4.6 Article

Employing a genetic algorithm and grey wolf optimizer for optimizing RF models to evaluate soil liquefaction potential

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Interdisciplinary Applications

Developing a hybrid model of information entropy and unascertained measurement theory for evaluation of the excavatability in rock mass

Jian Zhou et al.

Summary: This study proposes a hybrid model based on unascertained measurement and information entropy to assess the excavatability of rock mass in a specific area. The model utilizes data from five different sources and calculates single-index measurement values, while determining the weights of evaluation indices. The results indicate that the model can be introduced as an accurate and applicable tool in the field of excavation.

ENGINEERING WITH COMPUTERS (2022)

Article Engineering, Geological

Predicting tunnel squeezing using support vector machine optimized by whale optimization algorithm

Jian Zhou et al.

Summary: In this study, a model named WOA-SVM was proposed to accurately predict tunnel squeezing by optimizing the support vector machine (SVM) model. The optimized WOA-SVM model showed the highest accuracy among all proposed models, and the percentage strain was identified as the most influential parameter for the model.

ACTA GEOTECHNICA (2022)

Article Computer Science, Interdisciplinary Applications

Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibration

Yingui Qiu et al.

Summary: The accurate prediction of ground vibration caused by blasting is crucial in the mining industry. The use of advanced supervised machine learning with metaheuristic algorithms can significantly enhance the predictive reliability and accuracy, benefiting mine planners and engineers.

ENGINEERING WITH COMPUTERS (2022)

Article Computer Science, Interdisciplinary Applications

Integrating the LSSVM and RBFNN models with three optimization algorithms to predict the soil liquefaction potential

Mingxiang Cai et al.

Summary: This study developed and evaluated models to predict soil liquefaction potential using least squares support vector machine and radial basis function neural network with optimization algorithms. The proposed models outperformed previous ones, with cyclic stress ratio identified as the most important parameter impacting soil liquefaction.

ENGINEERING WITH COMPUTERS (2022)

Article Computer Science, Artificial Intelligence

An optimized system of GMDH-ANFIS predictive model by ICA for estimating pile bearing capacity

Danial Jahed Armaghani et al.

Summary: A new technique, ANFIS-GMDH-ICA, is proposed for predicting pile bearing capacity, with higher accuracy compared to traditional models. This technology can be utilized in the field of foundation engineering and design.

ARTIFICIAL INTELLIGENCE REVIEW (2022)

Article Computer Science, Interdisciplinary Applications

Performance evaluation of hybrid GA-SVM and GWO-SVM models to predict earthquake-induced liquefaction potential of soil: a multi-dataset investigation

Jian Zhou et al.

Summary: This study proposes two support vector machine models for predicting soil liquefaction potential, optimized by genetic algorithm and grey wolf optimizer. The results show that the GWO-SVM model achieved the highest classification accuracy on three data sets and outperformed the GA-SVM model.

ENGINEERING WITH COMPUTERS (2022)

Article Computer Science, Interdisciplinary Applications

Deep neural network and whale optimization algorithm to assess flyrock induced by blasting

Hongquan Guo et al.

Summary: This study developed a deep neural network (DNN) model to predict flyrock induced by blasting, which showed a significant increase in prediction accuracy compared to an artificial neural network (ANN) model. The DNN model, optimized using the whale optimization algorithm (WOA), successfully minimized flyrock resulting from blasting and provided a suitable pattern for blasting operations in mines.

ENGINEERING WITH COMPUTERS (2021)

Article Computer Science, Interdisciplinary Applications

Performance evaluation of hybrid FFA-ANFIS and GA-ANFIS models to predict particle size distribution of a muck-pile after blasting

Jian Zhou et al.

Summary: The study optimized the parameters of ANFIS using the firefly algorithm and genetic algorithm, comparing the accuracy of particle size prediction models and finding the ANFIS-GA model performed the best.

