Abstract
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.
Highlights
Liquefaction-induced hazards cause substantial infrastructure damages to buildings, bridges, and lifelines during earthquakes [1,2,3,4,5]
A Bayesian belief network (BBN) structure is developed by using data fusion methodology based on an machine learning (ML) algorithm i.e., K2 learning from cone penetration test (CPT) case histories using FullBNT-1.0.7 tool in MATLAB, following which effective information is embedded as domain knowledge (DK)
The BBN and C4.5 decision tree (DT) models were developed from the training dataset of 201 CPT case histories (144 case records of liquefaction and 57 case records of non-liquefaction) that has class imbalances and almost no sampling bias owing to the class ratio of 180:71 for 251 case histories
Summary
Liquefaction-induced hazards (for instance, settlements, sand boils, lateral spreading, and ground cracks) cause substantial infrastructure damages to buildings, bridges, and lifelines during earthquakes [1,2,3,4,5]. Bayesian belief network (BBN) and C4.5 decision tree (DT) approaches are employed to assess seismic soil liquefaction potential based on the updated and relatively large cone penetration test (CPT) database, which includes 251 case histories. Ardakani and Kohestani [16] used the C4.5 DT model on 109 CPT-based case histories; they considered seven significant factors of seismic soil liquefaction—cone tip resistance (qc ), total vertical stress (σv ), vertical effective stress (σv 0 ), mean grain size (D50 ), peak ground acceleration (amax ), cyclic stress ratio (τ/σv 0 ), and earthquake magnitude (M)—without paying attention to sampling bias in the training and testing data sets. The BBN and C4.5 DT effective approaches are used to evaluate and compare the seismic soil liquefaction potential of the updated and relatively large cone penetration test (CPT) data set, which includes 251 case history records. Sci. 2019, 9, x FOR PEER REVIEW for future work
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