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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 wereResults suggest improved performance by RF regression for both pile groups.Model developed using RF regression approach with smooth pile group data was found to be in good agreement

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

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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: With this study, along with the conventional methods of simple and multiple linear regression models,three machine learning algorithms, random forest, gradient boosting and stacked models, are developed

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

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

Hai-Bang LY; Huong-Lan Thi VU; Lanh Si HO; Binh Thai PHAM

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 2, 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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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 mouseWe also compare the RF model with the baseline models (logistic regression and support vector machine

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

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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: In this study, three machine learning methods entitled Gaussian process regression, M5P model tree, andrandom forest 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： transportation infrastructure flexible pavement structural number prediction Gaussian process regression M5P model tree random forest

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of catalyst temperature in automotive engines over coldstart operation in the presence of different randomnoises and uncertainty: Implementation of generalized Gaussian process regression machine

Nasser L. AZAD,Ahmad MOZAFFARI

Frontiers of Mechanical Engineering 2015, Volume 10, Issue 4, Pages 405-412 doi: 10.1007/s11465-015-0354-x

Abstract: current study is to develop a systematic stochastic model to capture the undesired uncertainty and randomthe modeling and analysis of automotive engines’ behavior during coldstart operations by using regressionThis encourages automotive engineers to take advantage of knowledge-based modeling tools and regressionHere, the authors introduce a random noise to experimentally derived data and simulate an uncertain databaseThen, by using a Gaussian process regression machine (GPRM), a reliable model is used for the sake of

Keywords： automotive engine calibration coldstart operation Gaussian process regression machine (GPRM) uncertaintyand random noises

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

Van Quan TRAN; Van Loi GIAP; Dinh Phien VU; Riya Catherine GEORGE; Lanh Si HO

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 9, 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

Nang Duc BUI; Hieu Chi PHAN; Tiep Duc PHAM; Ashutosh Sutra DHAR

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 6, 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 wall(artificial neural network regression models, RANN1) and 2) vertical settlement of soil (RANN2).data-driven system is partially investigated by developing 1000 RFC models, based on the application of randomThe regression ANN models have coefficient of determinations on test set, as high as 0.9521 for RANN1

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

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

Zhao Xianwen

Strategic Study of CAE 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 CAE 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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Title Author Date Type Operation

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

Tanvi SINGH, Mahesh PAL, V. K. ARORA

Journal Article

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

Walid Khalid MBARAK, Esma Nur CINICIOGLU, Ozer CINICIOGLU

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

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

Hai-Bang LY; Huong-Lan Thi VU; Lanh Si HO; Binh Thai PHAM

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

Estimation of flexible pavement structural capacity using machine learning techniques

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

Journal Article

of catalyst temperature in automotive engines over coldstart operation in the presence of different randomnoises and uncertainty: Implementation of generalized Gaussian process regression machine

Nasser L. AZAD,Ahmad MOZAFFARI

Journal Article

Progress of forest certification in China

Wenming LU, Maharaj MUTHOO

Journal Article

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

Van Quan TRAN; Van Loi GIAP; Dinh Phien VU; Riya Catherine GEORGE; Lanh Si HO

Journal Article

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

Nang Duc BUI; Hieu Chi PHAN; Tiep Duc PHAM; Ashutosh Sutra DHAR

Journal Article

Data-Driven Model Falsification and Uncertainty Quantification for Fractured Reservoirs