Resource Type

Journal Article 714

Conference Videos 18

Year

2024 1

2023 60

2022 76

2021 51

2020 40

2019 37

2018 40

2017 46

2016 32

2015 29

2014 22

2013 38

2012 21

2011 24

2010 30

2009 57

2008 21

2007 22

2006 17

2005 11

open ︾

Keywords

Machine learning 50

machine learning 24

prediction 20

Artificial intelligence 9

artificial neural network 9

tunnel 9

Deep learning 8

artificial intelligence 6

shield tunnel 6

compressive strength 5

neural network 5

Support vector machine 4

construction 4

earthquake prediction 4

life prediction 4

simulation 4

subsea tunnel 4

tunnel construction 4

ANOVA 3

open ︾

Search scope:

排序： Display mode:

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    

HTML PDF Collect

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    

HTML PDF Collect

Effect of cutterhead configuration on tunnel face stability during shield machine maintenance outages

Frontiers of Structural and Civil Engineering 2023, Volume 17, Issue 4,   Pages 522-532 doi: 10.1007/s11709-023-0930-9

Abstract: Engineering safety during maintenance outages is determined by the stability of the tunnel face.The tunnel face is supported by a medium at the bottom of the excavation chamber and compressed air atDisregarding the supporting effect of the cutterhead will result in a tunnel face with underestimated

Keywords： tunnel face stability     cutterhead configuration     aperture ratio     pressure gradient     support ratio    

HTML PDF Collect

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    

HTML PDF Collect

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    

HTML PDF Collect

Clogging of slurry-shield tunnel-boring machine drives in sedimentary soft rock: A case study

Frontiers of Structural and Civil Engineering doi: 10.1007/s11709-023-0984-8

Abstract: This paper presents a case study of the clogging of a slurry-shield tunnel-boring machine (TBM) experiencedduring tunnel operations in clay-rich argillaceous siltstones under the Ganjiang River, China.In this case study, the effect of clogging on the slurry-shield TBM tunneling performance (e.g., advanceThe potential for clogging during tunnel operations in argillaceous siltstone was estimated using an

Keywords： slurry-shield TBM     geological investigation     clogging     argillaceous siltstone     TBM performance     mitigation measures    

HTML PDF Collect

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    

HTML PDF Collect

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    

HTML PDF Collect

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.Field data sets including geological conditions, shield operational parameters, and tunnel geometry collectedfrom four sections of tunnel with a total of 3.93 km are used to build models.

Keywords： EPB shield     shield tunneling     settlement prediction     machine learning    

HTML PDF Collect

Construction risks of Huaying mount tunnel and countermeasures

Haibo YAO, Feng GAO, Shigang YU, Wei DANG

Frontiers of Structural and Civil Engineering 2017, Volume 11, Issue 3,   Pages 279-285 doi: 10.1007/s11709-017-0414-x

Abstract: inrush because of existence of faults, high pressure gas, karst tectonics and coal goafs around the tunnelOne is geology prediction, and the other is automatic wireless real-time monitoring system, which contains

Keywords： tunnel?construction     gas?outburst     geology?prediction     automatic?monitoring?system    

HTML PDF Collect

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    

HTML PDF Collect

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    

HTML PDF Collect

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    

HTML PDF Collect

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    

HTML PDF Collect

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    

HTML PDF Collect

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

Effect of cutterhead configuration on tunnel face stability during shield machine maintenance outages

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

Clogging of slurry-shield tunnel-boring machine drives in sedimentary soft rock: A case study

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

Construction risks of Huaying mount tunnel and countermeasures

Haibo YAO, Feng GAO, Shigang YU, Wei DANG

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