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Prediction of hydro-suction dredging depth using data-driven methods

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 3,   Pages 652-664 doi: 10.1007/s11709-021-0719-7

Abstract: In this study, data-driven methods (DDMs) including different kinds of group method of data handlingAlso, 33 data samples from three previous studies were used.Data-driven simulation results indicated that the HGSO algorithm accurately trains the GMDH methods better

Keywords： sedimentation     water resources     dam engineering     machine learning     heuristic    

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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 improved

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

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A hierarchical system to predict behavior of soil and cantilever sheet wall by data-driven models

Nang Duc BUI; Hieu Chi PHAN; Tiep Duc PHAM; Ashutosh Sutra DHAR

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 6,   Pages 667-684 doi: 10.1007/s11709-022-0822-4

Abstract: The study proposes a framework combining machine learning (ML) models into a logical hierarchical systemThe uncertainty of this data-driven system is partially investigated by developing 1000 RFC models, basedon the application of random sampling technique in the data splitting process.Investigation on the distribution of the evaluation metrics reveals negative skewed data toward the 1.0000

Keywords： finite element analysis     cantilever sheet wall     machine learning     artificial neural network     random forest    

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Hybrid Data-Driven and Mechanistic Modeling Approaches for Multiscale Material and Process Design Perspective

Teng Zhou, Rafiqul Gani, Kai Sundmacher

Engineering 2021, Volume 7, Issue 9,   Pages 1231-1238 doi: 10.1016/j.eng.2020.12.022

Abstract: modeling, the material properties, which are computationally expensive to obtain, are described by data-driven

Keywords： Data-driven     Surrogate model     Machine learning     Hybrid modeling     Material design     Process optimization    

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Data-driven soft sensors in blast furnace ironmaking: a survey Review Article

Yueyang LUO, Xinmin ZHANG, Manabu KANO, Long DENG, Chunjie YANG, Zhihuan SONG

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 3,   Pages 327-354 doi: 10.1631/FITEE.2200366

Abstract: With the advancement of the Internet of Things, big data, and artificial intelligence, data-driven inThis review covers the state-of-the-art studies of data-driven technologies in the .Specifically, we first conduct a comprehensive overview of various data-driven soft sensor modeling methodsSecond, the important applications of data-driven in ironmaking (silicon content, molten iron temperatureFinally, the potential challenges and future development trends of data-driven in ironmaking applications

Keywords： Soft sensors     Data-driven modeling     Machine learning     Deep learning     Blast furnace     Ironmaking process    

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Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of MachineLearning for Materials Design Review

Teng Zhou, Zhen Song, Kai Sundmacher

Engineering 2019, Volume 5, Issue 6,   Pages 1017-1026 doi: 10.1016/j.eng.2019.02.011

Abstract:

Materials development has historically been driven by human needs and desires, and this is likelyAs big data generated by modern experimental and computational techniques is becoming more readily available, data-driven or machine learning (ML) methods have opened new paradigms for the discovery and rational

Keywords： Big data     Data-driven     Machine learning     Materials screening     Materials design    

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Physics-Informed Deep Learning-Based Real-Time Structural Response Prediction Method

Ying Zhou,Shiqiao Meng,Yujie Lou,Qingzhao Kong,

Engineering doi: 10.1016/j.eng.2023.08.011

Abstract: and efficiency of structural response prediction, this study proposes a novel physics-informed deep-learning-basedstructural response prediction method that can predict a large number of nodes in a structure through a data-driven

Keywords： seismic response prediction     Physics information informed     Real-time prediction     Earthquake engineering     Data-drivenmachine learning    

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Data-driven distribution network topology identification considering correlated generation power of distributed

Frontiers in Energy 2022, Volume 16, Issue 1,   Pages 121-129 doi: 10.1007/s11708-021-0780-x

Abstract: This paper proposes a data-driven topology identification method for distribution systems with distributed

Keywords： power distribution network     data-driven     topology identification     distributed energy resource     maximal    

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Data-driven approach to solve vertical drain under time-dependent loading

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 3,   Pages 696-711 doi: 10.1007/s11709-021-0727-7

Abstract: Currently, the vertical drain consolidation problem is solved by numerous analytical solutions, such as time-dependent solutions and linear or parabolic radial drainage in the smear zone, and no artificial intelligence (AI) approach has been applied. Thus, in this study, a new hybrid model based on deep neural networks (DNNs), particle swarm optimization (PSO), and genetic algorithms (GAs) is proposed to solve this problem. The DNN can effectively simulate any sophisticated equation, and the PSO and GA can optimize the selected DNN and improve the performance of the prediction model. In the present study, analytical solutions to vertical drains in the literature are incorporated into the DNN–PSO and DNN–GA prediction models with three different radial drainage patterns in the smear zone under time-dependent loading. The verification performed with analytical solutions and measurements from three full-scale embankment tests revealed promising applications of the proposed approach.

