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Dimensionality reduction and prediction of soil consolidation coefficient using random forest coupling

Frontiers of Structural and Civil Engineering   Pages 224-238 doi: 10.1007/s11709-022-0812-6

Abstract: In this study, we developed a hybrid model of Random Forest coupling with a Relief algorithm (RF-RL)

Keywords： soil consolidation coefficient     machine learning     random forest     Relief    

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Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition

Diego CABRERA,Fernando SANCHO,René-Vinicio SÁNCHEZ,Grover ZURITA,Mariela CERRADA,Chuan LI,Rafael E. VÁSQUEZ

Frontiers of Mechanical Engineering 2015, Volume 10, Issue 3,   Pages 277-286 doi: 10.1007/s11465-015-0348-8

Abstract:

This paper addresses the development of a random forest classifier for the multi-class fault diagnosisthrough the parameters’ space to find the best values for the number of trees and the number of randomthe application is identified and the best features are selected through the internal ranking of the randomforest classifier.

Keywords： fault diagnosis     spur gearbox     wavelet packet decomposition     random forest    

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Modeling oblique load carrying capacity of batter pile groups using neural network, random forest regression

Tanvi SINGH, Mahesh PAL, V. K. ARORA

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 3,   Pages 674-685 doi: 10.1007/s11709-018-0505-3

Abstract: M5 model tree, random forest regression (RF) and neural network (NN) based modelling approaches were

Keywords： batter piles     oblique load test     neural network     M5 model tree     random forest regression     ANOVA    

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Application of machine learning technique for predicting and evaluating chloride ingress in concrete

Frontiers of Structural and Civil Engineering   Pages 1153-1169 doi: 10.1007/s11709-022-0830-4

Abstract: concrete using three hybrid models of gradient boosting (GB), artificial neural network (ANN), and randomforest (RF) in combination with particle swarm optimization (PSO).

Keywords： gradient boosting     random forest     chloride content     concrete     sensitivity analysis.    

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A hierarchical system to predict behavior of soil and cantilever sheet wall by data-driven models

Frontiers of Structural and Civil Engineering   Pages 667-684 doi: 10.1007/s11709-022-0822-4

Abstract: Consequently, a system containing three trained ML models is proposed to first predict the stability status (randomforest classification, RFC) followed by 1) the cantilever top horizontal displacement of sheet walldata-driven system is partially investigated by developing 1000 RFC models, based on the application of random

Keywords： finite element analysis     cantilever sheet wall     machine learning     artificial neural network     random forest    

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Man-machine verification of mouse trajectory based on the random forestmodel Research Articles

Zhen-yi XU, Yu KANG, Yang CAO, Yu-xiao YANG

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 7,   Pages 925-929 doi: 10.1631/FITEE.1700442

Abstract: In this study, we propose a random forest (RF) model for man-machine verification based on the mouse

Keywords： Man-machine verification     Random forest     Support vector machine     Logistic regression     Performance metrics    

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Data-Driven Model Falsification and Uncertainty Quantification for Fractured Reservoirs Article

Junling Fang,Bin Gong,Jef Caers

Engineering 2022, Volume 18, Issue 11,   Pages 116-128 doi: 10.1016/j.eng.2022.04.015

Abstract:

Many properties of natural fractures are uncertain, such as their spatial distribution, petrophysical properties, and fluid flow performance. Bayesian theorem provides a framework to quantify the uncertainty in geological modeling and flow simulation, and hence to support reservoir performance predictions. The application of Bayesian methods to fractured reservoirs has mostly been limited to synthetic cases. In field applications, however, one of the main problems is that the Bayesian prior is falsified, because it fails to predict past reservoir production data. In this paper, we show how a global sensitivity analysis (GSA) can be used to identify why the prior is falsified. We then employ an approximate Bayesian computation (ABC) method combined with a tree-based surrogate model to match the production history. We apply these two approaches to a complex fractured oil and gas reservoir where all uncertainties are jointly considered, including the petrophysical properties, rock physics properties, fluid properties, discrete fracture parameters, and dynamics of pressure and transmissibility. We successfully identify several reasons for the falsification. The results show that the methods we propose are effective in quantifying uncertainty in the modeling and flow simulation of a fractured reservoir. The uncertainties of key parameters, such as fracture aperture and fault conductivity, are reduced.

Keywords： Bayesian evidential learning     Falsification     Fractured reservoir     Random forest     Approximate Bayesian computation    

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SPT based determination of undrained shear strength: Regression models and machine learning

Walid Khalid MBARAK, Esma Nur CINICIOGLU, Ozer CINICIOGLU

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 1,   Pages 185-198 doi: 10.1007/s11709-019-0591-x

Abstract: conventional methods of simple and multiple linear regression models, three machine learning algorithms, randomforest, gradient boosting and stacked models, are developed for prediction of undrained shear strength

Keywords： undrained shear strength     linear regression     random forest     gradient boosting     machine learning     standard    

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Estimation of flexible pavement structural capacity using machine learning techniques

Nader KARBALLAEEZADEH, Hosein GHASEMZADEH TEHRANI, Danial MOHAMMADZADEH SHADMEHRI, Shahaboddin SHAMSHIRBAND

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 5,   Pages 1083-1096 doi: 10.1007/s11709-020-0654-z

Abstract: this study, three machine learning methods entitled Gaussian process regression, M5P model tree, and randomforest used for the prediction of structural numbers in flexible pavements.Among the methods employed in this paper, random forest is the most accurate as it yields the best values

Keywords： infrastructure     flexible pavement     structural number prediction     Gaussian process regression     M5P model tree     randomforest    

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Progress of forest certification in China

Wenming LU, Maharaj MUTHOO

Frontiers of Agricultural Science and Engineering 2017, Volume 4, Issue 4,   Pages 414-420 doi: 10.15302/J-FASE-2017185

Abstract: forest management.Program has resulted in little demand for forest certification of natural forest management units.to promote sustainable forest management and how the concept of forest certification can be used toIn general, forest certification in China has a clear role in sustainable forest management, both fortimber and non-timber forest products.

