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Computer Modeling in Engineering & Sciences 2024年第7期140卷 229-253页

作者： Kai Wang Biao He pijush Samui Jian Zhou No.Three Engineering Co. Ltd.CCCC First Highway Engineering CompanyBeijing101102China Department of Civil Engineering Faculty of EngineeringUniversiti MalayaKuala Lumpur50603Malaysia Department of Civil Engineering National Institute of Technology PatnaBihar800005India School of Resources and Safety Engineering Central South UniversityChangsha410083China

Rock bursts represent a formidable challenge in underground engineering,posing substantial risks to both infrastructure and human *** sudden and violent failures of rock masses are characterized by the rapid release of accumulated stress within the rock,leading to severe seismic events and structural ***,the development of reliable prediction models for rock bursts is paramount to mitigating these *** study aims to propose a tree-based model—a Light Gradient Boosting Machine(LightGBM)—to predict the intensity of rock bursts in underground engineering.322 actual rock burst cases are collected to constitute an exhaustive rock burst dataset,which serves to train the *** population-basedmetaheuristic algorithms are used to optimize the hyperparameters of the LightGBM ***,the sensitivity analysis is used to identify the predominant factors that may incur the occurrence of rock *** results show that the population-based metaheuristic algorithms have a good ability to search out the optimal hyperparameters of the LightGBM *** developed LightGBM model yields promising performance in predicting the intensity of rock bursts,with which accuracy on training and testing sets are 0.972 and 0.944,*** sensitivity analysis discloses that the risk of occurring rock burst is significantly sensitive to three factors:uniaxial compressive strength(σc),stress concentration factor(SCF),and elastic strain energy index(Wet).Moreover,this study clarifies the particular impact of these three factors on the intensity of rock bursts through the partial dependence plot.

关键词： Rock burst prediction LightGBM coati optimization algorithm pelican optimization algorithm partial dependence plot

Machine learning-enhanced Monte Carlo and subset simulations for advanced risk assessment in transportation infrastructure

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Journal of Mountain Science 2024年第2期21卷 690-717页

作者： Furquan AHMAD pijush SAMUI S.S.MISHRA Department of Civil Engineering National Institute of TechnologyPatna 800006India

The maintenance of safety and dependability in rail and road embankments is of utmost importance in order to facilitate the smooth operation of transportation *** study introduces a comprehensive methodology for soil slope stability evaluation,employing Monte Carlo Simulation(MCS)and Subset Simulation(SS)with the"UPSS 3.0 Add-in"in *** on an 11.693-meter embankment with a soil slope(inclination ratio of 2H:1V),the investigation considers earthquake coefficients(kh)and pore water pressure ratios(ru)following Indian zoning *** chance of slope failure showed a considerable increase as the Coefficient of Variation(COV),seismic coefficients(kh),and pore water pressure ratios(ru)experienced an *** SS approach showed exceptional efficacy in calculating odds of failure that are notably *** computational modeling,the study optimized the worst-case scenario using ANFIS-GA,ANFIS-GWO,ANFIS-PSO,and ANFIS-BBO *** ANFIS-PSO model exhibits exceptional accuracy(training R2=0.9011,RMSE=0.0549;testing R2=0.8968,RMSE=0.0615),emerging as the most *** study highlights the significance of conducting thorough risk assessments and offers practical insights into evaluating and improving the stability of soil slopes in transportation *** findings contribute to the enhancement of safety and reliability in real-world situations.

关键词： Monte Carlo Simulation Subset Simulation Machine Learning Seismic coefficient

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Fluid Mechanics

Prediction of bearing capacity of pile foundation using deep learning approaches

2010年

作者： kundu, pijush K. Cohen, Ira M.

ISBN: (数字)9780123814005

ISBN: (纸本)9780123813992

Fluid mechanics, the study of how fluids behave and interact under various forces and in various applied situations hether in the liquid or gaseous state or both s introduced and comprehensively covered in this widely adopted text. Fluid Mechanics, Fourth Edition is the leading advanced general text on fluid mechanics. Changes for the 4th edition from the 3rd edition: Updates to several chapters and sections, including Boundary Layers, Turbulence, Geophysical Fluid Dynamics, Thermodynamics and Compressibility Fully revised and updated chapter on computational fluid dynamics New chapter on Biofluid Mechanics by Professor Portonovo Ayyaswamy, the Asa Whitney Professor of Dynamical Engineering at the University of Pennsylvania

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Frontiers of Structural and Civil Engineering 2024年第6期18卷 870-886页

作者： Manish KUMAR Divesh Ranjan KUMAR Jitendra KHATTI pijush SAMUI Kamaldeep Singh GROVER Department of Civil Engineering SRM Institute of Science and Technology Tiruchirappalli CampusTrichy 621105India Department of Civil Engineering National Institute of Technology PatnaBihar 800005India Department of Civil Engineering Faculty of EngineeringThammasat School of EngineeringThammasat UniversityBangkok10200Thailand Department of Civil Engineering Rajasthan Technical UniversityKota 324010India

