Resource Type

Journal Article 1015

Conference Videos 8

Year

2023 69

2022 86

2021 73

2020 75

2019 52

2018 43

2017 47

2016 44

2015 54

2014 44

2013 41

2012 40

2011 34

2010 47

2009 46

2008 40

2007 38

2006 27

2005 26

2004 20

open ︾

Keywords

model 15

model test 15

numerical simulation 15

mathematical model 14

regression analysis 12

kinetic model 10

uncertainty 9

COVID-19 7

sensitivity analysis 7

Deep learning 6

optimization 6

China 5

constitutive model 5

Machine learning 4

artificial neural network 4

development model 4

machine learning 4

numerical model 4

regression 4

open ︾

Search scope:

排序： Display mode:

Multivariable regression model for Fox depth correction factor

Ravi Kant MITTAL, Sanket RAWAT, Piyush BANSAL

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 1,   Pages 103-109 doi: 10.1007/s11709-018-0474-6

Abstract: Therefore, this paper presents a non-linear regression model for the analysis of effect of embedment

Keywords： settlement     embedment     Fox depth correction factor     regression     multivariable    

HTML PDF Collect

Multiple regression models for energy consumption of office buildings in different climates in China

Siyu ZHOU, Neng ZHU

Frontiers in Energy 2013, Volume 7, Issue 1,   Pages 103-110 doi: 10.1007/s11708-012-0220-z

Abstract: Then on the basis of the simulated results, the multiple regression models were developed respectivelyAccording to the analysis of regression coefficients, the appropriate building envelope design schemesAt last, the regression model evaluations consisting of the simulation evaluations and the actual caseevaluations were performed to verify the feasibility and accuracy of the regression models.It is believed that the regression models developed in this paper can be used to estimate the energy

Keywords： regression model     energy consumption     building envelope     office building     different climates    

HTML PDF Collect

Modeling oblique load carrying capacity of batter pile groups using neural network, random forest regressionand M5 model tree

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.M5 model tree provides simple linear relation which can be used for the prediction of oblique load forModel 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    

HTML PDF Collect

Presentation of regression analysis, GP and GMDH models to predict the pedestrian density in various

Iraj BARGEGOL; Seyed Mohsen HOSSEINIAN; Vahid NAJAFI MOGHADDAM GILANI; Mohammad NIKOOKAR; Alireza OROUEI

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 2,   Pages 250-265 doi: 10.1007/s11709-021-0785-x

Abstract: Regression analysis was then applied to determine the relationship between SMS, flow rate, andBy the use of regression analysis, the mathematical relationships between variables in all facilitiesprediction of pedestrian density in terms of flow rate and SMS, suggesting that GP was better able to model

Keywords： pedestrian density     regression analysis     GP model     GMDH model    

HTML PDF Collect

Development of machine learning multi-city model for municipal solid waste generation prediction

Frontiers of Environmental Science & Engineering 2022, Volume 16, Issue 9, doi: 10.1007/s11783-022-1551-6

Abstract:

● A database of municipal solid waste (MSW) generation in China was established.

Keywords： Municipal solid waste     Machine learning     Multi-cities     Gradient boost regression tree    

HTML PDF Collect

Prediction of cutting force in turning of UD-GFRP using mathematical model and simulated annealing

Meenu GUPTA, Surinder Kumar GILL

Frontiers of Mechanical Engineering 2012, Volume 7, Issue 4,   Pages 417-426 doi: 10.1007/s11465-012-0343-2

Abstract: The present investigation deals with the study and development of a cutting force prediction model forthe machining of unidirectional glass fiber reinforced plastics (UD-GFRP) composite using regressionThe predicted values radial cutting force model is compared with the experimental values.

Keywords： UD-GFRP     ANOVA     radial cutting force     PCD tool     Taguchi method     regression analysis     simulated annealing     multi    

HTML PDF Collect

Dynamic response surface methodology using Lasso regression for organic pharmaceutical synthesis

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2,   Pages 221-236 doi: 10.1007/s11705-021-2061-y

Abstract: For utilizing such time-resolved data to model the dynamic behavior, dynamic response surface methodologyTwo approaches can be adopted in the estimation of the model parameters: stepwise regression, used inseveral of previous publications, and Lasso regression, which is newly incorporated in this paper forTherefore, DRSM with Lasso regression can provide faster and more accurate data-driven models for a variety

Keywords： data-driven modeling     pharmaceutical organic synthesis     Lasso regression     dynamic response surface methodology    

HTML PDF Collect

Interaction behavior and load sharing pattern of piled raft using nonlinear regression and LM algorithm-based

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 5,   Pages 1181-1198 doi: 10.1007/s11709-021-0744-6

Abstract: The obtained results are then checked statistically with nonlinear multiple regression (NMR) and artificial

Keywords： interaction     load sharing ratio     piled raft     nonlinear regression     artificial neural network    

HTML PDF Collect

Water quality prediction of copper-molybdenum mining-beneficiation wastewater based on the PSO-SVR model

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 8, doi: 10.1007/s11783-023-1698-9

Abstract:

● Data acquisition and pre-processing for wastewater treatment were summarized.

