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Data driven models for compressive strength prediction of concrete at high temperatures

Mahmood AKBARI, Vahid JAFARI DELIGANI

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 2,   Pages 311-321 doi: 10.1007/s11709-019-0593-8

Abstract: The use of data driven models has been shown to be useful for simulating complex engineering processes, when the only information available consists of the data of the process.In this study, four data-driven models, namely multiple linear regression, artificial neural network,driven models to predict the compressive strength at high temperature.driven models to make satisfactory results.

Keywords： data driven model     compressive strength     concrete     high temperature    

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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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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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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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Data-Driven Anomaly Diagnosis for Machining Processes Article

Y.C. Liang, S. Wang, W.D. Li, X. Lu

Engineering 2019, Volume 5, Issue 4,   Pages 646-652 doi: 10.1016/j.eng.2019.03.012

Abstract: To address this issue, this paper presents a novel data-driven diagnosis system for anomalies.In this system, power data for condition monitoring are continuously collected during dynamic machininganalysis, preprocessing mechanisms have been designed to denoise, normalize, and align the monitored dataImportant features are extracted from the monitored data and thresholds are defined to identify anomaliesBased on historical data, the values of thresholds are optimized using a fruit fly optimization (FFO)

Keywords： Computer numerical control machining     Anomaly detection     Fruit fly optimization algorithm     Data-driven    

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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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An adaptive data-driven method for accurate prediction of remaining useful life of rolling bearings

Yanfeng PENG, Junsheng CHENG, Yanfei LIU, Xuejun LI, Zhihua PENG

Frontiers of Mechanical Engineering 2018, Volume 13, Issue 2,   Pages 301-310 doi: 10.1007/s11465-017-0449-7

Abstract:

A novel data-driven method based on Gaussian mixture model (GMM) and distance evaluation techniqueThe data sets are clustered by GMM to divide all data sets into several health states adaptively andThus, either the health state of the data sets or the number of the states is obtained automatically.training data sets.sets into several health states and remove the abnormal data sets.

Keywords： Gaussian mixture model     distance evaluation technique     health state     remaining useful life     rolling bearing    

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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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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 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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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     particle swarm optimization    

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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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Decomposition and decoupling analysis of electricity consumption carbon emissions in China

Frontiers of Engineering Management   Pages 486-498 doi: 10.1007/s42524-022-0215-3

Abstract: Electricity consumption is one of the major contributors to greenhouse gas emissions. In this study, we build a power consumption carbon emission measurement model based on the operating margin factor. We use the decomposition and decoupling technology of logarithmic mean Divisia index method to quantify six effects (emission intensity, power generation structure, consumption electricity intensity, economic scale, population structure, and population scale) and comprehensively reflect the degree of dependence of electricity consumption carbon emissions on China’s economic development and population changes. Moreover, we utilize the decoupling model to analyze the decoupling state between carbon emissions and economic growth and identify corresponding energy efficiency policies. The results of this study provide a new perspective to understand carbon emission reduction potentials in the electricity use of China.

Keywords： electricity consumption carbon emission measurement     LMDI model     decoupling model     data driven    

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On the Data-Driven Materials Innovation Infrastructure

Hong Wang, X.-D. Xiang, Lanting Zhang

Engineering 2020, Volume 6, Issue 6,   Pages 609-611 doi: 10.1016/j.eng.2020.04.004

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Dynamic response surface methodology using Lasso regression for organic pharmaceutical synthesis

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2,   Pages 221-236 doi: 10.1007/s11705-021-2061-y

Abstract: To study the dynamic behavior of a process, time-resolved data are collected at different time instantsFor utilizing such time-resolved data to model the dynamic behavior, dynamic response surface methodology(DRSM), a data-driven modeling method, has been proposed.Therefore, DRSM with Lasso regression can provide faster and more accurate data-driven models for a variety

Keywords： data-driven modeling     pharmaceutical organic synthesis     Lasso regression     dynamic response surface methodology    

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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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Title Author Date Type Operation

Data driven models for compressive strength prediction of concrete at high temperatures

Mahmood AKBARI, Vahid JAFARI DELIGANI

Journal Article

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

Journal Article

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

Journal Article

Optimal Antibody Purification Strategies Using Data-Driven Models

Songsong Liu, Lazaros G. Papageorgiou

Journal Article

Data-Driven Anomaly Diagnosis for Machining Processes

Y.C. Liang, S. Wang, W.D. Li, X. Lu

Journal Article

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

Li Sun, Fengqi You

Journal Article

An adaptive data-driven method for accurate prediction of remaining useful life of rolling bearings

Yanfeng PENG, Junsheng CHENG, Yanfei LIU, Xuejun LI, Zhihua PENG

Journal Article

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

Teng Zhou, Rafiqul Gani, Kai Sundmacher

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

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

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

Decomposition and decoupling analysis of electricity consumption carbon emissions in China

Journal Article

On the Data-Driven Materials Innovation Infrastructure

Hong Wang, X.-D. Xiang, Lanting Zhang

Journal Article

Dynamic response surface methodology using Lasso regression for organic pharmaceutical synthesis

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