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Realtime prediction of hard rock TBM advance rate using temporal convolutional network (TCN) with tunnel

Zaobao LIU; Yongchen WANG; Long LI; Xingli FANG; Junze WANG

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 4,   Pages 401-413 doi: 10.1007/s11709-022-0823-3

Abstract: Real-time dynamic adjustment of the tunnel bore machine (TBM) advance rate according to the rock-machineThis paper proposes a real-time predictive model of TBM advance rate using the temporal convolutionalThe prediction model was built using an experimental database, containing 235 data sets, establishedadvance rate of the next moment.The work provides a new concept of real-time prediction of the TBM performance for highly efficient tunnel

Keywords： hard rock tunnel     tunnel bore machine advance rate prediction     temporal convolutional networks     soft    

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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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Liquefaction prediction using support vector machine model based on cone penetration data

Pijush SAMUI

Frontiers of Structural and Civil Engineering 2013, Volume 7, Issue 1,   Pages 72-82 doi: 10.1007/s11709-013-0185-y

Abstract: A support vector machine (SVM) model has been developed for the prediction of liquefaction susceptibilityThis paper examines the potential of SVM model in prediction of liquefaction using actual field coneThe SVM, a novel learning machine based on statistical theory, uses structural risk minimization (SRMUsing cone resistance ( ) and cyclic stress ratio ( ), model has been developed for prediction of liquefactionThe study shows that SVM can be used as a practical tool for prediction of liquefaction potential, based

Keywords： earthquake     cone penetration test     liquefaction     support vector machine (SVM)     prediction    

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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 theDifferent ML methods for the factor of safety (FOS) prediction are studied and compared hoping to make

Keywords： slope stability     factor of safety     regression     machine learning     repeated cross-validation    

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Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 4,   Pages 523-535 doi: 10.1007/s11705-021-2083-5

Abstract: Solubility prediction, as an alternative to experiments which can reduce waste and improve crystallizationmodels by machine learning algorithms.The solubility data of 120 active pharmaceutical ingredients (APIs) in ethanol were considered in the predictionFurthermore, a comparison with traditional prediction methods including the modified solubility equationThe highest accuracy shown by the testing set proves that the ML models have the best solubility prediction

Keywords： solubility prediction     machine learning     artificial neural network     random decision forests    

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Short-term prediction of influent flow rate and ammonia concentration in municipal wastewater treatment

Shuai MA, Siyu ZENG, Xin DONG, Jining CHEN, Gustaf OLSSON

Frontiers of Environmental Science & Engineering 2014, Volume 8, Issue 1,   Pages 128-136 doi: 10.1007/s11783-013-0598-9

Abstract: The prediction of the influent load is of great importance for the improvement of the control systemreconstruction; 2) typical cycle identification using power spectrum density analysis; 3) fitting and predictionpresent an obvious periodicity, the decreasing of prediction accuracy is not distinct with increasingof the prediction time scales; 3) the periodicity influence is larger than rainfalls; 4) the rainfallswill make the periodicity of flow rate less obvious in intensive rainy periods.

Keywords： influent load prediction     wavelet de-noising     power spectrum density     autoregressive model     time-frequency    

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Prediction of shield tunneling-induced ground settlement using machine learning techniques

Renpeng CHEN, Pin ZHANG, Huaina WU, Zhiteng WANG, Zhiquan ZHONG

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 6,   Pages 1363-1378 doi: 10.1007/s11709-019-0561-3

Abstract: This study investigates the efficiency and feasibility of six machine learning (ML) algorithms, namelyneural network, wavelet neural network, general regression neural network (GRNN), extreme learning machine, support vector machine and random forest (RF), to predict tunneling-induced settlement.

Keywords： EPB shield     shield tunneling     settlement prediction     machine learning    

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Machine learning enabled prediction and process optimization of VFA production from riboflavin-mediated

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 11, doi: 10.1007/s11783-023-1735-8

Abstract:

● Data-driven approach was used to simulate VFA production from WAS fermentation.

