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| 1 | Seismic liquefaction potential assessment by using relevance vector machine显示文摘Determining the liquefaction potential of soil is important in earthquake engineering. This study proposes the use of the Relevance Vector Machine (RVM) to determine the liquefaction potential of soil by using actual cone penetration test (CPT) data. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. The results are compared with a widely used artifi cial neural network (ANN) model. Overall, the RVM shows good performance and is proven to be more accurate than the ANN model. It also provides probabilistic output. The model provides a viable tool for earthquake engineers to assess seismic conditions for sites that are susceptible to liquefaction. | Pijush Samui | 2007 | Earthquake Engineering and Engineering Vibration2007，6，4： | 4 |
| 2 | Prediction of Compressive Strength of Self-Compacting Concrete Using Intelligent Computational Modeling显示文摘In the present scenario,computational modeling has gained much importance for the prediction of the properties of concrete.This paper depicts that how computational intelligence can be applied for the prediction of compressive strength of Self Compacting Concrete(SCC).Three models,namely,Extreme Learning Machine(ELM),Adaptive Neuro Fuzzy Inference System(ANFIS)and Multi Adaptive Regression Spline(MARS)have been employed in the present study for the prediction of compressive strength of self compacting concrete.The contents of cement(c),sand(s),coarse aggregate(a),fly ash(f),water/powder(w/p)ratio and superplasticizer(sp)dosage have been taken as inputs and 28 days compressive strength(fck)as output for ELM,ANFIS and MARS models.A relatively large set of data including 80 normalized data available in the literature has been taken for the study.A comparison is made between the results obtained from all the above-mentioned models and the model which provides best fit is established.The experimental results demonstrate that proposed models are robust for determination of compressive strength of self-compacting concrete. | Susom Dutta ARamachandra Murthy Dookie Kim Pijush Samui | 2017 | Computers, Materials & Continua2017，，2： | 3 |
| 3 | Application of soft computing techniques for shallow foundation reliability in geotechnical engineering显示文摘This research focuses on the application of three soft computing techniques including Minimax Probability Machine Regression(MPMR),Particle Swarm Optimization based Artificial Neural Network(ANN-PSO)and Particle Swarm Optimization based Adaptive Network Fuzzy Inference System(ANFIS-PSO)to study the shallow foundation reliability based on settlement criteria.Soil is a heterogeneous medium and the involvement of its attributes for geotechnical behaviour in soil-foundation system makes the prediction of settlement of shallow a complex engineering problem.This study explores the feasibility of soft computing techniques against the deterministic approach.The settlement of shallow foundation depends on the parametersγ(unit weight),e0(void ratio)and CC(compression index).These soil parameters are taken as input variables while the settlement of shallow foundation as output.To assess the performance of models,different performance indices i.e.RMSE,VAF,R^2,Bias Factor,MAPE,LMI,U(95),RSR,NS,RPD,etc.were used.From the analysis of results,it was found that MPMR model outperformed PSO-ANFIS and PSO-ANN.Therefore,MPMR can be used as a reliable soft computing technique for non-linear problems for settlement of shallow foundations on soils. | Rahul Ray Deepak Kumar Pijush Samui Lal Bahadur Roy A.T.C.Goh Wengang Zhang | 2021 | Geoscience Frontiers2021，12，1： | 3 |
| 4 | Hybrid ensemble soft computing approach for predicting penetration rate of tunnel boring machine in a rock environment显示文摘This study implements a hybrid ensemble machine learning method for forecasting the rate of penetration(ROP) of tunnel boring machine(TBM),which is becoming a prerequisite for reliable cost assessment and project scheduling in tunnelling and underground projects in a rock environment.For this purpose,a sum of 185 datasets was collected from the literature and used to predict the ROP of TBM.Initially,the main dataset was utilised to construct and validate four conventional soft computing(CSC)models,i.e.minimax probability machine regression,relevance vector machine,extreme learning machine,and functional network.Consequently,the estimated outputs of CSC models were united and trained using an artificial neural network(ANN) to construct a hybrid ensemble model(HENSM).The outcomes of the proposed HENSM are superior to other CSC models employed in this study.Based on the experimental results(training RMSE=0.0283 and testing RMSE=0.0418),the newly proposed HENSM is potential to assist engineers in predicting ROP of TBM in the design phase of tunnelling and underground projects. | Abidhan Bardhan Navid Kardani Anasua GuhaRay Avijit Burman Pijush Samui Yanmei Zhang | 2021 | Journal of Rock Mechanics and Geotechnical Engineering2021，13，6： | 2 |
