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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    

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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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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: this study, we carried out nonlinear finite element simulations to predict the performance of a column-treeThese 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    

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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    

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Stochastic extra-gradient based alternating direction methods for graph-guided regularizedminimization None

Qiang LAN, Lin-bo QIAO, Yi-jie WANG

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 6,   Pages 755-762 doi: 10.1631/FITEE.1601771

Abstract: In this study, we propose and compare stochastic variants of the extra-gradient alternating directionmethod, named the stochastic extra-gradient alternating direction method with Lagrangian function (SEGL) and the stochastic extra-gradient alternating direction method with augmented Lagrangian function (, logistic regression, Lasso, structured extensions of Lasso, and structured regularized logistic regressionWe conduct experiments on fused logistic regression and graph-guided regularized regression.

Keywords： Stochastic optimization     Graph-guided minimization     Extra-gradient method     Fused logistic regression     Graph-guidedregularized logistic regression    

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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, and

Keywords： transportation infrastructure     flexible pavement     structural number prediction     Gaussian process regression     M5P model tree     random forest    

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Kd-tree and quad-tree decompositions for declustering of 2D range queries over uncertain space

Ahmet SAYAR,Süleyman EKEN,Okan ÖZTÜRK

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 2,   Pages 98-108 doi: 10.1631/FITEE.1400165

Abstract: We present a study to show the possibility of using two well-known space partitioning and indexing techniques, kd trees and quad trees, in declustering applications to increase input/output (I/O) parallelization and reduce spatial data processing times. This parallelization enables time-consuming computational geometry algorithms to be applied efficiently to big spatial data rendering and querying. The key challenge is how to balance the spatial processing load across a large number of worker nodes, given significant performance heterogeneity in nodes and processing skews in the workload.

Keywords： Kd tree     Quad tree     Space partitioning     Spatial indexing     Range queries     Query optimization    

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Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 4,   Pages 814-828 doi: 10.1007/s11465-021-0650-6

Abstract: To address this issue, this paper explores a decision-tree-structured neural network, that is, the deepconvolutional tree-inspired network (DCTN), for the hierarchical fault diagnosis of bearings.proposed model effectively integrates the advantages of convolutional neural network (CNN) and decision treeoutput decision layer of CNN according to the hierarchical structural characteristics of the decision tree

Keywords： cross-severity fault diagnosis     hierarchical fault diagnosis     convolutional neural network     decision tree    

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prediction and optimization design of sustainable concrete based on squirrel search algorithm-extreme gradient

Frontiers of Structural and Civil Engineering   Pages 1310-1325 doi: 10.1007/s11709-023-0997-3

Abstract: traditional compressive strength test, this study combines five novel metaheuristic algorithms with extreme gradientThe results indicated that the squirrel search algorithm-extreme gradient boosting (SSA-XGB) yielded

Keywords： sustainable concrete     fly ash     slay     extreme gradient boosting technique     squirrel search algorithm    

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Vibration analysis of nano-structure multilayered graphene sheets using modified strain gradient theory

Amir ALLAHBAKHSHI,Masih ALLAHBAKHSHI

Frontiers of Mechanical Engineering 2015, Volume 10, Issue 2,   Pages 187-197 doi: 10.1007/s11465-015-0339-9

Abstract:

In this paper, for the first time, the modified strain gradient theory is used as a new size-dependentAfter obtaining the governing equations based on modified strain gradient theory via principle of minimum

Keywords： graphene     van der Waals (vdW) force     modi- fied strain gradient elasticity theory     size effect parameter    

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The strategy on the Tree Paeony ) Oil Industry in China

Li Yucai

Strategic Study of CAE 2014, Volume 16, Issue 10,   Pages 58-63

Abstract:

the seed of tree paeony native to china could be squeezed oil with highIt is very important to develop the tree paeony oil industry for the safety of Chinese food oil production

Keywords： Oil tree paeony     Tree oil plant     Engineering     Strategics    

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Velocity gradient elasticity for nonlinear vibration of carbon nanotube resonators

Hamid M. SEDIGHI, Hassen M. OUAKAD

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 6,   Pages 1520-1530 doi: 10.1007/s11709-020-0672-x

Abstract: undertake two models to capture the nanostructure nonlocal size effects: the strain and the velocity gradientThe structural nonlinear behavior of the system assuming both strain and velocity gradient theories is

