RAC beams. The current study develops accurate predictive models for the shear strength of RAC beams based on a dataset of experimental results collected from the literature. The experimental database used in this study consists of full-scale four-point flexural tests. The recycled coarse aggregate (RCA) percentage, compressive strength (fc′), effective depth (d), width of the cross-section (b), ratio of shear span to effective depth (a/d), and ratio of longitudinal reinforcement (ρw) are the input features used in the model training. It is demonstrated that the proposed machine learning models outperform the existing code equations in the prediction of shear strength. State-of-the-art metrics of accuracy, such as the coefficient of determination (R2), mean absolute error, and root mean squared error, have been utilized to quantify the performances of the ensemble machine learning models. The most accurate predictions could be obtained from the XGBoost model, with an R2 score of 0.94 on the test set. Moreover, the impact of different input features on the machine learning model predictions is explained using the SHAP algorithm. Using individual conditional expectation plots, the variation of the model predictions with respect to different input features has been visualized.

show abstract

“…SHapley Additive exPlanations (SHAP) summary plots are visual depictions of the influence each input variable has on the predicted quantity. In recent years, the SHAP approach has been frequently applied in order to explain ML models [54][55][56][57][58][59]. In SHAP summary plots, each data sample is represented by a dot, the color of which is determined by the feature value.…”

Section: Shapley Additive Explanations (Shap)mentioning

confidence: 99%

Explainable Data-Driven Ensemble Learning Models for the Mechanical Properties Prediction of Concrete Confined by Aramid Fiber-Reinforced Polymer Wraps Using Generative Adversarial Networks

Cakiroglu

2023

Applied Sciences

View full text Add to dashboard Cite

The current study offers a data-driven methodology to predict the ultimate strain and compressive strength of concrete reinforced by aramid FRP wraps. An experimental database was collected from the literature, on which seven different machine learning (ML) models were trained. The diameter and length of the cylindrical specimens, the compressive strength of unconfined concrete, the thickness, elasticity modulus and ultimate tensile strength of the FRP wrap were used as the input features of the machine learning models, to predict the ultimate strength and strain of the specimens. The experimental dataset was further enhanced with synthetic data using the tabular generative adversarial network (TGAN) approach. The machine learning models’ performances were compared to the predictions of the existing strain capacity and compressive strength prediction equations for aramid FRP-confined concrete. The accuracy of the predictive models was measured using state-of-the-art statistical metrics such as the coefficient of determination, mean absolute error and root mean squared error. On average, the machine learning models were found to perform better than the available equations in the literature. In particular, the extra trees regressor, XGBoost and K-nearest neighbors algorithms performed significantly better than the remaining algorithms, with R2 scores greater than 0.98. Furthermore, the SHapley Additive exPlanations (SHAP) method and individual conditional expectation (ICE) plots were used to visualize the effects of various input parameters on the predicted ultimate strain and strength values. The unconfined compressive strength of concrete and the ultimate tensile strength of the FRP wrap were found to have the greatest impact on the machine learning model outputs.

show abstract

Explainable Machine learning on New Zealand strong motion for PGV and PGA

Cited by 23 publications

References 19 publications

Prediction and Factor Analysis of Liquefaction Ground Subsidence Based on Machine-Learning Techniques

Prediction and Factor Analysis of Liquefaction Ground Subsidence Based on Machine-Learning Techniques

Predictive Modeling of Recycled Aggregate Concrete Beam Shear Strength Using Explainable Ensemble Learning Methods

Explainable Data-Driven Ensemble Learning Models for the Mechanical Properties Prediction of Concrete Confined by Aramid Fiber-Reinforced Polymer Wraps Using Generative Adversarial Networks

Contact Info

Product

Resources

About