Seismic Soil Liquefaction Potential Research Articles

Probabilistic and Deterministic Assessment of Seismic Soil Liquefaction Potential in The Niger Delta Region of Nigeria Using Standard Penetration Test-Based

Probabilistic and Deterministic Assessment of Seismic Soil Liquefaction Potential in The Niger Delta Region of Nigeria Using Standard Penetration Test-Based

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 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.

Probabilistic evaluation of CPT-based seismic soil liquefaction potential: towards the integration of interpretive structural modeling and bayesian belief network.

This paper proposes a probabilistic graphical model that integrates interpretive structural modeling (ISM) and Bayesian belief network (BBN) approaches to predict cone penetration test (CPT)-based soil liquefaction potential. In this study, an ISM approach was employed to identify relationships between influence factors, whereas BBN approach was used to describe the quantitative strength of their relationships using conditional and marginal probabilities. The proposed model combines major causes, such as soil, seismic and site conditions, of seismic soil liquefaction at once. To demonstrate the application of the propose framework, the paper elaborates on each phase of the BBN framework, which is then validated with historical empirical data. In context of the rate of successful prediction of liquefaction and non-liquefaction events, the proposed probabilistic graphical model is proven to be more effective, compared to logistic regression, support vector machine, random forest and naive Bayes methods. This research also interprets sensitivity analysis and the most probable explanation of seismic soil liquefaction appertaining to engineering perspective.

A step forward towards a comprehensive framework for assessing liquefaction land damage vulnerability: Exploration from historical data

The unprecedented liquefaction-related land damage during earthquakes has highlighted the need to develop a model that better interprets the liquefaction land damage vulnerability (LLDV) when determining whether liquefaction is likely to cause damage at the ground’s surface. This paper presents the development of a novel comprehensive framework based on select case history records of cone penetration tests using a Bayesian belief network (BBN) methodology to assess seismic soil liquefaction and liquefaction land damage potentials in one model. The BBN-based LLDV model is developed by integrating multi-related factors of seismic soil liquefaction and its induced hazards using a machine learning (ML) algorithm-K2 and domain knowledge (DK) data fusion methodology. Compared with the C4.5 decision tree-J48 model, naive Bayesian (NB) classifier, and BBN-K2 ML prediction methods in terms of overall accuracy and the Cohen’s kappa coefficient, the proposed BBN K2 and DK model has a better performance and provides a substitutive novel LLDV framework for characterizing the vulnerability of land to liquefaction-induced damage. The proposed model not only predicts quantitatively the seismic soil liquefaction potential and its ground damage potential probability but can also identify the main reasons and fault-finding state combinations, and the results are likely to assist in decisions on seismic risk mitigation measures for sustainable development. The proposed model is simple to perform in practice and provides a step toward a more sophisticated liquefaction risk assessment modeling. This study also interprets the BBN model sensitivity analysis and most probable explanation of seismic soil liquefied sites based on an engineering point of view.

A hybrid approach for evaluating CPT-based seismic soil liquefaction potential using Bayesian belief networks

Discernment of seismic soil liquefaction is a complex and non-linear procedure that is affected by diversified factors of uncertainties and complexity. The Bayesian belief network (BBN) is an effective tool to present a suitable framework to handle insights into such uncertainties and cause–effect relationships. The intention of this study is to use a hybrid approach methodology for the development of BBN model based on cone penetration test (CPT) case history records to evaluate seismic soil liquefaction potential. In this hybrid approach, naive model is developed initially only by an interpretive structural modeling (ISM) technique using domain knowledge (DK). Subsequently, some useful information about the naive model are embedded as DK in the K2 algorithm to develop a BBN-K2 and DK model. The results of the BBN models are compared and validated with the available artificial neural network (ANN) and C4.5 decision tree (DT) models and found that the BBN model developed by hybrid approach showed compatible and promising results for liquefaction potential assessment. The BBN model developed by hybrid approach provides a viable tool for geotechnical engineers to assess sites conditions susceptible to seismic soil liquefaction. This study also presents sensitivity analysis of the BBN model based on hybrid approach and the most probable explanation of liquefied sites, owing to know the most likely scenario of the liquefaction phenomenon.

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) 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.

