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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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Enhanced wear prediction of tunnel boring machine disc cutters for accurate remaining useful life estimation

Frontiers of Structural and Civil Engineering 2024, Volume 18, Issue 4,   Pages 642-662 doi: 10.1007/s11709-024-1058-2

Abstract: In tunnel construction with tunnel boring machines (TBMs), accurate prediction of the remaining usefulintroduces a novel hybrid model, integrating fundamental and data-driven approaches, to enhance wear predictionimproved by incorporating composite wear mechanisms and load estimation techniques, showcasing superior predictionsupplementary residual term into the improved fundamental model, leading to a high-performance wear predictionUsing actual field data from a highway tunnel project in Shenzhen, the performance of the hybrid model

Keywords： tunnel boring machine     disc cutter     wear prediction     remaining useful life     field data     hybrid model    

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Online machine learning for stream wastewater influent flow rate prediction under unprecedented emergencies

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 12, doi: 10.1007/s11783-023-1752-7

Abstract:

● Online learning models accurately predict influent flow rate at

Keywords： Wastewater prediction     Data stream     Online learning     Batch learning     Influent flow rates    

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Real-time prediction of tunnel face conditions using XGBoost Random Forest algorithm

Frontiers of Structural and Civil Engineering 2023, Volume 17, Issue 12,   Pages 1777-1795 doi: 10.1007/s11709-023-0044-4

Abstract: perception of rock conditions based on continuously collected data to meet the requirements of continuous TunnelBoring Machine (TBM) construction presents a critical challenge that warrants increased attention.of the TBM loading phase is short, usually within a few minutes after the disc cutter contacts the tunnelA model based on the features during the loading phase has a miss rate of 21.8%, indicating that it canmodel, ultimately reducing the miss rate to 16.1%. (3) Resampling the imbalanced data set can effectively

Keywords： Tunnel Boring Machine     fractured and weak rock mass     machine learning model     real-time early warming     tunnel face rock condition    

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Geological risk prediction under uncertainty in tunnel excavation using online learning and hidden Markov

Frontiers of Engineering Management doi: 10.1007/s42524-024-0082-1

Abstract: including the hidden Markov model, long short-term memory network, neural network, and support vector machine, in predicting geological risks ahead of the tunnel boring machine.This research advances geological risk prediction models by offering an online updating capability fortunnel excavation and construction projects.It enables early-stage risk prediction and provides long-term forecasts with minimal historical data

Keywords： geological risk prediction     machine learning     online learning     hidden Markov model     borehole logging    

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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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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    

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Technical innovation in the “Beishan No. 1” hard rock tunnel boring machine for high-gradient spiral

Frontiers of Engineering Management 2024, Volume 11, Issue 1,   Pages 175-179 doi: 10.1007/s42524-023-0288-7

Abstract: Technical innovation in the “Beishan No. 1” hard rock tunnel boring machine for high-gradient spiral

Keywords： tunnel boring machine     extremely hard rock     continuous small-radius curves     high gradient     equipment    

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

Frontiers of Structural and Civil Engineering 2023, Volume 17, Issue 10,   Pages 1502-1516 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    

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Toward Next-Generation Heterogeneous Catalysts: Empowering Surface Reactivity Prediction with Machine

Xinyan Liu,Hong-Jie Peng,

Engineering doi: 10.1016/j.eng.2023.07.021

Abstract: Computational high-throughput screening presents a viable solution to this challenge, as machine learningThis review focuses on recent progress in applying ML in adsorption energy prediction, which predominantly

Keywords： Machine learning     Heterogeneous catalysis     Chemisorption     Theoretical simulation     Materials design     High-throughput    

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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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Drainage design combining drain holes and pinholes for tunnel boring machine segments subject to high

Frontiers of Structural and Civil Engineering 2023, Volume 17, Issue 11,   Pages 1723-1738 doi: 10.1007/s11709-023-0948-z

Abstract: However, regarding the segments of tunnel boring machines (TBMs) under high water pressure, the stabilityfluid–structure coupling theory, a new drainage design for TBM segments was developed by considering a mountain tunnel

Keywords： TBM segment     high water pressure     drain hole     pinhole     groundwater table drawdown    

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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

Enhanced wear prediction of tunnel boring machine disc cutters for accurate remaining useful life estimation

Journal Article

Online machine learning for stream wastewater influent flow rate prediction under unprecedented emergencies

Journal Article

Real-time prediction of tunnel face conditions using XGBoost Random Forest algorithm

Journal Article

Geological risk prediction under uncertainty in tunnel excavation using online learning and hidden Markov

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

Technical innovation in the “Beishan No. 1” hard rock tunnel boring machine for high-gradient spiral

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

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

Journal Article

Toward Next-Generation Heterogeneous Catalysts: Empowering Surface Reactivity Prediction with Machine

Xinyan Liu,Hong-Jie Peng,

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

Drainage design combining drain holes and pinholes for tunnel boring machine segments subject to high

Journal Article