| 5 | Determination of rock depth using artificial intelligence techniques显示文摘This article adopts three artificial intelligence techniques, Gaussian Process Regression(GPR), Least Square Support Vector Machine(LSSVM) and Extreme Learning Machine(ELM), for prediction of rock depth(d) at any point in Chennai. GPR, ELM and LSSVM have been used as regression techniques.Latitude and longitude are also adopted as inputs of the GPR, ELM and LSSVM models. The performance of the ELM, GPR and LSSVM models has been compared. The developed ELM, GPR and LSSVM models produce spatial variability of rock depth and offer robust models for the prediction of rock depth. | R.Viswanathan Pijush Samui | 2016 | Geoscience Frontiers2016，7，1： | 2 |
| 6 | Performance assessment of genetic programming(GP)and minimax probability machine regression(MPMR)for prediction of seismic ultrasonic attenuation显示文摘The determination of seismic attenuation(s)(dB/cm) is a challenging task in earthquake science.This article employs genetic programming(GP) and minimax probability machine regression(MPMR) for prediction of s. GP is developed based on genetic algorithm. MPMR maximizes the minimum probability of future predictions being within some bound of the true regression function. Porosity(n)(%), permeability(k)(millidarcy), grain size(d)(lm), and clay content(c)(%) have been considered as inputs of GP and MPMR.The output of GP and MPMR is s. The developed GP gives an equation for prediction of s. The results of GP and MPMR have been compared with the artificial neural network. This article gives robust models based on GP and MPMR for prediction of s. | Manoj Kumar Manav Mittal Pijush Samui | 2013 | Earthquake Science2013，26，2： | 2 |
| 7 | Slope stability analysis:A support vector machine approach显示文摘 | Pijush Samui | 2008 | Environmental Geology2008，56，2： | 1 |
| 8 | Slope stability analysis:a support vector machine approach显示文摘 | Pijush Samui | 2008 | Environ Geol2008，56，2： | 1 |
| 9 | Multivariate adap-tive regression spline (MARS) and least squares supportvector machine C LSSVM) for OCR prediction 显示文摘 | SAMUI PIJUSH KURUP PRADEEP | 2012 | SoftComputing2012，16，8： | 1 |
| 10 | Liquefaction prediction using support vector machine model based on cone penetration data显示文摘A support vector machine(SVM)model has been developed for the prediction of liquefaction susceptibility as a classification problem,which is an imperative task in earthquake engineering.This paper examines the potential of SVM model in prediction of liquefaction using actual field cone penetration test(CPT)data from the 1999 Chi-Chi,Taiwan earthquake.The SVM,a novel learning machine based on statistical theory,uses structural risk minimization(SRM)induction principle to minimize the error.Using cone resistance(q_(c))and cyclic stress ratio(CSR),model has been developed for prediction of liquefaction using SVM.Further an attempt has been made to simplify the model,requiring only two parameters(q_(c)and maximum horizontal acceleration a_(max)),for prediction of liquefaction.Further,developed SVM model has been applied to different case histories available globally and the results obtained confirm the capability of SVM model.For Chi-Chi earthquake,the model predicts with accuracy of 100%,and in the case of global data,SVM model predicts with accuracy of 89%.The effect of capacity factor(C)on number of support vector and model accuracy has also been investigated.The study shows that SVM can be used as a practical tool for prediction of liquefaction potential,based on field CPT data. | Pijush SAMUI | 2013 | Frontiers of Structural and Civil Engineering2013，7，1： | 1 |
| 11 | Support vector machine applied to settlement of shallow foundations on eohesionless soils显示文摘 | Pijush Samui | 2008 | Computers and Geotechnics2008，35，： | 1 |
| 12 | Least Square Support Vector Machine and Relevance Vector Machine for Evaluating Seismic Liquefaction Potential Using SPT显示文摘 | SAMUI PIJUSH | | 0，，02： | 1 |
| 13 | Prediction of compressive strength of self-compacting concrete using least square support vector machine and relevance vector machine显示文摘 | Bhairevi Ganesh Aiyer Dookie Kim Nithin Karingattikkal Pijush Samui P. Ramamohan Rao | 2014 | KSCE Journal of Civil Engineering2014，，6： | 1 |
