Evaluation of Liquefaction Potential Using Random Forest Method and Shear Wave Velocity Results

Nejad, Amin Shoari; Güler, Erol; Özturan, Meltem

doi:10.1109/icamcs.net46018.2018.00012

Cited by 12 publications

(3 citation statements)

References 14 publications

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“…Figure 3 shows the feature importance of each training parameter. It is worth noting that a max and V s1 are the two most important parameters for predicting soil liquefaction, which is in line with the conclusion given by Nejad et al [29] and Yang et al [39]. e other three parameters also show 15% feature importance for liquefaction evaluation, which indirectly proves that it is reasonable to choose these five parameters as training parameters and the liquefaction evaluation results are also credible.…”

Section: Comparison Among Different Methodssupporting

confidence: 85%

“…Kohestani et al [22] reported the evaluation of liquefaction potential based on CPT data using RF method. Nejad et al [29] established a RF model for predicting the occurrence or nonoccurrence of liquefaction based on the shear wave velocity data collected by Kayen et al [2]. However, these liquefaction models using RF cannot be well verified by other datasets since limited dataset was utilized by separating training dataset from testing set randomly.…”

Section: Introductionmentioning

confidence: 99%

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Liquefaction Evaluation Based on Shear Wave Velocity Using Random Forest

Liu

Zhang

Yao

et al. 2021

Advances in Civil Engineering

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Liquefaction evaluation on the sands induced by earthquake is of significance for engineers in seismic design. In this study, the random forest (RF) method is introduced and adopted to evaluate the seismic liquefaction potential of soils based on the shear wave velocity. The RF model was developed using the Andrus database as a training dataset comprising 225 sets of liquefaction performance and shear wave velocity measurements. Five training parameters are selected for RF model including seismic magnitude ( M w ), peak horizontal ground surface acceleration ( a max ), stress-corrected shear wave velocity of soil ( V s 1 ), sandy-layer buried depth (ds), and a new introduced parameter, stress ratio (k). In addition, the optimal hyperparameters for the random forest model are determined based on the minimum error rate for the out-of-bag dataset (ERROOB) such as the number of classification trees, maximum depth of trees, and maximum number of features. The established random forest model was validated using the Kayen database as testing dataset and compared with the Chinese code and the Andrus methods. The results indicated that the random forest method established based on the training dataset was credible. The random forest method gave a success rate for liquefied sites and even a total success rate for all cases higher than 80%, which is completely acceptable. By contrast, the Chinese code method and the Andrus methods gave a high success rate for liquefaction but very low for nonliquefaction which led to the increase of engineering cost. The developed RF model can provide references for engineers to evaluate liquefaction potential.

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Section: Comparison Among Different Methodssupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Liquefaction Evaluation Based on Shear Wave Velocity Using Random Forest

Liu

Zhang

Yao

et al. 2021

Advances in Civil Engineering

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“…RF is considered as an ensemble algorithm with the advantages of high accuracy, resistance to overfitting, parallelized training, flexibility, and ease of use. Therefore, RF is widely used in various areas to solve classification and prediction problems [36].…”

Section: Random Forestmentioning

confidence: 99%

Prediction of Soil Compaction Parameters Using Machine Learning Models

Li,

You,

et al. 2024

Applied Sciences

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Maximum Dry Density (MDD) and Optimum Moisture Content (OMC) are two important parameters of soil filling, which affect the soil stability and bearing capacity, and thus the reliability and durability of facilities such as highways and bridges. Therefore, it is important to make reasonable predictions of OMC and MDD. Four machine learning algorithms, namely, Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), and Extreme Gradient Boosting Tree (XGBoost), are adopted in this paper to establish MDD and OMC prediction models. After training and testing, the best models of the four algorithms are compared. The results show that, as an ensemble learning algorithm, XGBoost is the best model for predicting MDD and OMC, with an R2 of 0.9234 for OMC, and an R2 of 0.9098 for MDD. Finally, the feature importance analysis concludes that the plastic limit (PL) and the liquid limit (LL) are the two features that affect OMC and MDD the most. The prediction of soil compaction parameters using machine learning models, especially ensemble learning, can significantly reduce the amount of laboratory work and improve the efficiency of optimizing design for soil resource utilization in engineering construction.

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