Keywords： vertical drain     artificial neural network     time-dependent loading     deep learning network     genetic algorithm    

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Multiple input self-organizing-map ResNet model for optimization of petroleum refinery conversion units

Frontiers of Chemical Science and Engineering 2023, Volume 17, Issue 6,   Pages 759-771 doi: 10.1007/s11705-022-2269-5

Abstract: This work introduces a deep-learning network, i.e., multi-input self-organizing-map ResNet (MISR), forThe self-organizing-map part maps the input data into multiple two-dimensional planes and sends them

Keywords： hydrocracking     convolutional neural networks     self-organizing map     deep learning     data-driven optimization    

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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: components are smart sensing, mobile communication, Internet of Things, modelling and simulation, advanced dataprocessing, storage and analysis, advanced process control, artificial intelligence and machine learningessential element to this transformation is the exploitation of large amounts of historical process dataand large volumes of data generated in real-time by smart sensors widely used in industry.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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Hybrid-driven Gaussian process online learning for highly maneuvering multi-target tracking Research Article

Qiang GUO, Long TENG, Tianxiang YIN, Yunfei GUO, Xinliang WU, Wenming SONG

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 11,   Pages 1647-1656 doi: 10.1631/FITEE.2300348

Abstract: This paper proposes a hybrid-driven approach for tracking multiple highly maneuvering targets, leveragingThrough the simulations, it has been demonstrated that the hybrid-driven approach exhibits significant

Keywords： Target tracking     Gaussian process     Data-driven     Online learning     Model-driven     Probabilistic data association    

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Concrete corrosion in wastewater systems: Prediction and sensitivity analysis using advanced extreme learningmachine

Mohammad ZOUNEMAT-KERMANI, Meysam ALIZAMIR, Zaher Mundher YASEEN, Reinhard HINKELMANN

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 2,   Pages 444-460 doi: 10.1007/s11709-021-0697-9

Abstract: The implementation of novel machine learning models can contribute remarkably to simulating the degradationlearning models.learning models.For the first assessment, the machine learning models were developed using all the available cement andThe online sequential extreme learning machine model demonstrated superior performance over the other

Keywords： sewer systems     environmental engineering     data-driven methods     sensitivity analysis    

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Optimal Antibody Purification Strategies Using Data-Driven Models Article

Songsong Liu, Lazaros G. Papageorgiou

Engineering 2019, Volume 5, Issue 6,   Pages 1077-1092 doi: 10.1016/j.eng.2019.10.011

Abstract: Data-driven models of chromatography throughput are developed considering loaded mass, flow velocity,height as the inputs, using manufacturing-scale simulated datasets based on microscale experimental datato minimize the total cost of goods per gram of the antibody purification process, incorporating the data-driven

Keywords： Antibody purification     Multiscale optimization     Antigen-binding fragment     Mixed-integer programming     Data-driven    

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A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 2, doi: 10.1007/s11783-023-1622-3

Abstract:

● A novel deep learning framework for short-term water demand forecasting

Keywords： Long-short term memory neural network     Convolutional Neural Network     Wavelet multi-resolution analysis     Data-driven    

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Title Author Date Type Operation

Prediction of hydro-suction dredging depth using data-driven methods

Journal Article

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

Li Sun, Fengqi You

Journal Article

A hierarchical system to predict behavior of soil and cantilever sheet wall by data-driven models

Nang Duc BUI; Hieu Chi PHAN; Tiep Duc PHAM; Ashutosh Sutra DHAR

Journal Article

Hybrid Data-Driven and Mechanistic Modeling Approaches for Multiscale Material and Process Design

Teng Zhou, Rafiqul Gani, Kai Sundmacher

Journal Article

Data-driven soft sensors in blast furnace ironmaking: a survey

Yueyang LUO, Xinmin ZHANG, Manabu KANO, Long DENG, Chunjie YANG, Zhihuan SONG

Journal Article

Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of MachineLearning for Materials Design

Teng Zhou, Zhen Song, Kai Sundmacher

Journal Article

Physics-Informed Deep Learning-Based Real-Time Structural Response Prediction Method

Ying Zhou,Shiqiao Meng,Yujie Lou,Qingzhao Kong,

Journal Article

Data-driven distribution network topology identification considering correlated generation power of distributed

Journal Article

Data-driven approach to solve vertical drain under time-dependent loading

Journal Article

Multiple input self-organizing-map ResNet model for optimization of petroleum refinery conversion units

Journal Article

Big data and machine learning: A roadmap towards smart plants

Journal Article

Hybrid-driven Gaussian process online learning for highly maneuvering multi-target tracking

Qiang GUO, Long TENG, Tianxiang YIN, Yunfei GUO, Xinliang WU, Wenming SONG

Journal Article

Concrete corrosion in wastewater systems: Prediction and sensitivity analysis using advanced extreme learningmachine

Mohammad ZOUNEMAT-KERMANI, Meysam ALIZAMIR, Zaher Mundher YASEEN, Reinhard HINKELMANN

Journal Article

Optimal Antibody Purification Strategies Using Data-Driven Models

Songsong Liu, Lazaros G. Papageorgiou

Journal Article

A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

Journal Article