Keywords： oxygen demand     Mining-beneficiation wastewater treatment     Particle swarm optimization     Support vector regression    

HTML PDF Collect

An innovative model for predicting the displacement and rotation of column-tree moment connection under

Mohammad Ali NAGHSH, Aydin SHISHEGARAN, Behnam KARAMI, Timon RABCZUK, Arshia SHISHEGARAN, Hamed TAGHAVIZADEH, Mehdi MORADI

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 1,   Pages 194-212 doi: 10.1007/s11709-020-0688-2

Abstract: These surrogate models include a multiple linear regression (MLR), multiple Ln equation regression (MLnER

Keywords： column-tree moment connection     Finite element model     parametric study     fire     regression models     gene expression    

HTML PDF Collect

of driver-response relationships: identifying factors using a novel framework integrating quantile regression

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 6, doi: 10.1007/s11783-023-1676-2

Abstract:

● A novel framework integrating quantile regression with machine learning

Keywords： Driver-response     Upper boundary of relationship     Interpretable machine learning     Quantile regression    

HTML PDF Collect

compressive strength of soil-RAP blend stabilized with Portland cement using multivariate adaptive regression

Ali Reza GHANIZADEH, Morteza RAHROVAN

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 4,   Pages 787-799 doi: 10.1007/s11709-019-0516-8

Abstract: This paper aims to develop a mathematical model for predicting the unconfined compressive strength (UCS) of soil-RAP blend stabilized with Portland cement based on multivariate adaptive regression splineFor training and testing of MARS model, total of 64 experimental UCS data were employed.Predictors or independent variables in the developed model are percentage of RAP, percentage of cementSensitivity analysis of the proposed model showed that the cement, optimum moisture content, and percent

Keywords： soil-reclaimed asphalt pavement blend     Portland cement     unconfined compressive strength     multivariate adaptive regression    

HTML PDF Collect

Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach

Pijush Samui, Jagan J

Frontiers of Structural and Civil Engineering 2013, Volume 7, Issue 2,   Pages 133-136 doi: 10.1007/s11709-013-0202-1

Abstract: This article examines the capability of Gaussian process regression (GPR) for prediction of effectiveThe results show that the developed GPR is reliable model for prediction of of unsaturated soil.

Keywords： unsaturated soil     effective stress parameter     Gaussian process regression (GPR)     artificial neural network    

HTML PDF Collect

Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive

Faezehossadat KHADEMI,Mahmoud AKBARI,Sayed Mohammadmehdi JAMAL,Mehdi NIKOO

Frontiers of Structural and Civil Engineering 2017, Volume 11, Issue 1,   Pages 90-99 doi: 10.1007/s11709-016-0363-9

Abstract: In this paper, considering the experimental results, three different models of multiple linear regressionmodel (MLR), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) areevaluate the compressive strength of concrete with different mix designs, however, multiple linear regressionmodel is not feasible enough in this area because of nonlinear relationship between the concrete mix

Keywords： concrete     28 days compressive strength     multiple linear regression     artificial neural network     ANFIS     sensitivity    

HTML PDF Collect

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,

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

HTML PDF Collect

Title Author Date Type Operation

Multivariable regression model for Fox depth correction factor

Ravi Kant MITTAL, Sanket RAWAT, Piyush BANSAL

Journal Article

Multiple regression models for energy consumption of office buildings in different climates in China

Siyu ZHOU, Neng ZHU

Journal Article

Modeling oblique load carrying capacity of batter pile groups using neural network, random forest regressionand M5 model tree

Tanvi SINGH, Mahesh PAL, V. K. ARORA

Journal Article

Presentation of regression analysis, GP and GMDH models to predict the pedestrian density in various

Iraj BARGEGOL; Seyed Mohsen HOSSEINIAN; Vahid NAJAFI MOGHADDAM GILANI; Mohammad NIKOOKAR; Alireza OROUEI

Journal Article

Development of machine learning multi-city model for municipal solid waste generation prediction

Journal Article

Prediction of cutting force in turning of UD-GFRP using mathematical model and simulated annealing

Meenu GUPTA, Surinder Kumar GILL

Journal Article

Dynamic response surface methodology using Lasso regression for organic pharmaceutical synthesis

Journal Article

Interaction behavior and load sharing pattern of piled raft using nonlinear regression and LM algorithm-based

Journal Article

Water quality prediction of copper-molybdenum mining-beneficiation wastewater based on the PSO-SVR model

Journal Article

An innovative model for predicting the displacement and rotation of column-tree moment connection under

Mohammad Ali NAGHSH, Aydin SHISHEGARAN, Behnam KARAMI, Timon RABCZUK, Arshia SHISHEGARAN, Hamed TAGHAVIZADEH, Mehdi MORADI

Journal Article

of driver-response relationships: identifying factors using a novel framework integrating quantile regression

Journal Article

compressive strength of soil-RAP blend stabilized with Portland cement using multivariate adaptive regression

Ali Reza GHANIZADEH, Morteza RAHROVAN

Journal Article

Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach

Pijush Samui, Jagan J

Journal Article

Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive

Faezehossadat KHADEMI,Mahmoud AKBARI,Sayed Mohammadmehdi JAMAL,Mehdi NIKOO

Journal Article

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

Walid Khalid MBARAK, Esma Nur CINICIOGLU, Ozer CINICIOGLU

Journal Article