Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential

This study investigates the performance of four machine learning (ML) algorithms to evaluate the earthquake-induced liquefaction potential of soil based on the cone penetration test field case history records using the Bayesian belief network (BBN) learning software Netica. The BBN structures that w...

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Bibliographic Details
Published in:Frontiers of Structural and Civil Engineering 2021-04, Vol.15 (2), p.490-505
Main Authors: AHMAD, Mahmood, TANG, Xiao-Wei, QIU, Jiang-Nan, AHMAD, Feezan, GU, Wen-Jing
Format: Article
Language:English
Subjects:
Algorithms
Bayesian analysis
Bayesian belief network
Belief networks
Cities
Civil Engineering
cone penetration test
Cone penetration tests
Countries
Earthquakes
Engineering
Grain size
Learning algorithms
Liquefaction
Machine learning
Mathematical models
parameter learning
Performance indices
Performance prediction
Regions
Research Article
Seismic activity
Seismic response
seismic soil liquefaction
Sensitivity analysis
Soil investigations
Soils
structural learning
Tabu search
Training evaluation
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Summary:This study investigates the performance of four machine learning (ML) algorithms to evaluate the earthquake-induced liquefaction potential of soil based on the cone penetration test field case history records using the Bayesian belief network (BBN) learning software Netica. The BBN structures that were developed by ML algorithms-K2, hill climbing (HC), tree augmented naive (TAN) Bayes, and Tabu search were adopted to perform parameter learning in Netica, thereby fixing the BBN models. The performance measure indexes, namely, overall accuracy ( OA), precision, recall, F-measure, and area under the receiver operating characteristic curve, were used to evaluate the training and testing BBN models' performance and highlight the capability of the K2 and TAN Bayes models over the Tabu search and HC models. The sensitivity analysis results showed that the cone tip resistance and vertical effective stress are the most sensitive factors, whereas the mean grain size is the least sensitive factor in the prediction of seismic soil liquefaction potential. The results of this study can provide theoretical support for researchers in selecting appropriate ML algorithms and improving the predictive performance of seismic soil liquefaction potential models.
ISSN:2095-2430
2095-2449
DOI:10.1007/s11709-020-0669-5