The accurate prediction of bearing capacity is crucial in ensuring the structural integrity and safety of pile *** research compares the Deep Neural Networks(DNN),Convolutional Neural Networks(CNN),Recurrent Neural Networks(RNN),Long Short-Term Memory(LSTM),and Bidirectional LSTM(BiLSTM)algorithms utilizing a data set of 257 dynamic pile load tests for the first ***,this research illustrates the multicollinearity effect on DNN,CNN,RNN,LSTM,and BiLSTM models’performance and accuracy for the first time.A comprehensive comparative analysis is conducted,employing various statistical performance parameters,rank analysis,and error matrix to evaluate the performance of these *** performance is further validated using external validation,and visual interpretation is provided using the regression error characteristics(REC)curve and Taylor *** from the comparative analysis reveal that the DNN(Coefficient of determination(R^(2))_(training(TR))=0.97,root mean squared error(RMSE)_(TR)=0.0413;R^(2)_(testing(TS))=0.9,RMSE_(TS)=0.08)followed by BiLSTM(R^(2)_(TR)=0.91,RMSE_(TR)=0.782;R^(2)_(TS)=0.89,RMSE_(TS)=0.0862)model demonstrates the highest performance *** is noted that the BiLSTM model is better than LSTM because the BiLSTM model,which increases the amount of information for the network,is a sequence processing model made up of two LSTMs,one of which takes the input in a forward manner,and the other in a backward *** prediction of pile-bearing capacity is strongly influenced by ram weight(having a considerable multicollinearity level),and the effect of the considerable multicollinearity level has been determined for the model based on the recurrent neural network *** this study,the recurrent neural network model has the least performance and accuracy in predicting the pile-bearing capacity.

关键词： deep learning algorithms high-strain dynamic pile test bearing capacity of the pile

Dynamical behaviors of a constant prey refuge ratio-dependent prey-predator model with Allee and fear effects

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International Journal of Biomathematics 2024年第1期17卷 227-250页

作者： Soumitra Pal pijush Panday Nikhil Pal A.K.Misra Joydev Chattopadhyays Department of Mathematics Institute of Science Banaras Hindu University Varanasi 221005India Department of Mathematical and Statistical Sciences University of Alberta EdmontonAB T6G 2G1Canada Department of Mathematics Visva-Bharati Santiniketan 731235India Agricultural and Ecological Research Unit Indian Statistical Institute 203 B.T.RoadKolkata 700108India

In this paper,we consider a nonlinear ratio-dependent prey-predator model with constant prey refuge in the prey *** Allee and fear phenomena are incorporated explicitly in the growth rate of the prey *** qualitative behaviors of the proposed model are investigated around the equilibrium points in *** bifurcation including its direction and stability for the model is also *** observe that fear of predation risk can have both stabilizing and destabilizing effects and induces bubbling phenomenon in the *** is also observed that for a fixed strength of fear,an increase in the Allee parameter makes the system unstable,whereas an increase in prey refuge drives the system toward ***,higher values of both the Allee and prey refuge parameters have negative impacts and the populations go to ***,we explore the variation of densities of the populations in different bi-parameter spaces,where the coexistence equilibrium point remains *** simulations are carried out to explore the dynamical behaviors of the system with the help of MATLAB software.

关键词： Predator-prey system Allee effect fear effect prey refuge bifurcation population density

Separation of Amino Acids Based on Thin-Layer Chromatography by a Novel Quinazoline Based Anti-Microbial Agent

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American Journal of Analytical Chemistry 2012年第9期3卷 669-674页

作者： Supriti Sen Sandipan Sarkar pijush kundu Subrata Laskar Department of Chemistry The University of Burdwan Golapbag India

A newly designed quinazoline based compound, 6-pyridin-2-yl-5,6-dihydro-benzo[4,5]imidazo[1,2-c] quinazoline (PDBIQ) has shown the ability for the easy detection of nineteen amino acids on thin-layer chromatography plates as a spray reagent. This new reagent enabled to produce various distinguishable colors with amino acids with different RF values. The detection limits and the binding ability of PDBIQ with amino acids have been calculated. PDBIQ is also able to detect aminoacids from hydrolised seed protein. The title compound also exhibited profound inhibitory action against some gm (+ve) and gm (-ve) bacterial organisms. This paper deals with synthesis, spectroscopic application and biological evaluation of the organic moity.