Keywords： China Forest Certification Scheme     forest certification     government support     opportunities and challenges     sustainable forest management    

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A New Way to Study the Three Essential Factors of Forest Fire

Zhao Xianwen

Strategic Study of Chinese Academy of Engineering 2000, Volume 2, Issue 5,   Pages 66-71

Abstract:

This thesis took the three essential factors of forest fire (fire source, environment, and litter)as the point of departure,and has approached the forecast method of forest fire in tropical area ofFor example,in the aspect of forest fire forecast, the main cause of forest fire was artificial fire,So Markov random processes could be employed in the study.In the aspect of the analysis of environment that contributes to forest fire,correlation would reveal

Keywords： forecast of forest fire     space remote sensing     essential factors of forest fire     a new way    

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New Trend of Forest Chemical Industry in China

Song Zhanqian

Strategic Study of Chinese Academy of Engineering 2001, Volume 3, Issue 2,   Pages 1-6

Abstract:

Forest chemical industry can produce various useful products by chemical processing of forest resources.It is one of the important fields of effective and sustainable utilization of forest resources.The present conditiobns of the forest chemical industry in China was reviewed in this paper.

Keywords： forest chemical industry     forest resource     chemical processing    

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Cattle manure biochar and earthworm interactively affected CO and NO emissions in agricultural and forest

Frontiers of Environmental Science & Engineering 2022, Volume 16, Issue 3, doi: 10.1007/s11783-021-1473-8

Abstract: ItemContent>

• Earthworms increase CO2 and N2O emissions in agricultural and forest

Keywords： Carbon sequestration     Forest soil     Cattle manure biochar     Greenhouse gas emissions     Soil fauna    

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Floating forest: A novel breakwater-windbreak structure against wind and wave hazards

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 5,   Pages 1111-1127 doi: 10.1007/s11709-021-0757-1

Abstract: A novel floating breakwater-windbreak structure (floating forest) has been designed for the protectionAn array of tubular columns on the sloping deck of the breakwater act as an artificial forest-type windbreakThe study shows that the proposed 1 km long floating forest is able to shelter a lee area that stretches

Keywords： floating structure     breakwater     windbreak     hydrodynamic     CFD    

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Simulation of heterogeneous two-phase media using random fields and level sets

George STEFANOU

Frontiers of Structural and Civil Engineering 2015, Volume 9, Issue 2,   Pages 114-120 doi: 10.1007/s11709-014-0267-5

Abstract: The accurate and efficient simulation of random heterogeneous media is important in the framework ofIt is usually assumed that the morphology of a random microstructure can be described as a non-Gaussianrandom field that is completely defined by its multivariate distribution.A particular kind of non-Gaussian random fields with great practical importance is that of translationmedia with emphasis on level-cut random fields which are a special case of translation fields.

Keywords： microstructure     random fields     level sets     shape recovery     two-phase media    

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Title Author Date Type Operation

Dimensionality reduction and prediction of soil consolidation coefficient using random forest coupling

Journal Article

Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition

Diego CABRERA,Fernando SANCHO,René-Vinicio SÁNCHEZ,Grover ZURITA,Mariela CERRADA,Chuan LI,Rafael E. VÁSQUEZ

Journal Article

Modeling oblique load carrying capacity of batter pile groups using neural network, random forest regression

Tanvi SINGH, Mahesh PAL, V. K. ARORA

Journal Article

Application of machine learning technique for predicting and evaluating chloride ingress in concrete

Journal Article

A hierarchical system to predict behavior of soil and cantilever sheet wall by data-driven models

Journal Article

Man-machine verification of mouse trajectory based on the random forestmodel

Zhen-yi XU, Yu KANG, Yang CAO, Yu-xiao YANG

Journal Article

Data-Driven Model Falsification and Uncertainty Quantification for Fractured Reservoirs

Junling Fang,Bin Gong,Jef Caers

Journal Article

SPT based determination of undrained shear strength: Regression models and machine learning

Walid Khalid MBARAK, Esma Nur CINICIOGLU, Ozer CINICIOGLU

Journal Article

Estimation of flexible pavement structural capacity using machine learning techniques

Nader KARBALLAEEZADEH, Hosein GHASEMZADEH TEHRANI, Danial MOHAMMADZADEH SHADMEHRI, Shahaboddin SHAMSHIRBAND

Journal Article

Progress of forest certification in China

Wenming LU, Maharaj MUTHOO

Journal Article

A New Way to Study the Three Essential Factors of Forest Fire

Zhao Xianwen

Journal Article

New Trend of Forest Chemical Industry in China

Song Zhanqian

Journal Article

Cattle manure biochar and earthworm interactively affected CO and NO emissions in agricultural and forest

Journal Article

Floating forest: A novel breakwater-windbreak structure against wind and wave hazards

Journal Article

Simulation of heterogeneous two-phase media using random fields and level sets

George STEFANOU

Journal Article