Fuzzy logic based assessment of seismic soil liquefaction potential and its application to foundations of bridge piers

Fuzzy logic based assessment of seismic soil liquefaction potential and its application to foundations of bridge piers

Interpretive Structural Modeling and MICMAC Analysis for Identifying and Benchmarking Significant Factors of Seismic Soil Liquefaction

Seismic soil liquefaction is considered as one of the most complex geotechnical earthquake engineering problems owing to the uncertainty and complexity involved in soil parameters, seismic parameters, and site condition factors. Each one of these parameters contains a variety of factors that trigger liquefaction and have varying degrees of importance. However, estimating accurate and reliable liquefaction-induced hazards requires identification and benchmarking of the most influential factors that control soil liquefaction. Seismic soil liquefaction factors were identified by Systematic Literature Review (SLR) approach and analyzed through Interpretive Structural Modeling (ISM) and the Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) methodologies. The ISM model presented the relationships between fifteen seismic soil liquefaction factors and their benchmarking position from higher to lower-level significant factors in hierarchy. MICMAC is used to examine the strength of the relationship between seismic soil liquefaction significant factors based on their driving and dependence power. This research characterizes the identification and benchmarking of the seismic soil liquefaction factors and their relationships. The results show that the factors—duration of earthquake, peak ground acceleration, drainage condition, and standard penetration test (SPT) blow counts—influence seismic soil liquefaction directly and soil type is the governing factor that forms the base of the ISM hierarchy and consequently triggers seismic soil liquefaction. The results provide a more accurate way of selecting significant factors for establishment of seismic soil liquefaction potential and liquefaction-induced hazards risk assessment models.

Energy-Based Estimation of Soil Liquefaction Potential Using GMDH Algorithm

Reliable determination of seismic liquefaction potential of soil is an important obligation in earthquake engineering. In this study, neuro-fuzzy group method of data handling (NF-GMDH) was employed for prediction of strain energy required to induce liquefaction. The NF-GMDH-based model was developed using particle swarm optimization. A wide-ranging database of soil element tests was used to develop an advanced model, capable of predicting soil liquefaction resistance accurately. Input variables of the model were chosen based on the previous studies on the liquefaction potential assessment. Results of geotechnical centrifuge tests were also involved during the training process for adequate generalization of the proposed model for future predictions. A parametric analysis was then performed to evaluate sensitivity of the proposed model to variations of the influencing parameters. A comparison between performance of the developed model and previously recommended relationships was done. The results clearly demonstrate that the proposed model, which was derived based on laboratory results, can be successfully utilized for strain energy-based estimation of liquefaction potential.

Evaluation of liquefaction potential based on CPT data using random forest

The prediction of liquefaction potential of soil due to an earthquake is an essential task in civil engineering. In this paper, random forest (RF) method is introduced and investigated for the prediction of seismic liquefaction potential of soil based on the cone penetration test data. RF has been proposed on the basis of classification and regression trees with “ensemble learning” strategy. The RF models were developed and validated on a relatively large dataset comprising 226 field records of liquefaction performance and cone penetration test measurements. The database contains the information about depth of potentially liquefiable soil layer (D), cone tip resistance ( $$q_{\text{c}}$$ ), sleeve friction ratio ( $$R_{\text{f}}$$ ), effective vertical stress ( $$\sigma_{0}^{\prime }$$ ), total vertical stress ( $$\sigma_{0}$$ ), maximum horizontal ground surface acceleration ( $$\alpha_{\hbox{max} }$$ ) and earthquake magnitude ( $$M_{\text{w}}$$ ). Two RF models (Model I and Model II) are developed for predicting the occurrence and non-occurrence of liquefaction on the basis of combination of above input parameters. The results of RF models have been compared with the available artificial neural network (ANN) and support vector machine (SVM) models. It is shown that the proposed RF models provide more accurate results than the ANN and SVM models proposed in the literature. The developed RF provides a viable tool for civil engineers to determine the liquefaction potential of soil.

Determination of seismic liquefaction potential of soil based on strain energy concept

In the present study, minimax probability machine regression (MPMR) and extreme learning machine (ELM) have been adopted for prediction of seismic liquefaction of soil based on strain energy. Initial effective mean confining pressure ( $$ \sigma_{\text{mean}}^{\prime} $$ ), initial relative density after consolidation (D r), percentage of fines content (FC), coefficient of uniformity (C u), and mean grain size (D 50) have been taken as inputs of MPMR and ELM models. MPMR and ELM have been used as regression techniques. The performances of MPMR and ELM have been compared with the artificial neural network. A sensitivity analysis has been carried out to determine the effect of each input. The experimental results demonstrate that proposed methods are robust models for determination seismic liquefaction potential of soil based on strain energy.