Keywords： Machine learning     Volatile fatty acids     Riboflavin     Waste activated sludge     eXtreme Gradient Boosting    

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Shear stress distribution prediction in symmetric compound channels using data mining and machine learning

Zohreh SHEIKH KHOZANI, Khabat KHOSRAVI, Mohammadamin TORABI, Amir MOSAVI, Bahram REZAEI, Timon RABCZUK

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 5,   Pages 1097-1109 doi: 10.1007/s11709-020-0634-3

Abstract: Shear stress distribution prediction in open channels is of utmost importance in hydraulic structuralA set of, data mining and machine learning algorithms including Random Forest (RF), M5P, Random CommitteeThe results showed that the RF model has the best prediction performance compared to SKM and Shannon

Keywords： compound channel     machine learning     SKM model     shear stress distribution     data mining models    

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An energy consumption prediction approach of die casting machines driven by product parameters

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 4,   Pages 868-886 doi: 10.1007/s11465-021-0656-0

Abstract: The energy consumption prediction of die casting machines can support energy consumption quota, processTo fill this gap, this paper proposes an energy consumption prediction approach for die casting machinesFirstly, the system boundary of energy consumption prediction is defined, and subsequently, based onConsequently, a systematic energy consumption prediction approach for die casting machines, involvingThe results show that the prediction accuracy of production time and energy consumption reached 91.64%

Keywords： die casting machine     energy consumption prediction     product parameters    

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Understanding the demand predictability of bike share systems: A station-level analysis

Frontiers of Engineering Management   Pages 551-565 doi: 10.1007/s42524-023-0279-8

Abstract: While researchers have mainly focused on improving prediction accuracy and analysing demand-influencingAdditionally, to verify that these predictability measures could represent the performance of predictionmodels, we implemented two commonly used demand prediction models to compare the empirical predictionFindings from this study provide more fundamental understanding of BSS demand prediction, which can helpdecision makers and system operators anticipate diverse station-level prediction errors from their prediction

Keywords： bike share systems     demand prediction     prediction errors     machine learning     entropy    

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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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Modeling, simulation, and prediction of global energy indices: a differential approach

Stephen Ndubuisi NNAMCHI, Onyinyechi Adanma NNAMCHI, Janice Desire BUSINGYE, Maxwell Azubuike IJOMAH, Philip Ikechi OBASI

Frontiers in Energy 2022, Volume 16, Issue 2,   Pages 375-392 doi: 10.1007/s11708-021-0723-6

Abstract: engineering, analysis, and prediction of energy indices.state-of-the-art of the research includes normalization of energy indices, generation of differential rateterms, and regression of rate terms against energy indices to generate coefficients and unexplainedThe exact solutions are ideal for interpolative prediction of historic data.Moreover, the sensitivity of the differential rate terms was instrumental in discovering the overwhelming

Keywords： energy indices     differential model     normalization     simulation     inflation/deflation     predictive factor and predictionrate    

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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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Advance in Structural Steel

Weng Yuqing

Strategic Study of CAE 2002, Volume 4, Issue 3,   Pages 48-53

Abstract:

Advance on “Ultra Steel”，which is characterized by fine grained microstructure，is described

Keywords： iron and steel materials     basic research     performance improvement     advance    

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Title Author Date Type Operation

Realtime prediction of hard rock TBM advance rate using temporal convolutional network (TCN) with tunnel

Zaobao LIU; Yongchen WANG; Long LI; Xingli FANG; Junze WANG

Journal Article

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

Journal Article

Liquefaction prediction using support vector machine model based on cone penetration data

Pijush SAMUI

Journal Article

Evaluation and prediction of slope stability using machine learning approaches

Journal Article

Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients

Journal Article

Short-term prediction of influent flow rate and ammonia concentration in municipal wastewater treatment

Shuai MA, Siyu ZENG, Xin DONG, Jining CHEN, Gustaf OLSSON

Journal Article

Prediction of shield tunneling-induced ground settlement using machine learning techniques

Renpeng CHEN, Pin ZHANG, Huaina WU, Zhiteng WANG, Zhiquan ZHONG

Journal Article

Machine learning enabled prediction and process optimization of VFA production from riboflavin-mediated

Journal Article

Shear stress distribution prediction in symmetric compound channels using data mining and machine learning

Zohreh SHEIKH KHOZANI, Khabat KHOSRAVI, Mohammadamin TORABI, Amir MOSAVI, Bahram REZAEI, Timon RABCZUK

Journal Article

An energy consumption prediction approach of die casting machines driven by product parameters

Journal Article

Understanding the demand predictability of bike share systems: A station-level analysis

Journal Article

Development of machine learning multi-city model for municipal solid waste generation prediction

Journal Article

Modeling, simulation, and prediction of global energy indices: a differential approach

Stephen Ndubuisi NNAMCHI, Onyinyechi Adanma NNAMCHI, Janice Desire BUSINGYE, Maxwell Azubuike IJOMAH, Philip Ikechi OBASI

Journal Article

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

Journal Article

Advance in Structural Steel

Weng Yuqing

Journal Article