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Integrating the effect of abutments in estimating the average vertical stress of elastic hard rock pillars by combining numerical modelling and artificial neural networks

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Underground Space 2023年第6期13卷 121-135页

作者： Nevaid Dzimunya Yoshiaki Fujii Youhei Kawamura Faculty of Engineering Hokkaido UniversitySapporo N13W8Japan

Estimating average vertical pillar stresses is a critical step in designing room-and-pillar *** analytical methods can be used to estimate the vertical stresses acting on the ***,the present analytical methods fail to adequately account for the influence of abutments on the distribution of vertical stresses,especially when applied to narrow panel widths and pillar layouts comprising evenly spaced *** this study,a multi-layer perceptron neural network(MLPNN)was applied to predict the vertical loads of regular pillars more *** of room-and-pillar mine layouts were modeled using a displacement discontinuity method(DDM),and a database of 2355 sampled pillar cases was *** MLPNN was trained based on this database,and its prediction capabilities were further validated using simulations by a finite difference code(i.e.,FLAC3D).The model predictions and the FLAC3D simulations reasonably agreed with a regression coefficient of *** model was also adapted for mine cases with evenly spaced barrier pillars,and its application to a real case study mine has shown to provide accurate pillar stress estimations;hence,this model is suitable for practical use at *** though the MLPNN model cannot be applied universally to all mine situations,it seems as a significant improvement over existing analytical techniques in terms of accounting for the influence of abutments on pillar stresses.

关键词： Pillar stress abutments Multi-layer perceptron neural network Numerical simulation Room-and-pillar mine

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Predicting and validating the load-settlement behavior of large-scale geosynthetic-reinforced soil abutments using hybrid intelligent modeling

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Journal of Rock Mechanics and Geotechnical Engineering 2023年第3期15卷 773-788页

作者： Muhammad Nouman Amjad Raja Syed Taseer Abbas Jaffar Abidhan Bardhan Sanjay Kumar Shukla Department of Civil Engineering School of EngineeringUniversity of Management and TechnologyLahorePakistan Department of Civil Engineering National Institute of Technology(NIT)PatnaPatnaBihar800005India Geotechnical and Geoenvironmental Research Group School of EngineeringEdith Cowan UniversityJoondalupPerthAustralia Department of Civil Engineering Delhi Technological UniversityDelhiIndia

Settlement prediction of geosynthetic-reinforced soil(GRS)abutments under service loading conditions is an arduous and challenging task for practicing geotechnical/civil ***,in this paper,a novel hybrid artificial intelligence(AI)-based model was developed by the combination of artificial neural network(ANN)and Harris hawks’optimisation(HHO),that is,ANN-HHO,to predict the settlement of the GRS *** other robust intelligent models such as support vector regression(SVR),Gaussian process regression(GPR),relevance vector machine(RVM),sequential minimal optimisation regression(SMOR),and least-median square regression(LMSR)were constructed and compared to the ANN-HHO *** predictive strength,relalibility and robustness of the model were evaluated based on rigorous statistical testing,ranking criteria,multi-criteria approach,uncertainity analysis and sensitivity analysis(SA).Moreover,the predictive veracity of the model was also substantiated against several large-scale independent experimental studies on GRS abutments reported in the scientific *** acquired findings demonstrated that the ANN-HHO model predicted the settlement of GRS abutments with reasonable accuracy and yielded superior performance in comparison to counterpart ***,it becomes one of predictive tools employed by geotechnical/civil engineers in preliminary decision-making when investigating the in-service performance of GRS ***,the model has been converted into a simple mathematical formulation for easy hand calculations,and it is proved cost-effective and less time-consuming in comparison to experimental tests and numerical simulations.

关键词： Geosynthetic-reinforced soil(GRS) abutments Settlement estimation Predictive modeling Artificial intelligence(AI) Artificial neural network(ANN)-Harris hawks’optimisation(HHO)

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VORTEX FLOW FIELD IN A SCOUR HOLE AROUND abutments

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International Journal of Sediment Research 2003年第4期18卷 310-325页

作者： Abdul Karim BARBHUIYA,Subhasish DEY West Bengal India. Lecturer Department of Applied Mechanics National Institute of Technology Silchar 788010 Assam India Associate Professor Department of Civil Engineering Indian Institute of Technology Kharagpur 721302

The three-dimensional flow field in a scour hole around different abutments under a clear water regime was experimentally measured in a laboratory flume, using the Acoustic Doppler Velocimeter (ADV). Three types of abutments used in the experiments were vertical-wall (rectangular section), 45° wing-wall (45° polygonal section) and semicircular. The three-dimensional time-averaged velocity components were detected at different vertical planes for vertical-wall abutment and azimuthal planes for wing-wall and semicircular abutments. The velocity components were also measured at different horizontal planes. In the upstream, presentations of flow field through vector plots at vertical / azimuthal and horizontal planes show the existence of a primary vortex associated with the downflow inside the scour hole. On the other hand, in the downstream, the flow field shows a reversed flow near the abutments having a subsequent recovery with a passage of flow as a part of the main flow. The data presented in this p

关键词： abutments Three-dimensional flow Vortex flow Open channel flow Scour Sediment transport Fluvial hydraulics

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M5 Model Tree to Predict Temporal Evolution of Clear-Water Abutment Scour

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Open Journal of Geology 2016年第9期6卷 1045-1054页

作者： R. Biabani M. Meftah Halaghi Kh. Ghorbani Department of Water Engineering Faculty of Agriculture Tabriz University Tabriz Iran Department of Water Engineering Gorgan University of Agricultural Sciences and Natural Resources Gorgan Iran

Scour is a natural phenomenon that is created by the rivers streams or the flood which brings about transferring or eroding of bed materials. To have accurate and safe erosion control structures design, maximum scour depth in downstream of the structures gains specific significance. In the current study, M5 model tree as remedy data mining approaches is suggested to estimate the scour depth around the abutments. To do this, Kayaturk laboratory data (2005), with different hydraulic conditions, are used. Then, the results of M5 model were also compared with genetic programming (GP) and pervious empirical results to investigate the applicability, ability, and accuracy of these procedures. To examine the accuracy of the results yielded from the M5 and GP procedures, two performance indicators (determination coefficient (R2) and root mean square error (RMSE)) were used. The comparison test of results clearly shows that the implementation of M5 technique sounds satisfactory regarding the performance indicators (R2 = 0.944 and RMSE = 0.126) with less deviation from the numerical values. In addition, M5 tree model, by presenting relationships based on liner regression, has good capability to estimate the depth of scour abutment for engineers in practical terms.

关键词： abutments Scour Depth M5 Model Tree Genetic Programming Model (GP)

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