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Viscoelastic Burger’s model for tunnels supported with tangentially yielding liner

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Journal of Rock Mechanics and Geotechnical Engineering 2023年第4期15卷 826-837页

作者： Xiongyu Hu Marte gutierrez University Transportation Center for Underground Transportation Infrastructure Colorado School of MinesGoldenCO80401USA

This study proposed an analytical model for the tunnel supported with a tangentially yielding liner in viscoelastic ground.The efficiency of the developed analytical model was verified by comparing the calculated results with associated numerical simulation results.Using the analytical model,a comprehensive parameter sensitivity analysis was performed to examine the effects of the rate of tunnel face advancement,concrete liner thickness,installation time of liner,and strength and thickness of yielding elements on the tunnel responses.The results highlight the significant benefit of the tangentially yielding liner to relieve overstress in the tunnel liner and improve the stability of the tunnel.The yield efficiency of the tangentially yielding liner depends highly on the yielding strength and deformable capacity of the yielding elements and less on the installation time.

关键词： Viscoelastic Burger’s rock Deep tunnel Tangentially yielding liner Closed-form solution

Physical model simulation of rock-support interaction for the tunnel in squeezing ground

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Journal of Rock Mechanics and Geotechnical Engineering 2022年第1期14卷 82-92页

作者： Ketan Arora Marte gutierrez Ahmadreza Hedayat Civil and Environmental Engineering Colorado School of MinesGoldenCO80401USA

The existence of squeezing ground conditions can lead to significant challenges in designing an adequate support system for tunnels.Numerous empirical,observational and analytical methods have been suggested over the years to design support systems in squeezing ground conditions,but all of them have some limitations.In this study,a novel experimental setup having physical model for simulating the tunnel boring machine(TBM)excavation and support installation process in squeezing clay-rich rocks is developed.The observations are made to understand better the interaction between the support and the squeezing ground.The physical model included a large true-triaxial cell,a miniature TBM,laboratoryprepared synthetic test specimen with properties similar to natural mudstone,and an instrumented cylindrical aluminum support system.Experiments were conducted at realistic in situ stress levels to study the time-dependent three-dimensional tunnel support convergence.The tunnel was excavated using the miniature TBM in the cubical rock specimen loaded in the true-triaxial cell,after which the support was installed.The confining stress was then increased in stages to values greater than the rock’s unconfined compressive strength.A model for the time-dependent longitudinal displacement profile(LDP)for the supported tunnel was proposed using the tunnel convergence measurements at different times and stress levels.The LDP formulation was then compared with the unsupported model to calculate the squeezing amount carried by the support.The increase in thrust in the support was backcalculated from an analytical solution with the assumption of linear elastic support.Based on the test results and case studies,a recommendation to optimize the support requirement for tunnels in squeezing ground is proposed.

关键词： Squeezing ground Tunnel support system Longitudinal displacement profile Rock-support interaction

Effectiveness of predicting tunneling-induced ground settlements using machine learning methods with small datasets

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Journal of Rock Mechanics and Geotechnical Engineering 2022年第4期14卷 1028-1041页

作者： Linan Liu Wendy Zhou Marte gutierrez Department of Geology and Geological Engineering Colorado School of MinesGoldenCO80401USA Department of Civil and Environmental Engineering Colorado School of MinesGoldenCO80401USA

Prediction of tunneling-induced ground settlements is an essential task,particularly for tunneling in urban settings.Ground settlements should be limited within a tolerable threshold to avoid damages to aboveground structures.Machine learning(ML)methods are becoming popular in many fields,including tunneling and underground excavations,as a powerful learning and predicting technique.However,the available datasets collected from a tunneling project are usually small from the perspective of applying ML methods.Can ML algorithms effectively predict tunneling-induced ground settlements when the available datasets are small?In this study,seven ML methods are utilized to predict tunneling-induced ground settlement using 14 contributing factors measured before or during tunnel excavation.These methods include multiple linear regression(MLR),decision tree(DT),random forest(RF),gradient boosting(GB),support vector regression(SVR),back-propagation neural network(BPNN),and permutation importancebased BPNN(PI-BPNN)models.All methods except BPNN and PI-BPNN are shallow-structure ML methods.The effectiveness of these seven ML approaches on small datasets is evaluated using model accuracy and stability.The model accuracy is measured by the coefficient of determination(R2)of training and testing datasets,and the stability of a learning algorithm indicates robust predictive performance.Also,the quantile error(QE)criterion is introduced to assess model predictive performance considering underpredictions and overpredictions.Our study reveals that the RF algorithm outperforms all the other models with the highest model prediction accuracy(0.9)and stability(3.0210^(-27)).Deep-structure ML models do not perform well for small datasets with relatively low model accuracy(0.59)and stability(5.76).The PI-BPNN architecture is proposed and designed for small datasets,showing better performance than typical BPNN.Six important contributing factors of ground settlements are identified,including tunnel depth,the distance between tunnel face an

关键词： Ground settlements Tunneling Machine learning Small dataset Model accuracy Model stability Feature importance