| 14 | Determination of effective stress parameter of unsaturated soils:A Gaussian process regression approach显示文摘This article examines the capability of Gaussian process regression(GPR)for prediction of effective stress parameter(χ)of unsaturated soil.GPR method proceeds by parameterising a covariance function,and then infers the parameters given the data set.Input variables of GPR are net confining pressure(σ_(3)),saturated volumetric water content(θ_(s)),residual water content(θ_(r)),bubbling pressure(h_(b)),suction(s)and fitting parameter(l).A comparative study has been carried out between the developed GPR and Artificial Neural Network(ANN)models.A sensitivity analysis has been done to determine the effect of each input parameter onχ.The developed GPR gives the variance of predictedχ.The results show that the developed GPR is reliable model for prediction ofχof unsaturated soil. | Pijush Samui Jagan J | 2013 | Frontiers of Structural and Civil Engineering2013，7，2： | 1 |
| 15 | Vector machine techniques for modeling of seismic liquefaction data显示文摘 | Pijush Samui | 2013 | Ain Shams Engineering Journal2013，05，： | 1 |
| 16 | Slope stability analysis: a support vector machine approach 显示文摘 | Pijush Samui | 2008 | Environmental Geology2008，，56： | 1 |
| 17 | Slope stability analysis: a support vector machine approach显示文摘 | Pijush Samui | 2008 | Environmental Geology2008，，2： | 1 |
| 18 | Support vector machine applied to settlement of shallow foundations on cohesionless soils 显示文摘 | Pijush Samui | 2008 | Computers and Geotechnics2008，35，3： | 1 |
| 19 | Analysis of Epimetamorphic Rock Slopes Using Soft Computing显示文摘This article adopts three soft computing techniques including support vector machine(SVM), least square support vector machine(LSSVM) and relevance vector machine(RVM) for prediction of status of epimetemorphic rock slope. The input variables of SVM, LSSVM and RVM are bulk density, height, inclination, cohesion and internal friction angle. There are 53 datasets which have been used to develop the SVM, LSSVM and RVM models. The developed SVM, LSSVM and RVM give equations for prediction of status of epimetemorphic rock slope. The performance of SVM, LSSVM and RVM is 100%. A comparative study has been presented between the developed SVM, LSSVM and RVM. The results confirm that the developed SVM, LSSVM and RVM are effective tools for prediction of status of epimetemorphic rock slope. | KUMAR Manoj SAMUI Pijush | 2014 | Journal of Shanghai Jiaotong university(Science)2014，19，3： | 0 |
| 20 | Reliability Analysis of Piled Raft Foundation Using a Novel Hybrid Approach of ANN and Equilibrium Optimizer显示文摘In many civil engineering projects,Piled Raft Foundations(PRFs)are usually preferred where the incoming load fromthe superstructures is very high.In geotechnical engineering practice,the settlement of soil layers is a critical issue for the serviceability of the structures.Thus,assessment of risk associated with the structures corresponding to the maximum allowable settlement of soils needs to be carried out in the design phase.In this study,reliability analysis of PRF based on settlement criteria is performed using a high-performance hybrid soft computing model.The new approach is an integration of the artificial neural network(ANN)and a recently developed meta-heuristic algorithm called equilibrium optimizer(EO).The concept of reliability index was used to explore the feasibility of a newly constructed hybrid model of ANN and EO(i.e.,ANN-EO)against the conventional approach of calculating the probability of failure of PRF.Experimental results show that the proposed ANN-EO attained the most accurate prediction with R^(2)=0.9914 and RMSE=0.0518 in the testing phase,which are significantly better than those obtained from conventional ANN,multivariate adaptive regression splines,and genetic programming,including the ANNoptimized with particle swarmoptimization developed in this study.Based on the experimental results of different settlement values,the newly constructedANN-EOis very potential to analyze the risk associatedwith civil engineering structures.Also,the present study would significantly contribute to the knowledge pool of reliability studies related to piled raft systems because the works of literature on reliability analysis of piled raft systems are relatively scarce. | Abidhan Bardhan Priyadip Manna Vinay Kumar Avijit Burman Bojan Zlender Pijush Samui | 2021 | Computer Modeling in Engineering & Sciences2021，，9： | 0 |
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