Machine Learning Algorithms for the Prediction of the Seismic Response of Rigid Rocking Blocks

Karampinis, Ioannis; Bantilas, Kosmas E.; Kavvadias, Ioannis E.; Iliadis, Lazaros; Elenas, Anaxagoras

doi:10.3390/app14010341

Cited by 3 publications

(1 citation statement)

References 68 publications

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“…By introducing global search through genetic algorithms, GA-SVM searches for better model parameters in the parameter space of SVMs and takes full advantage of the excellent generalization performance of SVMs in the training set to ensure good adaptation to unseen data. It is especially suitable for dealing with high-dimensional and complex classification problems by exploiting the synergy between the powerful performance of SVMs and the global search strategy of genetic algorithms to make the model have a stronger generalization capability [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Methods for Identifying Effective Microseismic Signals in a Strong-Noise Environment Based on the Variational Mode Decomposition and Modified Support Vector Machine Models

Shen,

Wang,

Zeng

et al. 2024

Applied Sciences

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The environment for acquiring microseismic signals is always filled with complex noise, leading to the presence of abundant invalid signals in the collected data and greatly disturbing effective microseismic signals. Regarding the identification of effective microseismic signals with a low signal-to-noise ratio, a method for identifying effective microseismic signals in a strong-noise environment by using the variational mode decomposition (VMD) and genetic algorithm (GA)-based optimized support vector machine (SVM) model is proposed. Microseismic signals with a low signal-to-noise ratio are adaptively decomposed into several intrinsic mode functions (IMFs) by using VMD. The characteristics of such IMFs are extracted and used as a basis for the determination of signal validity. The SVM model is optimized by utilizing GA to obtain the optimal penalty factor c and the kernel function parameter g. The availability of IMF components is judged by the optimized SVM model, based on which the effectiveness of microseismic signals is further identified. By applying the algorithm to the microseismic signals with artificially added noise, the effective microseismic signals and ineffective noise are discriminated, verifying the feasibility of the algorithm. After processing the microseismic records collected in the field, we effectively judge the effectiveness of microseismic signals, suppress the interfering noise in the data and greatly improve the signal-to-noise ratio of the seismic records. The results show that the method for identifying effective microseismic signals based on VMD and GA-SVM can well discriminate between effective and ineffective microseismic signals, which is very significant and provides technical support for microseismic monitoring in a strong-noise environment.

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Section: Introductionmentioning

confidence: 99%

Methods for Identifying Effective Microseismic Signals in a Strong-Noise Environment Based on the Variational Mode Decomposition and Modified Support Vector Machine Models

Shen,

Wang,

Zeng

et al. 2024

Applied Sciences

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Machine Learning Ensemble Methodologies for the Prediction of the Failure Mode of Reinforced Concrete Beam–Column Joints

Karabini,

Karampinis,

Rousakis

et al. 2024

Information

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One of the most critical aspects in the seismic behavior or reinforced concrete (RC) structures pertains to beam–column joints. Modern seismic design codes dictate that, if failure is to occur, then this should be the ductile yielding of the beam and not brittle shear failure of the joint, which can lead to sudden collapse and loss of human lives. To this end, it is imperative to be able to predict the failure mode of RC joints for a large number of structures in a building stock. In this research effort, various ensemble machine learning algorithms were employed to develop novel, robust classification models. A dataset comprising 486 measurements from real experiments was utilized. The performance of the employed classifiers was assessed using Precision, Recall, F1-Score, and overall Accuracy indices. N-fold cross-validation was employed to enhance generalization. Moreover, the obtained models were compared to the available engineering ones currently adopted by many international organizations and researchers. The novel ensemble models introduced in this research were proven to perform much better by improving the obtained accuracy by 12–18%. The obtained metrics also presented small variability among the examined failure modes, indicating unbiased models. Overall, the results indicate that the proposed methodologies can be confidently employed for the prediction of the failure mode of RC joints.

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Seismic Response Prediction of Rigid Rocking Structures Using Explainable LightGBM Models

Karampinis,

Bantilas,

Kavvadias

et al. 2024

Mathematics

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This study emphasizes the explainability of machine learning (ML) models in predicting the seismic response of rigid rocking structures, specifically using the LightGBM algorithm. By employing SHapley Additive exPlanations (SHAP), partial dependence plots (PDP), and accumulated local effects (ALE), a comprehensive feature importance analysis has been performed. This revealed that ground motion parameters, particularly peak ground acceleration (PGA), are critical for predicting small rotations, while structural parameters like slenderness and frequency are more significant for larger rotations. Utilizing an extensive dataset generated from nonlinear time history analyses, the trained LightGBM model demonstrated high accuracy in estimating the maximum rotation angle of rigid blocks under natural ground motions. The study also examined the sensitivity of model performance to lower bound thresholds of the target variable, revealing that reduced feature sets can maintain predictive performance effectively. These findings advance ML-based modeling of seismic rocking responses, providing interpretable and accurate models that enhance our understanding of rocking structures’ dynamic behavior, which is crucial for designing resilient structures and improving seismic risk assessments. Future research will focus on incorporating additional parameters and exploring advanced ML techniques to further refine these models.

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Machine Learning Algorithms for the Prediction of the Seismic Response of Rigid Rocking Blocks

Cited by 3 publications

References 68 publications

Methods for Identifying Effective Microseismic Signals in a Strong-Noise Environment Based on the Variational Mode Decomposition and Modified Support Vector Machine Models

Methods for Identifying Effective Microseismic Signals in a Strong-Noise Environment Based on the Variational Mode Decomposition and Modified Support Vector Machine Models

Machine Learning Ensemble Methodologies for the Prediction of the Failure Mode of Reinforced Concrete Beam–Column Joints

Seismic Response Prediction of Rigid Rocking Structures Using Explainable LightGBM Models

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