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Indeterminacy Causal Inductive Automatic Reasoning Mechanism Based On Fuzzy State Describing

Yang Bingru,Tang Jing

Strategic Study of CAE 2000, Volume 2, Issue 5,   Pages 44-50

Abstract:

New framework of knowledge representation of fuzzy language field and fuzzy language value structuresynthetically process fuzzy indeterminacy and random indeterminacy and the generalized inductive logic causalOn this basis, the new logic indeterminate causal inductive automatic reasoning mechanism which is based

Keywords： language field     language value structure     generalized cell automation     generalized inductive logic causal    

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Machine learning-based seismic assessment of framed structures with soil-structure interaction

Frontiers of Structural and Civil Engineering 2023, Volume 17, Issue 2,   Pages 205-223 doi: 10.1007/s11709-022-0909-y

Abstract: objective of the current study is to propose an expert system framework based on a supervised machine learningtechnique (MLT) to predict the seismic performance of low- to mid-rise frame structures considering soil-structureThe proposed framework is novel because it enables the designer to seismically assess the structure,

Keywords： seismic hazard     artificial neural network     soil-structure interaction     seismic analysis    

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Causal Inference Review

Kun Kuang, Lian Li, Zhi Geng, Lei Xu, Kun Zhang, Beishui Liao, Huaxin Huang, Peng Ding, Wang Miao, Zhichao Jiang

Engineering 2020, Volume 6, Issue 3,   Pages 253-263 doi: 10.1016/j.eng.2019.08.016

Abstract: machine learning to become explainable.How to marry causal inference with machine learning to develop eXplainable Artificial Intelligence (XAIWith the aim of bringing knowledge of causal inference to scholars of machine learning
and artificialaspects of causal inference.Zhi Geng, "Causal potential theory" from Prof.

Keywords： Causal inference     Instructive variables     Negative control     Causal reasoning and explanation     Causal discovery    

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Damage assessment and diagnosis of hydraulic concrete structures using optimization-based machine learning

Frontiers of Structural and Civil Engineering   Pages 1281-1294 doi: 10.1007/s11709-023-0975-9

Abstract: Changes in the concrete structure will result in changes in parameters such as the frequency mode and

Keywords： hydraulic structure     curvature mode     damage detection     artifical neural network     artificial bee colony    

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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 displacement

Keywords： machine learning     gridshell structure     regression     sensitivity analysis     interpretability methods    

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Face recognition based on subset selection via metric learning on manifold

Hong SHAO,Shuang CHEN,Jie-yi ZHAO,Wen-cheng CUI,Tian-shu YU

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 12,   Pages 1046-1058 doi: 10.1631/FITEE.1500085

Abstract: In this paper, we employ a metric learning approach which helps find the active elements correctly bytaking into account the interclass/intraclass relationship and manifold structure of face images.

Keywords： Face recognition     Sparse representation     Manifold structure     Metric learning     Subset selection    

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Deep convolutional neural network for multi-level non-invasive tunnel lining assessment

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 2,   Pages 214-223 doi: 10.1007/s11709-021-0800-2

Abstract: In a supervised learning framework, Ground Penetrating Radar (GPR) profiles and the revealed structural

Keywords： concrete structure     GPR     damage classification     convolutional neural network     transfer learning    

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Dynamic relationship analysis among parties of the low-carbon building

Liu Hongyong,Zheng Junwei,Lin Cheng

Strategic Study of CAE 2012, Volume 14, Issue 12,   Pages 94-99

Abstract: and public, puts forward to the diagram of the relationships among the stakeholders, and designs the causal

Keywords： low-carbon building     stakeholders     causal relation diagram     dynamic analysis    

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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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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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Intelligent Monitoring System Based on Spatio–Temporal Data for Underground Space Infrastructure Review

Bowen Du, Junchen Ye, Hehua Zhu, Leilei Sun, Yanliang Du

Engineering 2023, Volume 25, Issue 6,   Pages 194-203 doi: 10.1016/j.eng.2022.07.016

Abstract:

Intelligent sensing, mechanism understanding, and the deterioration forecasting based on spatio–temporal big data not only promote the safety of the infrastructure but also indicate the basic theory and key technology for the infrastructure construction to turn to intelligentization. The advancement of underground space utilization has led to the development of three characteristics (deep, big, and clustered) that help shape a tridimensional urban layout. However, compared to buildings and bridges overground, the diseases and degradation that occur underground are more insidious and difficult to identify. Numerous challenges during the construction and service periods remain. To address this gap, this paper summarizes the existing methods and evaluates their strong points and weak points based on real-world space safety management. The key scientific issues, as well as solutions, are discussed in a unified intelligent monitoring system.

Keywords： Structure health monitoring     Underground space infrastructure     Machine learning     Spatio–temporal data    

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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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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 machine learning

Keywords： machine learning     flowsheet simulations     constraints     exploration    

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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 demonstratedThis paper primarily aims to review the research and application of machine learning in the field ofFirst, the structure and function of the HST traction system are briefly introduced.Then, the research and application of machine learning in traction system fault diagnosis are comprehensivelydiagnosis under actual operating conditions are revealed, and the future research trends of machine learning

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

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Title Author Date Type Operation

Indeterminacy Causal Inductive Automatic Reasoning Mechanism Based On Fuzzy State Describing

Yang Bingru,Tang Jing

Journal Article

Machine learning-based seismic assessment of framed structures with soil-structure interaction

Journal Article

Causal Inference

Kun Kuang, Lian Li, Zhi Geng, Lei Xu, Kun Zhang, Beishui Liao, Huaxin Huang, Peng Ding, Wang Miao, Zhichao Jiang

Journal Article

Damage assessment and diagnosis of hydraulic concrete structures using optimization-based 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

Face recognition based on subset selection via metric learning on manifold

Hong SHAO,Shuang CHEN,Jie-yi ZHAO,Wen-cheng CUI,Tian-shu YU

Journal Article

Deep convolutional neural network for multi-level non-invasive tunnel lining assessment

Journal Article

Dynamic relationship analysis among parties of the low-carbon building

Liu Hongyong,Zheng Junwei,Lin Cheng

Journal Article

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

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

Intelligent Monitoring System Based on Spatio–Temporal Data for Underground Space Infrastructure

Bowen Du, Junchen Ye, Hehua Zhu, Leilei Sun, Yanliang Du

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

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

Journal Article