Empirical approach for reliability evaluation of tunnel excavation stability using the Q rock mass classification system

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Underground Space 2022年第5期7卷 862-881页

作者： Hui Lu Marte gutierrez Eunhye Kim McMillen Jacobs Associates Walnut CreekCAUSA Department of Civil and Environmental Engineering Colorado School of MinesGoldenCOUSA Federal Energy Regulatory Commission San FranciscoCAUSA

The critical strain concept has been widely used in analytical or numerical approaches to evaluate the stability of underground excavations.Analytical,empirical,and numerical procedures are usually used to determine the critical strain values.This paper presents a reliability assessment procedure for evaluating excavation stability using the empirical approach based on the rock mass classification Q and the first order reliability method(FORM).In contrast to deterministic critical strain values,a probabilistic critical strain,which considers uncertainties in rock mass parameters,was incorporated in a limit state function for reliability analysis.Using the rock mass classification Q,the empirically estimated tunnel stain was included in the limit state function.The critical strain and estimated tunnel strain were probabilistically characterized based on the rock mass classification Q-derived rock mass properties.Monte Carlo simulations were also conducted for comparing the reliability analysis results with those derived from the FORM algorithm.A highway tunnel case study was used to demonstrate the reliability assessment procedure.The effects of the input ground parameter correlations,probability distributions,and coefficients of variation on tunnel reliability were investigated.Results show that uncorrelated and normally distributed input parameters(intact rock strength and elastic modulus)have generated more conservative reliability.The reliability analysis results also show that the tunnel had relatively high reliability(reliability index of 2.78 and probability of failure of 0.27%),indicating the tunnel is not expected to experience instability after excavation.The tunnel excavation stability was assessed using analytical and numerical approaches for comparison.The results were consistent with the reliability analysis using the FORM algorithm’s Q-based empirical method.

关键词： Reliability analysis Tunnel Critical strain Rock mass classification Q First order reliability method

Predicting the coefficient of friction in a sliding contact by applying machine learning to acoustic emission data

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Friction 2024年第6期12卷 1299-1321页

作者： Robert gutierrez Tianshi FANG Robert MAINWARING Tom REDDYHOFF Tribology Group Department of Mechanical EngineeringImperial College LondonExhibition RoadLondon SW72AZUK Shell Global Solutions(US)Inc.Shell Technology Center Houston 3333 Highway 6 SouthHoustonTX 77082USA Shell International Petroleum Company Limited Shell CentreYork RoadLondon SE17NAUK

It is increasingly important to monitor sliding interfaces within machines,since this is where both energy is lost,and failures occur.Acoustic emission(AE)techniques offer a way to monitor contacts remotely without requiring transparent or electrically conductive materials.However,acoustic data from sliding contacts is notoriously complex and difficult to interpret.Herein,we simultaneously measure coefficient of friction(with a conventional force transducer)and acoustic emission(with a piezoelectric sensor and high acquisition rate digitizer)produced by a steel‒steel rubbing contact.Acquired data is then used to train machine learning(ML)algorithms(e.g.,Gaussian process regression(GPR)and support vector machine(SVM))to correlated acoustic emission with friction.ML training requires the dense AE data to first be reduced in size and a range of processing techniques are assessed for this(e.g.,down-sampling,averaging,fast Fourier transforms(FFTs),histograms).Next,fresh,unseen AE data is given to the trained model and the resulting friction predictions are compared with the directly measured friction.There is excellent agreement between the measured and predicted friction when the GPR model is used on AE histogram data,with root mean square(RMS)errors as low as 0.03 and Pearson correlation coefficients reaching 0.8.Moreover,predictions remain accurate despite changes in test conditions such as normal load,reciprocating frequency,and stroke length.This paves the way for remote,acoustic measurements of friction in inaccessible locations within machinery to increase mechanical efficiency and avoid costly failure/needless maintenance.

关键词： acoustic emission condition monitoring friction machine learning Gaussian process regression support vector machine

Modern data sources and techniques for analysis and forecast of road accidents:A review

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Journal of Traffic and Transportation Engineering(English Edition) 2020年第4期7卷 432-446页

作者： Camilo gutierrez-Osorio Cesar Pedraza Departamento de Ingeniena de Sistemas e Industrial Universidad Nacional de ColombiaBogotaColombia

Road accidents are one of the most relevant causes of injuries and death worldwide,and therefore,they constitute a significant field of research on the use of advanced algorithms and techniques to analyze and predict traffic accidents and determine the most relevant elements that contribute to road accidents.The research of road accident prediction aims to respond to the challenge of offer tools to generate a more secure mobility environment,and ultimately,save lives.This paper aims to provide an overview of the state of the art in the prediction of road accidents through machine learning algorithms and advanced techniques for analyzing information,such as convolutional neural networks and long short-term memory networks,among other deep learning architectures.Furthermore,in this article,a compendium and study of the most used data sources for the road accident forecast is made.And a classification is proposed according to its origin and characteristics,such as open data,measurement technologies,onboard equipment and social media data.For the analysis of the information,the different algorithms employed to make predictions about road accidents are listed and compared,as well as their applicability depending on the types of data being analyzed,along with the results obtained and their ease of interpretation and analysis.The best results reported by the authors are obtained when two or more analytic techniques are combined,in such a way that analysis of the obtained results is strengthened.Among the future challenges in road traffic forecasting lies the enhancement of the scope of the proposed models and predictions by the incorporation of heterogeneous data sources,that include geo spatial data,information from traffic volume,traffic statistics,video,sound,text and sentiment from social media,that many authors concur that can improve the precision and accuracy of the analysis and predictions.