Model uncertainty of SPT-based method for evaluation of seismic soil liquefaction potential using multi-gene genetic programming

In this paper, the model uncertainty of the developed standard penetration test (SPT)-based model for evaluation of liquefaction potential of soil is estimated within the framework of the first-order reliability method (FORM). First, an empirical model to determine the cyclic resistance ratio (CRR) of the soil is developed, based on the post-liquefaction SPT data using an evolutionary artificial intelligence technique, multi-gene genetic programming (MGGP). This developed resistance model along with an existing cyclic stress ratio (CSR) model forms a limit state function for reliability-based approach for liquefaction triggering analysis. The uncertainty of the developed limit state model is represented by a lognormal random variable, in terms of its mean and the coefficient of variation, estimated through an extensive reliability analysis following a trial and error approach using Bayesian mapping functions calibrated with a high quality post-liquefaction case history database. A deterministic model with a mapping function relating the probability of liquefaction (PL) and the factor of safety against liquefaction (Fs) is also developed for use in absence of parameter uncertainties. Two examples are presented to compare the present MGGP-based reliability method with the available regression-based reliability method.

Evaluation of liquefaction potential based on CPT results using C4.5 decision tree

The prediction of liquefaction potential of soil due to an earthquake is an essential task in Civil Engineering. The decision tree is a tree structure consisting of internal and terminal nodes which process the data to ultimately yield a classification. C4.5 is a known algorithm widely used to design decision trees. In this algorithm, a pruning process is carried out to solve the problem of the over-fitting. This article examines the capability of C4.5 decision tree for the prediction of seismic liquefaction potential of soil based on the Cone Penetration Test (CPT) data. The database contains the information about cone resistance (q_c), total vertical stress (σ_0), effective vertical stress (σ_0^'), mean grain size (D_50), normalized peak horizontal acceleration at ground surface (a_max), cyclic stress ratio (τ/σ_0^') and earthquake magnitude (M_w). The overall classification success rate for the entire data set is 98%. The results of C4.5 decision tree have been compared with the available artificial neural network (ANN) and relevance vector machine (RVM) models. The developed C4.5 decision tree provides a viable tool for civil engineers to determine the liquefaction potential of soil.

Modeling of SPT Seismic Liquefaction Data Using Minimax Probability Machine

The determination of seismic liquefaction potential of soil is an important task in geotechnical engineering. This article uses Minimax Probability Machine (MPM) for determination of seismic liquefaction potential of soil based on Standard Penetration Test value (N). MPM is developed based on the use of hyperplanes. It is a discriminant classifier. This study uses MPM as a classification tool. MPM uses the database collected from Chi–Chi earthquake. Two models (MODEL I and MODEL II) have been developed. MODEL I uses Cyclic Stress Ratio and N as input variables. Peck Ground Acceleration and N have been adopted as inputs for MODEL II. The performance of MODEL I and MODEL II are 97.67 and 96.51 % respectively. The performance of MODEL II is 94.11 % for the global data. The developed MPM shows good generalization capability. The results show that the developed MPM has ability for determination of seismic liquefaction potential of soil.

A unified classification model for modeling of seismic liquefaction potential of soil based on CPT

The evaluation of liquefaction potential of soil due to an earthquake is an important step in geosciences. This article examines the capability of Minimax Probability Machine (MPM) for the prediction of seismic liquefaction potential of soil based on the Cone Penetration Test (CPT) data. The dataset has been taken from Chi–Chi earthquake. MPM is developed based on the use of hyperplanes. It has been adopted as a classification tool. This article uses two models (MODEL I and MODEL II). MODEL I employs Cone Resistance (qc) and Cyclic Stress Ratio (CSR) as input variables. qc and Peak Ground Acceleration (PGA) have been taken as inputs for MODEL II. The developed MPM gives 100% accuracy. The results show that the developed MPM can predict liquefaction potential of soil based on qc and PGA.

CPT-based probabilistic evaluation of seismic soil liquefaction potential using multi-gene genetic programming

In this paper, liquefaction potential of soil is evaluated within a probabilistic framework based on the post-liquefaction cone penetration test (CPT) data using an evolutionary artificial intelligence technique, multi-gene genetic programming (MGGP). Based on the developed limit state function using MGGP, a relationship is given between probability of liquefaction (PL) and factor of safety against liquefaction using Bayesian theory. This Bayesian mapping function is further used to develop a PL-based design chart for evaluation of liquefaction potential of soil. Using an independent database of 200 cases, the efficacy of the present MGGP-based probabilistic method is compared with that of the available probabilistic methods based on artificial neural network (ANN) and statistical methods. The proposed method is found to be more efficient in terms of rate of successful prediction of liquefaction and non-liquefaction cases, in three different ranges of PL values compared to ANN and statistical methods.