ENGINEERING WITH COMPUTERS (2021)

Article Computer Science, Interdisciplinary Applications

A new hybrid simulated annealing-based genetic programming technique to predict the ultimate bearing capacity of piles

Weixun Yong et al.

Summary: This research develops three soft-computing techniques for predicting the ultimate-bearing capacity of a pile, with the SA-GP model performing the best in terms of correlation coefficient and mean square error. The pile's Q(ult) is most affected by the pile cross-sectional area and pile set.

ENGINEERING WITH COMPUTERS (2021)

Article Environmental Sciences

Development of a new methodology for estimating the amount of PPV in surface mines based on prediction and probabilistic models (GEP-MC)

Jian Zhou et al.

Summary: The study analyzed the environmental impacts of ground vibration and associated damage resulting from blasting, utilizing a combination of predictive and probabilistic models. By applying a new intelligent model GEP, a predictive model with mathematical relations was developed and the Monte Carlo simulation technique was used to manage potential risks more effectively.

INTERNATIONAL JOURNAL OF MINING RECLAMATION AND ENVIRONMENT (2021)

Article Computer Science, Interdisciplinary Applications

Developing a hybrid model of salp swarm algorithm-based support vector machine to predict the strength of fiber-reinforced cemented paste backfill

Enming Li et al.

Summary: This study tested the impact of different mixture ratios on backfilling strength in Fankou lead-zinc mine, and found that polypropylene fibers can improve CPB strength, while straw fibers may decrease it in some cases. Using SVM technique with three heuristic algorithms, the SSA-SVM method was recommended for modeling the complexity of interactions in fiber-reinforced CPB and predicting its strength.

ENGINEERING WITH COMPUTERS (2021)

Article Computer Science, Artificial Intelligence

Modeling of rock fragmentation by firefly optimization algorithm and boosted generalized additive model

Qiancheng Fang et al.

Summary: This paper introduces a new soft computing model FFA-BGAM for accurately modeling rock fragmentation using a firefly algorithm to optimize the BGAM model. Compared to other soft computing models, FFA-BGAM shows higher accuracy in predicting rock size distribution.

NEURAL COMPUTING & APPLICATIONS (2021)

Article Computer Science, Artificial Intelligence

An ensemble method to improve prediction of earthquake-induced soil liquefaction: a multi-dataset study

Junfei Zhang et al.

Summary: A novel hybrid classifier ensemble method was proposed to improve the generalizability of earthquake-induced liquefaction potential evaluation models by combining the predictions of seven base classifiers using weighted voting. The ensemble method outperformed the base classifiers in terms of various performance metrics on three datasets, and also identified the importance of influencing variables for future data collection. This robust method can be extended to solve classification problems in civil engineering.

NEURAL COMPUTING & APPLICATIONS (2021)

Article Automation & Control Systems

Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate

Jian Zhou et al.

Summary: This research aims to optimize the hyper-parameters of the support vector machine technique through the use of three optimization algorithms, namely gray wolf optimization, whale optimization algorithm, and moth flame optimization, for predicting the advance rate of a tunnel boring machine in hard rock conditions. The results indicate that the moth flame optimization algorithm can better capture the hyperparameters of the SVM model in predicting TBM AR.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2021)

Article Construction & Building Technology

Data-Driven Approach to Predict the Plastic Hinge Length of Reinforced Concrete Columns and Its Application

De-Cheng Feng et al.

Summary: The plastic hinge length is a key parameter in the inelastic response of reinforced concrete columns to combined axial and flexural loading. A novel data-driven model based on the AdaBoost algorithm is proposed to predict the plastic hinge length, showing considerably higher prediction accuracy compared to existing methods. Numerical experiments demonstrate that using the predicted plastic hinge length in force-based beam-column models closely resembles laboratory experiments.

JOURNAL OF STRUCTURAL ENGINEERING (2021)

Article Geosciences, Multidisciplinary

The adoption of ELM to the prediction of soil liquefaction based on CPT

Yong-gang Zhang et al.

