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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 | Assessment of ROCPAD relay for islanding detection in distributed generation显示文摘 | Samui A Samantaray S R | 2011 | IEEE Trans on Smart Grid2011，2，2： | 1 |
| 8 | Maleic acid grafted low density polyethylene for thermally sprayabIe anticorrosive coatings 显示文摘 | SINGH S K TAMBE S P SAMUI A B | 2006 | Progress in Organic Coatings2006，，55： | 1 |
| 9 | Slope stability analysis:A support vector machine approach显示文摘 | Pijush Samui | 2008 | Environmental Geology2008，56，2： | 1 |
| 10 | Humidity sensing with weak acid-doped polyaniline and its com- posites显示文摘 | JAIN S CHAKANE S SAMUI A B | 2003 | Sensors and Actuators B2003，96，12： | 1 |
| 11 | Slope stability analysis:a support vector machine approach显示文摘 | Pijush Samui | 2008 | Environ Geol2008，56，2： | 1 |
| 12 | Studies on sequential interpenetrating polymer network based on nitrile rubber and poly(vinyl acetate)显示文摘 | Patri M Samui A Deb P | 1993 | J Appl Polym Sci1993，48，10： | 1 |
| 13 | OCR prediction using support vector machine based on piezocone data 显示文摘 | SAMUI P SITHARAM T G KURUP P U | 2008 | Journal of Geotechnical and Geoenvironmental Engineering (ASCE)2008，134，6： | 1 |
| 14 | Least square support vector machine applied to slope reliability analysis 显示文摘 | Pijushl Samui Tim Lansivaara Madhav R Bhatt | 2013 | Geotechnical and Geological Engineering2013，31，4： | 1 |
| 15 | A direct approach to op- timal feeder routing for radial distribution system显示文摘 | Samui A Singh S Ghose T | 2012 | IEEE Trans on Power Delivery2012，27，1： | 1 |
| 16 | Antioxidant and nitric oxide synthase activation properties of A uricularia auricular显示文摘 | ACHARYA KRISHNENDU SAMUI KRISHNENDU RAI MANJULA DUTTA BANI BRATA ACHARYA RUPA | 2004 | Indian Journal of Experimental Biology2004，42，5： | 1 |
| 17 | Flexibility improvement of epoxy resin by chemical modification D Ratna显示文摘 | AB Samui BC Chakraborty | 2004 | Polymer International2004，53，： | 1 |
| 18 | Synthesis and characterizationof liquid-crystalline epoxy and its blend with conventional epoxy显示文摘 | Sadagopan K Ratna D Samui A B | 2003 | Journal of Polymer Science Part A:Polymer Chemistry2003，41，21： | 1 |
| 19 | Hysteresis characteristics of high modulus low shrinkage polyester tire yarn and cord显示文摘 | Samui B K Prakasan M P Chakrabarty D | 2011 | Rubber Chemistry and Technology2011，84，4： | 1 |
| 20 | Graft copolymerization of methyl methacrylate on to guar gum with cerie ammonium sulfate/dextrose redox pair 显示文摘 | Chowdhury P Samui S Kundu T | 2001 | Journal of Applled Polymer Scienee2001，82，14： | 1 |
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