Determination of Strain Energy for Triggering Liquefaction Based on Gaussian Process Regression

The determination of seismic liquefaction potential of soil is an imperative task in earthquake engineering. This article adopts Gaussian Process Regression (GPR) for determination of amount of strain energy required to induce liquefaction. Effective mean confining pressure (σ' mean), initial relative density after consolidation (Dr), percentage of fines content (FC), coefficient of uniformity (Cu), and mean grain size (D50) are considered as input of the GPR model. The developed GPR gives the variance of predicted output. The results of GPR have been compared with the Artificial Neural Network (ANN). The results of this article show the suitability of the proposed approach for determination of stain energy for triggering liquefaction.

Shear-Wave Velocity–Based Probabilistic and Deterministic Assessment of Seismic Soil Liquefaction Potential

AbstractShear-wave velocity (Vs) offers a means to determine the seismic resistance of soil to liquefaction by a fundamental soil property. This paper presents the results of an 11-year international project to gather new Vs site data and develop probabilistic correlations for seismic soil liquefaction occurrence. Toward that objective, shear-wave velocity test sites were identified, and measurements made for 301 new liquefaction field case histories in China, Japan, Taiwan, Greece, and the United States over a decade. The majority of these new case histories reoccupy those previously investigated by penetration testing. These new data are combined with previously published case histories to build a global catalog of 422 case histories of Vs liquefaction performance. Bayesian regression and structural reliability methods facilitate a probabilistic treatment of the Vs catalog for performance-based engineering applications. Where possible, uncertainties of the variables comprising both the seismic demand an...

Support vector machine for evaluating seismic-liquefaction potential using shear wave velocity

The use of the shear wave velocity data as a field index for evaluating the liquefaction potential of sands is receiving increased attention because both shear wave velocity and liquefaction resistance are similarly influenced by many of the same factors such as void ratio , state of stress, stress history and geologic age. In this paper, the potential of support vector machine (SVM) based classification approach has been used to assess the liquefaction potential from actual shear wave velocity data. In this approach, an approximate implementation of a structural risk minimization (SRM) induction principle is done, which aims at minimizing a bound on the generalization error of a model rather than minimizing only the mean square error over the data set. Here SVM has been used as a classification tool to predict liquefaction potential of a soil based on shear wave velocity. The dataset consists the information of soil characteristics such as effective vertical stress (σ′ v0 ), soil type, shear wave velocity (V s ) and earthquake parameters such as peak horizontal acceleration (a max ) and earthquake magnitude (M). Out of the available 186 datasets, 130 are considered for training and remaining 56 are used for testing the model. The study indicated that SVM can successfully model the complex relationship between seismic parameters, soil parameters and the liquefaction potential. In the model based on soil characteristics, the input parameters used are σ′ v0 , soil type, V s , a max and M. In the other model based on shear wave velocity alone uses V s , a max and M as input parameters. In this paper, it has been demonstrated that Vs alone can be used to predict the liquefaction potential of a soil using a support vector machine model. ► The developed SVM successfully models the complex relationship between the seismic and soil parameters. ► User can use the developed equations for prediction of seismic liquefaction potential of soil. ► This study shows that the developed SVM model can be used as a practical tool for prediction of seismic liquefaction potential of soil based on V s data.

Probabilistic evaluation of seismic soil liquefaction potential based on SPT data

The performance-based liquefaction potential analysis was carried out in the present study to estimate the liquefaction return period for Bangalore, India, through a probabilistic approach. In this approach, the entire range of peak ground acceleration (PGA) and earthquake magnitudes was used in the evaluation of liquefaction return period. The seismic hazard analysis for the study area was done using probabilistic approach to evaluate the peak horizontal acceleration at bed rock level. Based on the results of the multichannel analysis of surface wave, it was found that the study area belonged to site class D. The PGA values for the study area were evaluated for site class D by considering the local site effects. The soil resistance for the study area was characterized using the standard penetration test (SPT) values obtained from 450 boreholes. These SPT data along with the PGA values obtained from the probabilistic seismic hazard analysis were used to evaluate the liquefaction return period for the study area. The contour plot showing the spatial variation of factor of safety against liquefaction and the corrected SPT values required for preventing liquefaction for a return period of 475 years at depths of 3 and 6 m are presented in this paper. The entire process of liquefaction potential evaluation, starting from collection of earthquake data, identifying the seismic sources, evaluation of seismic hazard and the assessment of liquefaction return period were carried out, and the entire analysis was done based on the probabilistic approach.