Why “AI” models for predicting soil liquefaction have been ignored, plus some that shouldn't be
BW Maurer, MD Sanger - Earthquake Spectra, 2023 - journals.sagepub.com
Soil liquefaction remains an important and interesting problem that has attracted the
development of enumerable prediction models. Increasingly, these models are utilizing …
development of enumerable prediction models. Increasingly, these models are utilizing …
[HTML][HTML] Prediction of compaction parameters for fine-grained soil: Critical comparison of the deep learning and standalone models
A comparison between deep learning and standalone models in predicting the compaction
parameters of soil is presented in this research. One hundred and ninety and fifty-three soil …
parameters of soil is presented in this research. One hundred and ninety and fifty-three soil …
Comparison of tree-based machine learning algorithms for predicting liquefaction potential using canonical correlation forest, rotation forest, and random forest based …
This research investigates and compares the performance of three tree-based Machine
Learning (ML) methods, Canonical Correlation Forest (CCF), Rotation Forest (RotFor), and …
Learning (ML) methods, Canonical Correlation Forest (CCF), Rotation Forest (RotFor), and …
Liquefaction prediction with robust machine learning algorithms (SVM, RF, and XGBoost) supported by genetic algorithm-based feature selection and parameter …
Liquefaction prediction is an important issue in the seismic design of engineering structures,
and research on this topic has been continuing in current literature using different methods …
and research on this topic has been continuing in current literature using different methods …
The adoption of ELM to the prediction of soil liquefaction based on CPT
Y Zhang, J Qiu, Y Zhang, Y Wei - Natural Hazards, 2021 - Springer
Establishing a soil liquefaction prediction model with high accuracy is a critical way to
evaluate the quality of in situ and prevent the loss caused by seismic. In this paper …
evaluate the quality of in situ and prevent the loss caused by seismic. In this paper …
Greedy-AutoML: A novel greedy-based stacking ensemble learning framework for assessing soil liquefaction potential
Automated machine learning (AutoML) is a generic term for a specific approach to machine
learning (ML) area that tries to automate the end-to-end process of employing repetitive ML …
learning (ML) area that tries to automate the end-to-end process of employing repetitive ML …
Prediction of compaction parameters of compacted soil using LSSVM, LSTM, LSBoostRF, and ANN
The present research introduces a robust approach for predicting the maximum dry density
(MDD) and optimum moisture content (OMC) of compacted soil by comparing models based …
(MDD) and optimum moisture content (OMC) of compacted soil by comparing models based …
The adoption of a support vector machine optimized by GWO to the prediction of soil liquefaction
Y Zhang, J Qiu, Y Zhang, Y Xie - Environmental Earth Sciences, 2021 - Springer
Establishing a prediction model of soil liquefaction is an effective way to evaluate the site's
quality and prevent the relevant loss caused by the earthquake. Considering the complexity …
quality and prevent the relevant loss caused by the earthquake. Considering the complexity …
Neural transfer learning for soil liquefaction tests
Soil liquefaction is one of the most disastrous sides of earthquakes which can cause severe
damage to structures, infrastructures, and individuals' lives. Therefore, establishing new and …
damage to structures, infrastructures, and individuals' lives. Therefore, establishing new and …
An enhanced hybrid approach for spatial distribution of seismic liquefaction characteristics by integrating physics-based simulation and machine learning
Z Ba, S Han, M Wu, Y Lu, J Liang - Soil Dynamics and Earthquake …, 2024 - Elsevier
This study aims to propose an enhanced hybrid approach that combines physics-based
simulation and machine learning to investigate the spatial distribution of seismic liquefaction …
simulation and machine learning to investigate the spatial distribution of seismic liquefaction …