Summary: This study established a soil liquefaction prediction model using extreme learning machine (ELM) trained with cone penetration test (CPT) data, which improved the prediction accuracy. Experimental results showed that using the sin function achieved higher prediction accuracy, demonstrating the applicability and feasibility of the ELM model.

NATURAL HAZARDS (2021)

Article Engineering, Geological

A novel PSO-KELM based soil liquefaction potential evaluation system using CPT and Vs measurements

Zening Zhao et al.

Summary: This study proposes a novel soil liquefaction potential evaluation system combining CPT and Vs measurements, using the machine learning model PSO-KELM to assess soil liquefaction potential, and develops a new probabilistic model to improve prediction accuracy.

SOIL DYNAMICS AND EARTHQUAKE ENGINEERING (2021)

Article Construction & Building Technology

Prediction of TBM performance in fresh through weathered granite using empirical and statistical approaches

Danial Jahed Armaghani et al.

Summary: This study successfully predicted the penetration rate and advance rate of tunnel boring machines in different weathering zones through the development of new equations, demonstrating the accuracy of these new models.

TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY (2021)

Article Environmental Sciences

The adoption of a support vector machine optimized by GWO to the prediction of soil liquefaction

Yan Zhang et al.

Summary: Establishing a prediction model of soil liquefaction is crucial for evaluating site quality and preventing earthquake-related losses. This study utilized SPT data and the GWO algorithm to improve the accuracy of the SVM model. By training the model with the training set and updating parameters with the test set, the GWO algorithm was able to enhance accuracy and optimize performance, ultimately showing the advantage of combining SPT and shear wave data for improved prediction accuracy.

ENVIRONMENTAL EARTH SCIENCES (2021)

Article Engineering, Geological

Prediction of blasting mean fragment size using support vector regression combined with five optimization algorithms

Enming Li et al.

Summary: The main purpose of blasting operations is to produce desired and optimum mean size rock fragments to improve production efficiency and reduce costs. AI-based models are popular for predicting blasting fragmentation, with the Grey Wolf Optimization Support Vector Regression model showing the best comprehensive performance.

JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING (2021)

Article Geosciences, Multidisciplinary

Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms

Chengyu Xie et al.

Summary: Four intelligent models were proposed in this study to predict the size of rock distribution in mining blasting, with the FFA-GBM model providing the highest accuracy. The combination of nature-inspired and machine learning algorithms is effective in optimizing blasting parameters and improving blasting efficiency in open mines.

GEOSCIENCE FRONTIERS (2021)

Article Geosciences, Multidisciplinary

Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques

Jian Zhou et al.

Summary: This study aimed to develop hybrid models to predict TBM performance, with PSO-XGB technique identified as the best predictive model. Sensitivity analysis revealed that UCS, BTS, and TFC have the greatest impact on TBM performance.

GEOSCIENCE FRONTIERS (2021)

Article Engineering, Civil

Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential

Mahmood Ahmad et al.

Summary: This study evaluated the seismic soil liquefaction potential using four machine learning algorithms, showing that the K2 and TAN Bayes models outperformed Tabu search and HC models. Sensitivity analysis indicated that cone tip resistance and vertical effective stress are the most sensitive factors, while mean grain size is the least sensitive.

FRONTIERS OF STRUCTURAL AND CIVIL ENGINEERING (2021)

Article Engineering, Geological

Developing a hybrid model of Jaya algorithm-based extreme gradient boosting machine to estimate blast-induced ground vibrations

Jian Zhou et al.

Summary: Blasting is still considered an important alternative for conventional excavations, but the ground vibration it generates can be harmful to nearby structures and should be prevented. A novel Jaya-XGBoost model was developed to predict blast-induced peak particle velocity (PPV) with high reliability using 150 sets of data and the Jaya algorithm for optimization. This model outperformed other machine learning models and traditional empirical models in predicting ground vibration.

INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES (2021)

Article Engineering, Geological

Intelligent modeling of blast-induced rock movement prediction using dimensional analysis and optimized artificial neural network technique

Zhi Yu et al.

Summary: A novel intelligent prediction model was proposed based on dimensional analysis and optimized artificial neural network technique, using monitoring test data from mines in the USA and Namibia to study ore loss and dilution during bench blasting. Results showed that the hybrid ANN-based model had better prediction performance and could serve as a reference for solving other engineering problems.

INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES (2021)

Article Engineering, Civil

Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization

Jian Zhou et al.

Summary: In this study, a hybrid model of XGBoost with BO was used to improve the accuracy of predicting TBM AR under hard rock conditions. By collecting data from an actual tunnel project in Malaysia, the proposed BO-XGBoost model demonstrated high accuracy in predicting TBM AR. The study also showed that machine parameters have the greatest impact on TBM AR compared to rock mass and material properties.

UNDERGROUND SPACE (2021)

Article Engineering, Civil

Evaluation of liquefaction-induced lateral displacement using Bayesian belief networks

Mahmood Ahmad et al.

Summary: A novel probabilistic framework based on Bayesian belief network was proposed for evaluating liquefaction-induced lateral displacement, with two models predicting lateral displacements for different ground conditions and compared with multiple linear regression and genetic programming models. The results showed that the proposed models have reasonable precision in learning complex relationships between lateral displacement and its influencing factors.

FRONTIERS OF STRUCTURAL AND CIVIL ENGINEERING (2021)

Article Construction & Building Technology

Predicting the compressive strength of normal and High-Performance Concretes using ANN and ANFIS hybridized with Grey Wolf Optimizer

Emadaldin Mohammadi Golafshani et al.

CONSTRUCTION AND BUILDING MATERIALS (2020)

Article Engineering, Environmental

Prediction of rockburst risk in underground projects developing a neuro-bee intelligent system

Jian Zhou et al.

BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT (2020)

Article Metallurgy & Metallurgical Engineering

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

Ahmad Mahmood et al.

JOURNAL OF CENTRAL SOUTH UNIVERSITY (2020)

Article Computer Science, Artificial Intelligence

Failure mode classification and bearing capacity prediction for reinforced concrete columns based on ensemble machine learning algorithm

De-Cheng Feng et al.

ADVANCED ENGINEERING INFORMATICS (2020)

Article Chemistry, Multidisciplinary

Random Forests and Cubist Algorithms for Predicting Shear Strengths of Rockfill Materials

Jian Zhou et al.

APPLIED SCIENCES-BASEL (2019)

Article Construction & Building Technology

Feasibility of Stochastic Gradient Boosting Approach for Evaluating Seismic Liquefaction Potential Based on SPT and CPT Case Histories

Jian Zhou et al.

JOURNAL OF PERFORMANCE OF CONSTRUCTED FACILITIES (2019)

Article Multidisciplinary Sciences

Predicting seismic-induced liquefaction through ensemble learning frameworks

Mohammad H. Alobaidi et al.

SCIENTIFIC REPORTS (2019)

Article Chemistry, Multidisciplinary

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

Mahmood Ahmad et al.

APPLIED SCIENCES-BASEL (2019)

Article Engineering, Environmental

Improving prediction of soil liquefaction using hybrid optimization algorithms and a fuzzy support vector machine

Alireza Rahbarzare et al.

BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT (2019)

Article Engineering, Environmental

Calibration of Vs-based empirical models for assessing soil liquefaction potential using expanded database

Chen Guoxing et al.

BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT (2019)

Article Computer Science, Artificial Intelligence

A Levy flight-based grey wolf optimizer combined with back-propagation algorithm for neural network training

Shima Amirsadri et al.

NEURAL COMPUTING & APPLICATIONS (2018)

Article Business

Loan evaluation in P2P lending based on Random Forest optimized by genetic algorithm with profit score

Xin Ye et al.

ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS (2018)

Article Engineering, Environmental

Seismic liquefaction potential assessed by support vector machines approaches

Xinhua Xue et al.

BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT (2016)

Article Environmental Sciences

Application of genetic algorithm-based support vector machines for prediction of soil liquefaction

Xinhua Xue et al.

ENVIRONMENTAL EARTH SCIENCES (2016)

Review Geography, Physical

Random forest in remote sensing: A review of applications and future directions

Mariana Belgiu et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2016)

Article Computer Science, Interdisciplinary Applications

Classification of Rockburst in Underground Projects: Comparison of Ten Supervised Learning Methods

Jian Zhou et al.

JOURNAL OF COMPUTING IN CIVIL ENGINEERING (2016)

Article Engineering, Geological

Assessment of seismic liquefaction potential based on Bayesian network constructed from domain knowledge and history data

Ji-Lei Hu et al.

SOIL DYNAMICS AND EARTHQUAKE ENGINEERING (2016)

Article Engineering, Geological

Assessment and Prediction of Liquefaction Potential Using Different Artificial Neural Network Models: A Case Study

Abbas Abbaszadeh Shahri

GEOTECHNICAL AND GEOLOGICAL ENGINEERING (2016)

Article Computer Science, Interdisciplinary Applications

Predictive lithological mapping of Canada's North using Random Forest classification applied to geophysical and geochemical data

J. R. Harris et al.

COMPUTERS & GEOSCIENCES (2015)

Article Geosciences, Multidisciplinary

Evaluation of liquefaction potential based on CPT data using random forest

V. R. Kohestani et al.

NATURAL HAZARDS (2015)

Article Multidisciplinary Sciences

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

Pijush Samui et al.

JOURNAL OF ADVANCED RESEARCH (2015)

Article Engineering, Environmental

The use of neural networks for CPT-based liquefaction screening

Yusuf Erzin et al.

BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT (2015)

Article Engineering, Geological

A Bayesian network approach for predicting seismic liquefaction based on interpretive structural modeling

Ji-Lei Hu et al.

GEORISK-ASSESSMENT AND MANAGEMENT OF RISK FOR ENGINEERED SYSTEMS AND GEOHAZARDS (2015)

Article Biochemical Research Methods

Prediction of O-glycosylation Sites Using Random Forest and GA-Tuned PSO Technique

Hebatallah Hassan et al.

BIOINFORMATICS AND BIOLOGY INSIGHTS (2015)

Article Computer Science, Interdisciplinary Applications

Grey Wolf Optimizer

Seyedali Mirjalili et al.

ADVANCES IN ENGINEERING SOFTWARE (2014)

Article Engineering, Geological

Pore pressure generation in sand with bentonite: from small strains to liquefaction

C. S. El Mohtar et al.

GEOTECHNIQUE (2014)

Article Engineering, Geological

The Use of a Relevance Vector Machine in Predicting Liquefaction Potential

Pijush Samui et al.

INDIAN GEOTECHNICAL JOURNAL (2014)

Article Engineering, Geological

Determination of liquefaction susceptibility of soil: a least square support vector machine approach

Pijush Samui et al.

INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS (2013)

Article Engineering, Geological

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

R. Kayen et al.

JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING (2013)

Article Geosciences, Multidisciplinary

Application of the adaptive neuro-fuzzy inference system for prediction of soil liquefaction

Xinhua Xue et al.

NATURAL HAZARDS (2013)

Article Engineering, Geological

Liquefaction Potential Assessment of Pleistocene Beach Sands near Charleston, South Carolina

Tahereh Heidari et al.

JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING (2012)

Article Engineering, Ocean

Evaluation of Liquefaction Potential Index Along Western Coast of South Korea Using SPT and CPT

Min-Woo Seo et al.

MARINE GEORESOURCES & GEOTECHNOLOGY (2012)

Article Geosciences, Multidisciplinary

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

Pijush Samui et al.

