Search scope:
排序: Display mode:
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
● A novel integrated machine learning method to analyze O3
Keywords: Ozone Integrated method Machine learning
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
● 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
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
Keywords: building energy management machine learning integrated framework knowledge evolution
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
Keywords: slope stability factor of safety regression machine learning repeated cross-validation
Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2, Pages 183-197 doi: 10.1007/s11705-021-2073-7
Keywords: machine learning flowsheet simulations constraints exploration
Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 12, doi: 10.1007/s11783-023-1752-7
● Online learning models accurately predict influent flow rate at
Keywords: Wastewater prediction Data stream Online learning Batch learning Influent flow rates
Big data and machine learning: A roadmap towards smart plants
Frontiers of Engineering Management Pages 623-639 doi: 10.1007/s42524-022-0218-0
Keywords: big data machine learning artificial intelligence smart sensor cyber–physical system Industry 4.0
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
● A database of municipal solid waste (MSW) generation in China was established.
Keywords: Municipal solid waste Machine learning Multi-cities Gradient boost regression tree
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
● 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
Machine learning for fault diagnosis of high-speed train traction systems: A review
Frontiers of Engineering Management doi: 10.1007/s42524-023-0256-2
Keywords: high-speed train traction systems machine learning fault diagnosis
Frontiers of Environmental Science & Engineering 2022, Volume 16, Issue 3, doi: 10.1007/s11783-021-1472-9
• A spectral machine learning approach is proposed for predicting mixed
Keywords: Antibiotic contamination Spectral detection Machine learning
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
Keywords: optimization metaheuristic algorithms harmony search structural designs machine learning artificial
Houfa Wu,Jianyun Zhang,Zhenxin Bao,Guoqing Wang,Wensheng Wang,Yanqing Yang,Jie Wang
Engineering doi: 10.1016/j.eng.2021.12.014
Keywords: Parameters estimation Ungauged catchments Regionalization scheme Machine learning algorithms Soil and
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
Keywords: support vector machine deep convolutional neural network microscope digital image curing period
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
Keywords: machine learning gridshell structure regression sensitivity analysis interpretability methods
Title Author Date Type Operation
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
Machine learning in building energy management: A critical review and future directions
Journal Article
Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet
Journal Article
Online machine learning for stream wastewater influent flow rate prediction under unprecedented emergencies
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
A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning
Journal Article
Runoff Modeling in Ungauged Catchments Using Machine Learning Algorithm-Based Model Parameters Regionalization
Houfa Wu,Jianyun Zhang,Zhenxin Bao,Guoqing Wang,Wensheng Wang,Yanqing Yang,Jie Wang
Journal Article
Assessing compressive strengths of mortar and concrete from digital images by machine learning techniques
Amit SHIULY; Debabrata DUTTA; Achintya MONDAL
Journal Article