A New Equation to Evaluate Liquefaction Triggering Using the Response Surface Method and Parametric Sensitivity Analysis

Tang, X.; Yang, Qing; Kang, Fei

doi:10.3390/su11010112

Cited by 24 publications

(14 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, in this work, q c1Ncs , the equivalent clean sand penetration resistance, was used to decrease uncertainty. Additionally, Pirhadi et al [50] concluded that normalized cone tip penetration value (q c1N ) is an important factor, and has the highest effect on seismic soil liquefaction triggering. The resulting BBN model showed a relatively better performance for training and testing data than other models for the metrics of overall accuracy (OA), MCC, precision, recall and F-measure, and AUC of ROC.…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2019

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

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

et al. 2019

View full text Add to dashboard Cite

show abstract

“…To this end, ML techniques outperform these empirical studies in more objectively capturing the nonlinear and multidimensional relationship between the critical inputs and the triggering of soil liquefaction. Significant studies in this area include the implementations of SVM (Goh and Goh, 2007; Pal, 2006), ANN (Goh, 1994, 1996; Hanna et al, 2007; Juang and Chen, 1999; Ramakrishnan et al, 2008; Ülgen and Engin, 2007), a combination of kernel Fisher discriminant analysis (KFDA) with SVM (Hoang and Bui, 2018), a combination of ANN and RSM (Pirhadi et al, 2018), RF (Kohestani and Ardakani, 2015), stochastic gradient boosting (SGB) (Zhou et al, 2019), generalized linear model (GLM) (Zhang et al, 2013), and evolutionary polynomial regression (EPR) (Rezania et al, 2010, 2011).…”

Section: Seismic Hazard Analysismentioning

confidence: 99%

The promise of implementing machine learning in earthquake engineering: A state-of-the-art review

et al. 2020

View full text Add to dashboard Cite

Machine learning (ML) has evolved rapidly over recent years with the promise to substantially alter and enhance the role of data science in a variety of disciplines. Compared with traditional approaches, ML offers advantages to handle complex problems, provide computational efficiency, propagate and treat uncertainties, and facilitate decision making. Also, the maturing of ML has led to significant advances in not only the main-stream artificial intelligence (AI) research but also other science and engineering fields, such as material science, bioengineering, construction management, and transportation engineering. This study conducts a comprehensive review of the progress and challenges of implementing ML in the earthquake engineering domain. A hierarchical attribute matrix is adopted to categorize the existing literature based on four traits identified in the field, such as ML method, topic area, data resource, and scale of analysis. The state-of-the-art review indicates to what extent ML has been applied in four topic areas of earthquake engineering, including seismic hazard analysis, system identification and damage detection, seismic fragility assessment, and structural control for earthquake mitigation. Moreover, research challenges and the associated future research needs are discussed, which include embracing the next generation of data sharing and sensor technologies, implementing more advanced ML techniques, and developing physics-guided ML models.

show abstract

“…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].…”

Section: Introductionmentioning

confidence: 99%

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

Ahmad

Hadzima-Nyarko

et al. 2021

Symmetry

View full text Add to dashboard Cite

Rockburst is a complex phenomenon of dynamic instability in the underground excavation of rock. Owing to the complex and unclear rockburst mechanism, it is difficult to accurately predict and reasonably assess the rockburst potential. With the increasing availability of case histories from rock engineering and the advancement of data science, the data mining algorithms provide a good way to predict complex phenomena, like rockburst potential. This paper investigates the potential of J48 and random tree algorithms to predict the rockburst classification ranks using 165 cases, with four parameters, namely maximum tangential stress of surrounding rock, uniaxial compressive strength, uniaxial tensile strength, and strain energy storage index. A comparison of developed models’ performances reveals that the random tree gives more reliable predictions than J48 and other empirical models (Russenes criterion, rock brittleness coefficient criterion, and artificial neural networks). Similar comparisons with convolutional neural network resulted at par performance in modeling the rockburst hazard data.

show abstract

A New Equation to Evaluate Liquefaction Triggering Using the Response Surface Method and Parametric Sensitivity Analysis

Cited by 24 publications

References 51 publications

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

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

The promise of implementing machine learning in earthquake engineering: A state-of-the-art review

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

Contact Info

Product

Resources

About