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Assessment of different machine learning techniques in predicting the compressive strength of self-compacting

Van Quan TRAN; Hai-Van Thi MAI; Thuy-Anh NGUYEN; Hai-Bang LY

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 7,   Pages 928-945 doi: 10.1007/s11709-022-0837-x

Abstract: compressive strength of SCC (CS of SCC) can be successfully predicted from mix design and curing age by a machinelearning (ML) technique named the Extreme Gradient Boosting (XGB) algorithm, including non-hybrid andNine ML techniques, such as Linear regression (LR), K-Nearest Neighbors (KNN), Support Vector MachineMoreover, the hybrid models of eight ML techniques and Particle Swarm Optimization (PSO) are constructedThe highest number of SCC samples available in the literature is collected for building the ML techniques

Keywords： compressive strength     self-compacting concrete     machine learning techniques     particle swarm optimization    

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Machine Learning and Data-Driven Techniques for the Control of Smart Power Generation Systems: An Uncertainty Review

Li Sun, Fengqi You

Engineering 2021, Volume 7, Issue 9,   Pages 1239-1247 doi: 10.1016/j.eng.2021.04.020

Abstract: The burgeoning era of machine learning (ML) and data-driven control (DDC) techniques promises an improvedA holistic view is provided on the control techniques of smart power generation, from the regulationThe benefits of ML and DDC techniques are accordingly interpreted in terms of visibility, maneuverability

Keywords： Smart power generation     Machine learning     Data-driven control     Systems engineering    

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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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Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 11, doi: 10.1007/s11783-023-1738-5

Abstract:

● A novel integrated machine learning method to analyze O3

Keywords： Ozone     Integrated method     Machine learning    

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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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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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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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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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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 learningand artificial intelligence technologies integrated with more traditional modelling techniques.

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

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The State-of-the-Art Review on Applications of Intrusive Sensing, Image Processing Techniques, and MachineLearning Methods in Pavement Monitoring and Analysis Review

Yue Hou, Qiuhan Li, Chen Zhang, Guoyang Lu, Zhoujing Ye, Yihan Chen, Linbing Wang, Dandan Cao

Engineering 2021, Volume 7, Issue 6,   Pages 845-856 doi: 10.1016/j.eng.2020.07.030

Abstract: technologies can be employed for the collection and analysis of such data, including various intrusive sensing techniques, image processing techniques, and machine learning methods.

Keywords： Pavement monitoring and analysis     The state-of-the-art review     Intrusive sensing     Image processing techniques     Machine learning methods    

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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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Predicting the elemental compositions of solid waste using ATR-FTIR and machine learning

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 10, doi: 10.1007/s11783-023-1721-1

Abstract:

● A method based on ATR-FTIR and ML was developed to predict CHNS contents in waste.

Keywords： Elemental composition     Infrared spectroscopy     Machine learning     Moisture interference     Solid waste     Spectral    

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Machine learning modeling identifies hypertrophic cardiomyopathy subtypes with genetic signature

Frontiers of Medicine 2023, Volume 17, Issue 4,   Pages 768-780 doi: 10.1007/s11684-023-0982-1

Abstract: method for illustrating the relationship between the phenotype and genotype of each HCM subtype by using machinelearning modeling and interactome network detection techniques based on whole-exome sequencing data.Machine learning modeling based on personal whole-exome data identified 46 genes with mutation burden

Keywords： machine learning methods     hypertrophic cardiomyopathy     genetic risk    

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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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Title Author Date Type Operation

Assessment of different machine learning techniques in predicting the compressive strength of self-compacting

Van Quan TRAN; Hai-Van Thi MAI; Thuy-Anh NGUYEN; Hai-Bang LY

Journal Article

Machine Learning and Data-Driven Techniques for the Control of Smart Power Generation Systems: An Uncertainty

Li Sun, Fengqi You

Journal Article

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

Journal Article

Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method

Journal Article

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

Journal Article

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

Journal Article

Evaluation and prediction of slope stability using machine learning approaches

Journal Article

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

Journal Article

Big data and machine learning: A roadmap towards smart plants

Journal Article

The State-of-the-Art Review on Applications of Intrusive Sensing, Image Processing Techniques, and MachineLearning Methods in Pavement Monitoring and Analysis

Yue Hou, Qiuhan Li, Chen Zhang, Guoyang Lu, Zhoujing Ye, Yihan Chen, Linbing Wang, Dandan Cao

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

Predicting the elemental compositions of solid waste using ATR-FTIR and machine learning

Journal Article

Machine learning modeling identifies hypertrophic cardiomyopathy subtypes with genetic signature

Journal Article

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

Journal Article