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

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.

Rockburst Hazard Prediction in Underground Projects Using Two Intelligent Classification Techniques: A Comparative Study

“…The main advantage of random trees is that they are simple to build and the resulting trees are easy to understand [37]. The C4.5 algorithm has recently been used to determine the potential for seismic soil liquefaction [28,29] and landslide susceptibility [37]. The J48 random tree algorithm takes the following steps [38].…”

“…Rockburst prediction is a complex and nonlinear process that is hindered by model and parameter uncertainty, as well as limited by inadequate knowledge, lack of information characterization, and noisy data. Machine learning has been widely recognized in mining and geotechnical engineering applications for dealing with nonlinear problems and developing predictive data-mining models [25][26][27][28][29][30][31].…”

Elucidation of Seismic Soil Liquefaction Significant Factors

“…Furthermore Ahmad et al [18] concluded that cone tip resistance (q c ) has a considerable influence on liquefaction triggering. Furthermore, Ahmad et al [28] used the equivalent clean sand penetration resistance (q c1Ncs ) to decrease uncertainty and has found the strongest influence on liquefaction potential.…”

Probabilistic Evaluation of CPT-based Seismic Soil Liquefaction Potential: Towards the Integration of Interpretive Structural Modeling and Bayesian Belief Network

“…Furthermore, its application in seismic liquefaction potential on CPT-based in-situ tests data is comparatively less e.g. (Ahmad et al 2019a;Ahmad et al 2020a;Ahmad et al 2020b). The contributions of this paper are fourfold: (1) this article discusses the interdependence of different CPT-based seismic soil liquefaction variables, whereas the Bayesian Belief Network (BBN) approach uses conditional and marginal probabilities to describe the quantitative strength of their relationships; (2) the performance of the proposed model is comparatively assessed with four traditional seismic soil liquefaction modeling algorithms (logistic regression, SVM, RF, and Naïve Bayes); (3) the sensitivity analysis of predictor variables is presented owing to know the effect of input factors on the liquefaction potential; and (4) the most probable explanation (MPE) of seismic soil liquefaction with reference to engineering perspective is presented.…”