关键词： Thin-Layer Chromatography Amino Acid Ninhydrin Binding Constant Antimicrobial Property

Ensemble unit and Al techniques for prediction of rock strain

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Frontiers of Structural and Civil Engineering 2022年第7期16卷 858-870页

作者： Pradeep T pijush SAMUI Navid KARDANI Panagiotis G ASTERIS Civil Engineering Department National Institute of TechnologyPatna 800005India Civil and Infrastructure Discipline School of EngineeringRoyal Melbourne Institute of Technology(RMIT)MelbourneVictoriaAustralia Computational Mechanics Laboratory School of Pedagogical and Technological EducationHeraklionGR 14121Greece

The behavior of rock masses is influenced by a variety of forces,with measurement of stress and strain playing the most critical roles in assessing *** laboratory test for determining strain at each location within rock samples is expensive and difficult but rock strain data are important for predicting failure of rock *** researchers employ AI technology in order to solve these *** algorithms such as gradient boosting machine(GBM),support vector regression(SVR),random forest(RF),and group method of data handling(GMDH)are used to efficiently estimate the strain at every point within a rock ***,the ensemble unit(EnU)may be utilized to evaluate rock *** this study,3000 experimental data are used for the purpose of *** obtained strain values are then evaluated using various statistical parameters and compared to each other using *** analysis,stress-strain curve,Young’s modulus,Poisson’s ratio,actual *** curve,error matrix and the Akaike’s information criterion(AIC)values are used for comparing *** GBM model achieved 98.16%and 99.98%prediction accuracy(in terms of values of R2)in the longitudinal and lateral dimensions,respectively,during the testing *** GBM model,based on the experimental data,has the potential to be a new option for engineers to use when assessing rock strain.

关键词： prediction strain ensemble unit rank analysis error matrix

Predicting Resource Availability in Local Mobile Crowd Computing Using Convolutional GRU

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Computers, Materials & Continua 2022年第3期70卷 5199-5212页

作者： pijush Kanti Dutta Pramanik Nilanjan Sinhababu Anand Nayyar Mehedi Masud Prasenjit Choudhury Department of Computer Science&Engineering National Institute of TechnologyDurgapurIndia Reliability Engineering Centre Indian Institute of Technology KharagpurIndia Graduate School Duy Tan UniversityDa Nang550000Vietnam Faculty of Information Technology Duy Tan UniversityDa Nang550000Vietnam Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

In mobile crowd computing(MCC),people’s smart mobile devices(SMDs)are utilized as computing *** the ever-growing computing capabilities of today’s SMDs,a collection of them can offer significantly high-performance computing *** a localMCC,the SMDs are typically connected to a local Wi-Fi *** and institutions can leverage the SMDs available within the campus to form local MCCs to cater to their computing needs without any financial and operational *** it offers an economical and sustainable computing solution,users’mobility poses a serious issue in the QoS of *** address this,before submitting a job to an SMD,we suggest estimating that particular SMD’s availability in the network until the job is *** this,we propose a convolutional GRU-based prediction model to assess how long an SMD is likely to be available in the network from any given point of *** experimental purposes,we collected real users’mobility data(in-time and outtime)with respect to a Wi-Fi access *** build the prediction model,we presented a novel feature extraction method to be applied to the time-series *** experimental results prove that the proposed convolutional GRU model outperforms the conventional GRU model.

关键词： Resource selection resource availability mobile grid mobile cloud ad-hoc cloud crowd computing deep learning GRU CNN RNN

Novel integration of extreme learning machine and improved Harris hawks optimization with particle swarm optimization-based mutation for predicting soil consolidation parameter

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Journal of Rock Mechanics and Geotechnical Engineering 2022年第5期14卷 1588-1608页

作者： Abidhan Bardhan Navid Kardani Abdel Kareem Alzo'ubi Bishwajit Roy pijush Samui Amir HGandomi Department of Civil Engineering National Institute of Technology PatnaPatna800005India Discipline of Civil and Infrastructure Engineering School of EngineeringRoyal Melbourne Institute of Technology(RMIT)MelbourneVIC3001Australia Department of Civil Engineering Abu Dhabi UniversityAbu DhabiUnited Arab Emirates School of Computer Science Engineering and Technology Bennett UniversityGreater NoidaIndia Faculty of Engineering and Information Technology University of Technology SydneySydneyNSW2007Australia

The study proposes an improved Harris hawks optimization(IHHO) algorithm by integrating the standard Harris hawks optimization(HHO) algorithm and mutation-based search mechanism for developing a high-performance machine learning solution for predicting soil compression index. HHO is a newly introduced meta-heuristic optimization algorithm(MOA) used to solve continuous search *** to the original HHO, the proposed IHHO can evade trapping in local optima, which in turn raises the search capabilities and enhances the search mechanism relying on mutation. Subsequently, a novel meta-heuristic-based soft computing technique called ELM-IHHO was established by integrating IHHO and extreme learning machine(ELM) to estimate soil compression index. A sum of 688 consolidation test data was collected for this purpose from an ongoing dedicated freight corridor railway project. To evaluate the generalization capability of the proposed ELM-IHHO model, a detailed comparison between ELM-IHHO and other well-established MOAs, such as particle swarm optimization,genetic algorithm, and biogeography-based optimization integrated with ELM, was performed. Based on the outcomes, the ELM-IHHO model exhibits superior performance over the other MOAs in predicting soil compression index.

关键词： Compression index Artificial intelligence Swarm intelligence Meta-heuristic optimization Dedicated freight corridor