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Spatial prediction of soil contamination based on machine learning: a review

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 8, doi: 10.1007/s11783-023-1693-1

Abstract:

● A review of machine learning (ML) for spatial prediction of soil

Keywords： Soil contamination     Machine learning     Prediction     Spatial distribution    

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Application of machine learning models in groundwater quality assessment and prediction: progress and

Frontiers of Environmental Science & Engineering 2022, Volume 18, Issue 3, doi: 10.1007/s11783-024-1789-2

Abstract:

● The application of ML in groundwater quality assessment and prediction is reviewed.

Keywords： Groundwater quality assessment     Groundwater quality prediction     Machine learning     Groundwater modeling    

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State-of-the-art applications of machine learning in the life cycle of solid waste management

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 4, doi: 10.1007/s11783-023-1644-x

Abstract:

● State-of-the-art applications of machine learning (ML) in solid waste

Keywords： Machine learning (ML)     Solid waste (SW)     Bibliometrics     SW management     Energy utilization     Life cycle    

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Evaluation and prediction of slope stability using machine learning approaches

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 4,   Pages 821-833 doi: 10.1007/s11709-021-0742-8

Abstract: In this paper, the machine learning (ML) model is built for slope stability evaluation and meets the

Keywords： slope stability     factor of safety     regression     machine learning     repeated cross-validation    

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Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2,   Pages 183-197 doi: 10.1007/s11705-021-2073-7

Abstract: exploration of the design variable space for such scenarios, an adaptive sampling technique based on machinelearning models has recently been proposed.

Keywords： machine learning     flowsheet simulations     constraints     exploration    

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Big data and machine learning: A roadmap towards smart plants

Frontiers of Engineering Management   Pages 623-639 doi: 10.1007/s42524-022-0218-0

Abstract: advanced data processing, storage and analysis, advanced process control, artificial intelligence and machinelearning, cloud computing, and virtual and augmented reality.Exploitation of the information contained in these data requires the use of advanced machine learning

Keywords： big data     machine learning     artificial intelligence     smart sensor     cyber–physical system     Industry 4.0    

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Machine learning in building energy management: A critical review and future directions

Frontiers of Engineering Management 2022, Volume 9, Issue 2,   Pages 239-256 doi: 10.1007/s42524-021-0181-1

Abstract: Over the past two decades, machine learning (ML) has elicited increasing attention in building energy

Keywords： building energy management     machine learning     integrated framework     knowledge evolution    

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MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 6, doi: 10.1007/s11783-023-1677-1

Abstract:

● MSWNet was proposed to classify municipal solid waste.

Keywords： Municipal solid waste sorting     Deep residual network     Transfer learning     Cyclic learning rate     Visualization    

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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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Development of gradient boosting-assisted machine learning data-driven model for free chlorine residual

Frontiers of Environmental Science & Engineering 2023, Volume 18, Issue 2, doi: 10.1007/s11783-024-1777-6

Abstract:

● A machine learning approach was applied to predict free chlorine

Keywords： Machine learning     Data-driven modeling     Drinking water treatment     Disinfection     Chlorination    

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Machine learning for fault diagnosis of high-speed train traction systems: A review

Frontiers of Engineering Management doi: 10.1007/s42524-023-0256-2

Abstract: In recent years, machine learning has been widely used in various pattern recognition tasks and has demonstratedMachine learning has made considerably advancements in traction system fault diagnosis; however, a comprehensiveThis paper primarily aims to review the research and application of machine learning in the field ofThen, the research and application of machine learning in traction system fault diagnosis are comprehensivelylearning in traction systems are discussed.

Keywords： high-speed train     traction systems     machine learning     fault diagnosis    

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A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning

Frontiers of Environmental Science & Engineering 2022, Volume 16, Issue 3, doi: 10.1007/s11783-021-1472-9

Abstract:

• A spectral machine learning approach is proposed for predicting mixed

Keywords： Antibiotic contamination     Spectral detection     Machine learning    

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Estimation of optimum design of structural systems via machine learning

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 6,   Pages 1441-1452 doi: 10.1007/s11709-021-0774-0

Abstract: Three different structural engineering designs were investigated to determine optimum design variables, and then to estimate design parameters and the main objective function of designs directly, speedily, and effectively. Two different optimization operations were carried out: One used the harmony search (HS) algorithm, combining different ranges of both HS parameters and iteration with population numbers. The other used an estimation application that was done via artificial neural networks (ANN) to find out the estimated values of parameters. To explore the estimation success of ANN models, different test cases were proposed for the three structural designs. Outcomes of the study suggest that ANN estimation for structures is an effective, successful, and speedy tool to forecast and determine the real optimum results for any design model.

Keywords： optimization     metaheuristic algorithms     harmony search     structural designs     machine learning     artificial    

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Structural performance assessment of GFRP elastic gridshells by machine learning interpretability methods

Soheila KOOKALANI; Bin CHENG; Jose Luis Chavez TORRES

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 10,   Pages 1249-1266 doi: 10.1007/s11709-022-0858-5

Abstract: Machine learning (ML) approaches are implemented in this study, to predict maximum stress and displacementAdaBoost), extreme gradient boosting (XGBoost), category boosting (CatBoost), and light gradient boosting machine

Keywords： machine learning     gridshell structure     regression     sensitivity analysis     interpretability methods    

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Assessing compressive strengths of mortar and concrete from digital images by machine learning techniques

Amit SHIULY; Debabrata DUTTA; Achintya MONDAL

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 3,   Pages 347-358 doi: 10.1007/s11709-022-0819-z

Abstract: In the present study, a new image-based machine learning method is used to predict concrete compressiveThese include support-vector machine model and various deep convolutional neural network models, namelyThe images and corresponding compressive strength were then used to train machine learning models toOverall, the present findings validated the use of machine learning models as an efficient means of estimating

Keywords： support vector machine     deep convolutional neural network     microscope     digital image     curing period    

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Title Author Date Type Operation

Spatial prediction of soil contamination based on machine learning: a review

Journal Article

Application of machine learning models in groundwater quality assessment and prediction: progress and

Journal Article

State-of-the-art applications of machine learning in the life cycle of solid waste management

Journal Article

Evaluation and prediction of slope stability using machine learning approaches

Journal Article

Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet

Journal Article

Big data and machine learning: A roadmap towards smart plants

Journal Article

Machine learning in building energy management: A critical review and future directions

Journal Article

MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal

Journal Article

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

Journal Article

Development of gradient boosting-assisted machine learning data-driven model for free chlorine residual

Journal Article

Machine learning for fault diagnosis of high-speed train traction systems: A review

Journal Article

A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning

Journal Article

Estimation of optimum design of structural systems via machine learning

Journal Article

Structural performance assessment of GFRP elastic gridshells by machine learning interpretability methods

Soheila KOOKALANI; Bin CHENG; Jose Luis Chavez TORRES

Journal Article

Assessing compressive strengths of mortar and concrete from digital images by machine learning techniques

Amit SHIULY; Debabrata DUTTA; Achintya MONDAL

Journal Article