JOURNAL OF APPLIED GEOPHYSICS (2011)

Article Geosciences, Multidisciplinary

Machine learning modelling for predicting soil liquefaction susceptibility

P. Samui et al.

NATURAL HAZARDS AND EARTH SYSTEM SCIENCES (2011)

Article Computer Science, Interdisciplinary Applications

Evaluation of liquefaction potential based on CPT results using evolutionary polynomial regression

Mohammad Rezania et al.

COMPUTERS AND GEOTECHNICS (2010)

Article Computer Science, Artificial Intelligence

Variable selection using random forests

Robin Genuer et al.

PATTERN RECOGNITION LETTERS (2010)

Article Computer Science, Information Systems

A systematic analysis of performance measures for classification tasks

Marina Sokolova et al.

INFORMATION PROCESSING & MANAGEMENT (2009)

Article Engineering, Civil

Seismic liquefaction potential assessment by using relevance vector machine

Pijush Samui

EARTHQUAKE ENGINEERING AND ENGINEERING VIBRATION (2007)

Article Engineering, Geological

Neural network model for liquefaction potential in soil deposits using Turkey and Taiwan earthquake data

Adel M. Hanna et al.

SOIL DYNAMICS AND EARTHQUAKE ENGINEERING (2007)

Article Computer Science, Interdisciplinary Applications

Evaluation of liquefaction potential of soil deposits using artificial neural networks

Adel M. Hanna et al.

ENGINEERING COMPUTATIONS (2007)

Article Computer Science, Interdisciplinary Applications

Support vector machines: Their use in geotechnical engineering as illustrated using seismic liquefaction data

Anthony T. C. Goh et al.

COMPUTERS AND GEOTECHNICS (2007)

Article Engineering, Geological

Support vector machines-based modelling of seismic liquefaction potential

Mahesh Pal

INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS (2006)

Review Engineering, Geological

CPT-based probabilistic and deterministic assessment of in situ seismic soil liquefaction potential

R. E. S. Moss et al.

JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING (2006)

Article Engineering, Geological

Semi-empirical procedures for evaluating liquefaction potential during earthquakes

IM Idriss et al.

SOIL DYNAMICS AND EARTHQUAKE ENGINEERING (2006)

Article Computer Science, Artificial Intelligence

An introduction to ROC analysis

Tom Fawcett

PATTERN RECOGNITION LETTERS (2006)

Article Engineering, Geological

Estimating liquefaction-induced lateral displacements using the standard penetration test or cone penetration test

G Zhang et al.

JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING (2004)

Article Engineering, Geological

Standard penetration test-based probabilistic and deterministic assessment of seismic soil liquefaction potential

KO Cetin et al.

JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING (2004)

Article Engineering, Geological

Simplified cone penetration test-based method for evaluating liquefaction resistance of soils

CH Juang et al.

JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING (2003)

Article Engineering, Geological

Assessing probability-based methods for liquefaction potential evaluation

CH Juang et al.

JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING (2002)

Article Engineering, Geological

Probabilistic neural network for evaluating seismic liquefaction potential

ATC Goh

CANADIAN GEOTECHNICAL JOURNAL (2002)

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)

Article Engineering, Geological

Liquefaction resistance of soils: Summary report from the 1996 NCEER and 1998 NCEER/NSF Workshops on Evaluation of Liquefaction Resistance of Soils

TL Youd et al.

JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING (2001)

Article Engineering, Geological

Liquefaction resistance of soils: Summary report from the 1996 NCEER and 1998 NCEER/NSF workshops on evaluation of liquefaction resistance of soils

TL Youd et al.

JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING (2001)

Article Engineering, Geological

Liquefaction resistance of soils from shear-wave velocity

RD Andrus et al.

JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING (2000)

Article Engineering, Geological

CPT-based liquefaction analysis, Part 1: Determination of limit state function

CH Juang et al.

GEOTECHNIQUE (2000)