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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 | Mechanical properties of the sodium montrnorillonite interlayer intercalated with amino acids 显示文摘 | DINESH K PIJUSH G STEVEN S | 2005 | Biomaeromoleeules2005，6，6： | 1 |
| 9 | A brief resume on the genus Ailanthus:chemical and pharmacological aspects显示文摘 | Pijush K Subrata L | | 0，，03： | 1 |
| 10 | Slope stability analysis:a support vector machine approach显示文摘 | Pijush Samui | 2008 | Environ Geol2008，56，2： | 1 |
| 11 | Relationship Between Fractures,Fault Zones,Stress,and Reservoir Productivity in the Suban Gas Field,Sumatra,Indonesia显示文摘 | Peter Hennings Patricia Allwardt Pijush Paul | 2012 | AAPG Bulletin2012，96，4： | 1 |
| 12 | Quality Management System in Special Libraries of Colkata City: a Survey 显示文摘 | Ranjan Sinha Thakur Pijush Kanti Jana | 2012 | IASLIC Bulletin2012，57，1： | 1 |
| 13 | Selective binding of divalent cations toward heme proteins显示文摘象 Hg, Cu 和 Cd 一样的转变金属的潜在的毒性是众所周知的,他们向蛋白质的亲密关系担心大。这个工作探索和 heme 蛋白质 leghemoglobin,肌球素和细胞色素 C 的 Cu 2+, Hg 2+ 和 Cd 2+ 的相互作用的选择性质。有约束力的侧面用吸收度光谱和不变的荧光光谱学被分析。相似热含量,熵和免费精力改变的热力学的参数被等温的热量测定导出,作为结果的有约束力的参数为这些 heme 蛋白质被比较。免费精力(DG ) 价值揭示了向肌球素和 leghemoglobin 有约束力的 Cu 2+ 与为 Hg 2+ 或 Cd 2+ 的弱绑定相对照特定、灵巧。预定相关单个光子认为为建议双分子的碰撞被涉及的金属 complexed 肌球素和 leghemoglobin 在激动的州的一生显示了重要改变。有趣地,任何一个都没这些阳离子证明为削尖那的细胞色素 c 的重要亲密关系,保存序列的存在或 heme 组不是为向残余的 heme 蛋白质,而是微型环境的阳离子绑定的唯一的标准或一个特定的合拢模式可能为这些负责微分变化形式侧面。这些阳离子的绑定可以调制这些生物学上重要的蛋白质的符合构造和函数。 | Pijush Basak Tanay Debnath Rajat Banerjee Maitree Bhattacharyya | 2016 | Frontiers in Biology2016，11，1： | 1 |
| 14 | 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 |
| 15 | Coherent-interface-induced strain in large lattice-mismatched materials:A new approach for modeling Raman shift显示文摘Strain engineering as one of the most powerful techniques for tuning optical and electronic properties of III-nitrides requires reliable methods for strain investigation.In this work,we reveal,that the linear model based on the experimental data limited to within a small range of biaxial strains(<0.2%),which is widely used for the non-destructive Raman study of strain with nanometer-scale spatial resolution is not valid for the binary wurtzite-structure group-III nitrides GaN and AlN.Importantly,we found that the discrepancy between the experimental values of strain and those calculated via Raman spectroscopy increases as the strain in both GaN and AlN increases.Herein,a new model has been developed to describe the strain-induced Raman frequency shift in GaN and AlN for a wide range of biaxial strains(up to 2.5%).Finally,we proposed a new approach to correlate the Raman frequency shift and strain,which is based on the lattice coherency in the epitaxial layers of superlattice structures and can be used for a wide range of materials. | Andrian V.Kuchuk Fernando Mde Oliveira Pijush K.Ghosh Yuriy I.Mazur Hryhorii V.Stanchu Marcio D.Teodoro Morgan E.Ware Gregory J.Salamo | 2022 | Nano Research2022，15，3： | 1 |
| 16 | Characteristics of ground vibrations and structural response to surface and underground blasting显示文摘 | Pijush P R | 1998 | Geotechnical and Geological Engineering1998，16，2： | 1 |
| 17 | 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 |
| 18 | A brief resume on the genusAilan- thus: chemical and pharmacological aspects 显示文摘 | Pijush K Subrata L | 2009 | Phyto- chem Rev2009，9，3： | 1 |
| 19 | Thermal and morphological analysis of thermoplastic polyurethane-clay nanocomposites: Comparison of efficacy of dual modified laponite vs commercial montmorillonites显示文摘 | Manas M Pijush K Chattopadh Y | 2010 | Thermochimica Acta2010，，510： | 1 |
| 20 | A unified computation framework for the Mikowski operations显示文摘 | Pijush K Ghosh | 1993 | Computers & Graphics1993，17，4： | 1 |
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