Keywords： velocity gradient elasticity theory     nanotube resonators     differential-quadrature method     nonlinear vibration    

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Real-time simulation platform for photovoltaic system with a boost converter using MPPT algorithm in

Geethanjali PURUSHOTHAMAN, Vimisha VENUGOPALAN, Aleena Mariya VINCENT

Frontiers in Energy 2013, Volume 7, Issue 3,   Pages 373-379 doi: 10.1007/s11708-013-0272-8

Abstract: In this paper, a real-time simulation of a PV system with a boost converter was proposed using only theA MATLAB/Simulink environment was used to develop the real-time PV system with a boost converter intoFurther, the performance of the PV with a boost converter was tested at different temperatures and irradiations

Keywords： photovoltaic (PV) module     digital signal processor (DSP) controller     power electronic converter     real-time simulation    

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Water quality soft-sensor prediction in anaerobic process using deep neural network optimized by Tree-structured

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 6, doi: 10.1007/s11783-023-1667-3

Abstract:

● Hybrid deep-learning model is proposed for water quality prediction.

Keywords： Water quality prediction     Soft-sensor     Anaerobic process     Tree-structured Parzen Estimator    

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Rational design on photoelectrodes and devices to boost photoelectrochemical performance of solar-driven

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 6,   Pages 777-798 doi: 10.1007/s11705-022-2148-0

Abstract: As an eco-friendly, efficient, and low-cost technique, photoelectrochemical water splitting has attracted growing interest in the production of clean and sustainable hydrogen by the conversion of abundant solar energy. In the photoelectrochemical system, the photoelectrode plays a vital role in absorbing the energy of sunlight to trigger the water splitting process and the overall efficiency depends largely on the integration and design of photoelectrochemical devices. In recent years, the optimization of photoelectrodes and photoelectrochemical devices to achieve highly efficient hydrogen production has been extensively investigated. In this paper, a concise review of recent advances in the modification of nanostructured photoelectrodes and the design of photoelectrochemical devices is presented. Meanwhile, the general principles of structural and morphological factors in altering the photoelectrochemical performance of photoelectrodes are discussed. Furthermore, the performance indicators and first principles to describe the behaviors of charge carriers are analyzed, which will be of profound guiding significance to increasing the overall efficiency of the photoelectrochemical water splitting system. Finally, current challenges and prospects for an in-depth understanding of reaction mechanisms using advanced characterization technologies and potential strategies for developing novel photoelectrodes and advanced photoelectrochemical water splitting devices are demonstrated.

Keywords： photoelectrochemical water splitting     photoelectrodes     hydrogen production     charge separation     catalytic mechanism    

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Title Author Date Type Operation

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

Journal Article

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

Walid Khalid MBARAK, Esma Nur CINICIOGLU, Ozer CINICIOGLU

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

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

Stochastic extra-gradient based alternating direction methods for graph-guided regularizedminimization

Qiang LAN, Lin-bo QIAO, Yi-jie WANG

Journal Article

Estimation of flexible pavement structural capacity using machine learning techniques

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

Journal Article

Kd-tree and quad-tree decompositions for declustering of 2D range queries over uncertain space

Ahmet SAYAR,Süleyman EKEN,Okan ÖZTÜRK

Journal Article

Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical

Journal Article

prediction and optimization design of sustainable concrete based on squirrel search algorithm-extreme gradient

Journal Article

Vibration analysis of nano-structure multilayered graphene sheets using modified strain gradient theory

Amir ALLAHBAKHSHI,Masih ALLAHBAKHSHI

Journal Article

The strategy on the Tree Paeony ) Oil Industry in China

Li Yucai

Journal Article

Velocity gradient elasticity for nonlinear vibration of carbon nanotube resonators

Hamid M. SEDIGHI, Hassen M. OUAKAD

Journal Article

Real-time simulation platform for photovoltaic system with a boost converter using MPPT algorithm in

Geethanjali PURUSHOTHAMAN, Vimisha VENUGOPALAN, Aleena Mariya VINCENT

Journal Article

Water quality soft-sensor prediction in anaerobic process using deep neural network optimized by Tree-structured

Journal Article

Rational design on photoelectrodes and devices to boost photoelectrochemical performance of solar-driven

Journal Article