关键词： Traffic engineering Data analysis Machine learning Road accident forecasting Traffic accident prediction

CFD Analysis of an 800 kV HVDC Transformer Using Rapeseed Oil as the Cooling Fluid

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CSEE Journal of Power and Energy Systems 2022年第5期8卷 1508-1518页

作者： Edgardy M.gutierrez Diaz Gaosheng Wei Liu Cui Key Laboratory of Power Station Energy Transfer Conversion and System(Ministry of Education) School of Energy Power and Mechanical EngineeringNorth China Electric Power UniversityBeijing 102206China Department of Power Engineering School of Electrical EngineeringBicentenary University of AraguaTurmero 2115Venezuela

This study aims to analyze rapeseed oil’s effectiveness as a coolant of a high-voltage direct current(HVDC)converter transformer while comparing it with mineral oil.We employed a numerical model,assisted by Computational Fluid Dynamics(CFD)tools,to simulate the convective heat transfer inside a HVDC converter transformer,which is an oil-directed air forced(ODAF)type.The model seeks to obtain the temperature and velocity fields at the critical points of the fluid domain.The simulation domain reproduces a 2D cross-sectional model in the center of the structure.The results show that rapeseed oil requires more time to gain heat from the core and windings due to its lower thermal conductivity.However,the average temperature of rapeseed and mineral oils will not significantly differ once the channel temperature reaches a stable value.It indicates that the rapeseed oil can effectively replace mineral oil under the same heating conditions.Also,a slower flow of the rapeseed oil allows the fluid to smoothly enter the cooling channel.Therefore,the rapeseed oil cools these channels by pushing the fluid in them and absorbs heat from the sources earlier than mineral oil.The improvement in cooling indicates that rapeseed oil is better than mineral oil as a coolant and electrical insulating fluid for power transformers.

关键词： CFD analysis cooling fluid HVDC transformer rapeseed oil

An Optimal Design Method Based on Small Amplitude Homogenization

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Chinese Annals of Mathematics,Series B 2015年第5期36卷 843-854页

作者： Sergio gutierrez Department of Structural and Geotechnical Engineering Pontificia Universidad Catolica de Chile

An optimal design method for two materials based on small amplitude homogenization is presented. The method allows to use quite general objective functions at the price that the two materials should have small contrasts in their relevant physical parameters. The following two applications are shown: Stress constrained compliance minimization and defect location in elastic bodies.

关键词：优化设计方法均匀化幅度目标函数物理参数缺陷位置应力约束双材料

Coupled hydro-thermo-mechanical modeling of hydraulic fracturing in quasi-brittle rocks using BPM-DEM

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Journal of Rock Mechanics and Geotechnical Engineering 2017年第1期9卷 92-104页

作者： Ingrid Tomac Marte gutierrez Department of Structural Engineering University of California San Diego Department of Civil and Environmental Engineering Colorado School of Mines Department of Geosciences University of Oslo

This paper presents an improved understanding of coupled hydro-thermo-mechanical(HTM) hydraulic fracturing of quasi-brittle rock using the bonded particle model(BPM) within the discrete element method(DEM). BPM has been recently extended by the authors to account for coupled convective econductive heat flow and transport, and to enable full hydro-thermal fluidesolid coupled modeling.The application of the work is on enhanced geothermal systems(EGSs), and hydraulic fracturing of hot dry rock(HDR) is studied in terms of the impact of temperature difference between rock and a flowing fracturing fluid. Micro-mechanical investigation of temperature and fracturing fluid effects on hydraulic fracturing damage in rocks is presented. It was found that fracture is shorter with pronounced secondary microcracking along the main fracture for the case when the convectiveeconductive thermal heat exchange is considered. First, the convection heat exchange during low-viscosity fluid infiltration in permeable rock around the wellbore causes significant rock cooling, where a finger-like fluid infiltration was observed. Second, fluid infiltration inhibits pressure rise during pumping and delays fracture initiation and propagation. Additionally, thermal damage occurs in the whole area around the wellbore due to rock cooling and cold fluid infiltration. The size of a damaged area around the wellbore increases with decreasing fluid dynamic viscosity. Fluid and rock compressibility ratio was found to have significant effect on the fracture propagation velocity.

关键词： Hydro-thermo-mechanical(HTM) modeling Enhanced geothermal systems(EGSs) Discrete element method(DEM) Bonded particle model(BPM) Conductive-convective heat flow and transport Hydraulic fracturing Rock permeability enhancement