Evaluating Seismic Soil Liquefaction Potential Using Bayesian Belief Network and C4.5 Decision Tree Approaches

Liquefaction is considered a damaging phenomenon of earthquakes and a major cause of concern in civil engineering. Therefore, its predictory assessment is an essential task for geotechnical experts. This paper investigates the performance of Bayesian belief network (BBN) and C4.5 decision tree (DT)...

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Bibliographic Details
Published in:Applied sciences 2019-10, Vol.9 (20), p.4226
Main Authors: Ahmad, Mahmood, Tang, Xiao-Wei, Qiu, Jiang-Nan, Ahmad, Feezan
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Bayesian analysis
bayesian belief network
Belief networks
c4.5 decision tree
Case histories
Civil engineering
cone penetration test
Cone penetration tests
Construction
Data integration
Datasets
Decision trees
domain knowledge
Earthquake damage
Earthquakes
Learning algorithms
Liquefaction
Machine learning
Mathematical models
Penetration resistance
Probability
Seismic activity
Seismic engineering
Sensitivity analysis
soil liquefaction
Soils
structural learning
Variables
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Summary:Liquefaction is considered a damaging phenomenon of earthquakes and a major cause of concern in civil engineering. Therefore, its predictory assessment is an essential task for geotechnical experts. This paper investigates the performance of Bayesian belief network (BBN) and C4.5 decision tree (DT) models to evaluate seismic soil liquefaction potential based on the updated and relatively large cone penetration test (CPT) dataset (which includes 251 case histories), comparing them to a simplified procedure and an evolutionary-based approach. The BBN model was developed using the K2 machine learning algorithm and domain knowledge (DK) with data fusion methodology, while the DT model was created using a C4.5 algorithm. This study shows that the BBN model is preferred over the others for evaluation of seismic soil liquefaction potential. Owing to its overall performance, simplicity in practice, data-driven characteristics, and ability to map interactions between variables, the use of a BBN model in assessing seismic soil liquefaction is quite promising. The results of a sensitivity analysis show that ‘equivalent clean sand penetration resistance’ is the most significant factor affecting liquefaction potential. This study also interprets the probabilistic reasoning of the robust BBN model and most probable explanation (MPE) of seismic soil liquefied sites, based on an engineering point of view.
ISSN:2076-3417
2076-3417
DOI:10.3390/app9204226