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Predicting Rock Burst in Underground Engineering Leveraging a Novel Metaheuristic-Based LightGBM Model

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

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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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Prediction of bearing capacity of pile foundation using deep learning approaches

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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

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Proposed numerical and machine learning models for fiber-reinforced polymer concrete-steel hollow and solid elliptical columns

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Frontiers of Structural and Civil Engineering 2024年第8期18卷 1169-1194页

作者： Tang QIONG Ishan JHA Alireza BAHRAMI Haytham F.ISLEEM Rakesh KUMAR pijush samui School of Applied Technologies Qujing Normal UniversityQujing 655011China Department of Civil Engineering Indian Institute of Technology-BHUVaranasi 221005India Department of Building Engineering Energy Systems and Sustainability ScienceFaculty of Engineering and Sustainable DevelopmentUniversity of GävleGävle 80176Sweden Department of Civil Engineering National Institute of Technology-PatnaPatna 800005India

This study employs a hybrid approach,integrating finite element method(FEM)simulations with machine learning(ML)techniques to investigate the structural performance of double-skin tubular columns(DSTCs)reinforced with glass fiber-reinforced polymer(GFRP).The investigation involves a comprehensive examination of critical parameters,including aspect ratio,concrete strength,number of GFRP confinement layers,and dimensions of steel tubes used in DSTCs,through comparative analyses and parametric *** ensure the credibility of the findings,the results are rigorously validated against experimental data,establishing the precision and trustworthiness of the *** present research work examines the use of the columns with elliptical cross-sections and contributes valuable insights into the application of FEM and ML in the design and evaluation of structural systems within the field of structural engineering.

关键词： elliptical column fiber-reinforced polymer machine learning finite element method ABAQUS

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Seismic liquefaction potential assessment by using relevance vector machine

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Earthquake Engineering and Engineering Vibration 2007年第4期6卷 331-336页

作者： pijush samui Department of Civil Engineering Indian Institute of Science Bangalore 560012 India

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 artificial 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.

关键词： liquefaction cone penetration test relevance vector machine artificial neural network

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Determination of rock depth using artificial intelligence techniques

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Geoscience Frontiers 2016年第1期7卷 61-66页

作者： R.Viswanathan pijush samui School of Information Technology & Engineering VIT University Centre for Disaster Mitigation and Management VIT University

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 *** 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.

关键词： Rock depth Spatial variability Least square support vector machine Gaussian process regression Extreme learning machine

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Liquefaction prediction using support vector machine model based on cone penetration data

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Frontiers of Structural and Civil Engineering 2013年第1期7卷 72-82页

作者： pijush samui Centre for Disaster Mitigation and Management VIT UniversityVellore-632014India

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 *** 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 *** SVM,a novel learning machine based on statistical theory,uses structural risk minimization(SRM)induction principle to minimize the *** cone resistance(q_(c))and cyclic stress ratio(CSR),model has been developed for prediction of liquefaction using *** an attempt has been made to simplify the model,requiring only two parameters(q_(c)and maximum horizontal acceleration a_(max)),for prediction of ***,developed SVM model has been applied to different case histories available globally and the results obtained confirm the capability of SVM *** 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 *** study shows that SVM can be used as a practical tool for prediction of liquefaction potential,based on field CPT data.

关键词： earthquake cone penetration test liquefaction support vector machine(SVM) prediction

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Analysis of Epimetamorphic Rock Slopes Using Soft Computing

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Journal of Shanghai Jiaotong university(Science) 2014年第3期19卷 274-278页

作者： KUMAR Manoj samui pijush Scientist-I National Institute of Rock Mechanics Kolar Gold Fields 563 117Karnataka India Centre for Disaster Mitigation and Management Vellore Institute of Technology

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.

关键词： epimetemorphic rock slope probability support vector machine(SVM) least square support vector machine(LSSVM) relevance vector machine(RVM)

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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

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Prediction of Compressive Strength of Self-Compacting Concrete Using Intelligent Computational Modeling

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Computers, Materials & Continua 2017年第2期53卷 157-174页

作者： Susom Dutta ARamachandra Murthy Dookie Kim pijush samui Undergraduate Student School of Mechanical&Building Sciences(SMBS)VIT UniversityVelloreTamil Nadu 632014India.Email:susomdutta7@*** Senior Scientist Computational Structural Mechanics GroupCSIR-Structural Engineering Research CentreTaramaniChennai-600113.Email:murthyarc@serc.res.in Professor Department of Civil EngineeringKunsan National UniversityKunsanJeonbukSouth Korea.Email:kim2kie@*** Associate Professor National Institute of TechnologyPatnaIndiaEmail:pijush@nitp.ac.in.

In the present scenario,computational modeling has gained much importance for the prediction of the properties of *** 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 *** 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 *** experimental results demonstrate that proposed models are robust for determination of compressive strength of self-compacting concrete.

关键词： Self Compacting Concrete(SCC) Compressive Strength Extreme Learning Machine(ELM) Adaptive Neuro Fuzzy Inference System(ANFIS) Multi Adaptive Regression Spline(MARS).

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