Predicting TBM penetration rate in hard rock condition:A comparative study among six XGB-based metaheuristic techniques

A reliable and accurate prediction of the tunnel boring machine(TBM)performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects.This research aims to develop six hybrid models of extreme gradient boosting(XGB)which are optimized by gray wolf optimization(GWO),particle swarm optimization(PSO),social spider optimization(SSO),sine cosine algorithm(SCA),multi verse optimization(MVO)and moth flame optimization(MFO),for estimation of the TBM penetration rate(PR).To do this,a comprehensive database with 1286 data samples was established where seven parameters including the rock quality designation,the rock mass rating,Brazilian tensile strength(BTS),rock mass weathering,the uniaxial compressive strength(UCS),revolution per minute and trust force per cutter(TFC),were set as inputs and TBM PR was selected as model output.Together with the mentioned six hybrid models,four single models i.e.,artificial neural network,random forest regression,XGB and support vector regression were also built to estimate TBM PR for comparison purposes.These models were designed conducting several parametric studies on their most important parameters and then,their performance capacities were assessed through the use of root mean square error,coefficient of determination,mean absolute percentage error,and a10-index.Results of this study confirmed that the best predictive model of PR goes to the PSO-XGB technique with system error of(0.1453,and 0.1325),R^(2) of(0.951,and 0.951),mean absolute percentage error(4.0689,and 3.8115),and a10-index of(0.9348,and 0.9496)in training and testing phases,respectively.The developed hybrid PSO-XGB can be introduced as an accurate,powerful and applicable technique in the field of TBM performance prediction.By conducting sensitivity analysis,it was found that UCS,BTS and TFC have the deepest impacts on the TBM PR.

Application of artificial neural networks to the prediction of tunnel boring machine penetration rate

Rate of penetration of a Tunnel Boring Machine(TBM) in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project.This paper presents the results of a study into the application of an Artificial Neural Network(ANN) technique for modeling the penetration rate of tunnel boring machines.A database,including actual,measured TBM penetration rates,uniaxial compressive strengths of the rock,the distance between planes of weakness in the rock mass and rock quality designation was established.Data collected from three different TBM projects(the Queens Water Tunnel,USA,the Karaj-Tehran water transfer tunnel,Iran,and the Gilgel Gibe II hydroelectric project,Ethiopia).A five-layer ANN was found to be optimum,with an architecture of three neurons in the input layer,9,7 and 3 neurons in the first,second and third hidden layers,respectively,and one neuron in the output layer.The correlation coefficient determined for penetration rate predicted by the ANN was 0.94.

Drillability prediction in some metamorphic rocks using composite penetration rate index(CPRI)–An approach

Assessment of drillability of rocks is vital in the selection,operation,and performance evaluation of cutting tools used in various excavation machinery deployed in mining and tunneling.The commonly used rock drillability prediction methods,namely,drilling rate index(DRI)and Cerchar hardness index(CHI)have limitations in predicting the penetration rate due to differential wear of the cutting tool in rocks with varied hardness and abrasivity.Since cutting tools get blunt differently in different rocks,the stress beneath the tip of the bit decreases until it reaches a threshold value beyond which the penetration rate becomes constant.In this research,a new composite penetration rate index(CPRI)is suggested based on the investigations on four metamorphic rocks viz.quartzite,gneiss,schist and phyllite with varied hardness-abrasivity values.The penetration-time behavior was classified into active,moderate,passive,and dormant phases based on the reduction in penetration rate at different stages of drilling.A comparison of predicted penetration rate values using DRI and CPRI with actual penetration rate values clearly establishes the supremacy of CPRI.Micro-structure and hardness-based index was also developed and correlated with CPRI.The new indices can help predict cutting tool penetration and its consumption more accurately.

Relationship between Formation Water Rate,Equivalent Penetration Rate and Volume Flow Rate of Air in Air Drilling

Formation water invasion is the most troublesome problem associated with air drilling. However, it is not economical to apply mist drilling when only a small amount of water flows into wellbore from formation during air drilling. Formation water could be circulated out of the wellbore through increasing the gas injection rate. In this paper, the Angel model was modified by introducing Nikurade friction factor for the flow in coarse open holes and translating formation water rate into equivalent penetration rate. Thus the distribution of annular pressure and the relationship between minimum air injection rate and formation water rate were obtained. Real data verification indicated that the modified model is more accurate than the Angel model and can provide useful information for air drilling.

Effect of Penetration Rates on the Piezocone Penetration Test in the Yellow River Delta Silt

Partial drainage often occurs during piezocone penetration testing on Yellow River Delta silt because of its intermediate physical and mechanical properties between those of sand and clay.Yet,there is no accurate understanding for the range of penetra-tion rates to trigger the partial drainage of silt soils.In order to fully investigate cone penetration rate effects under partial drainage condi-tions,indoor 1 g penetration model tests and numerical simulations of cavity expansion at variable penetration rates were carried out on the Yellow River Delta silt.The boundary effect of the model tests and the variation of key parameters at the different cavity ex-pansion rates were analyzed.The 1 g penetration model test results and numerical simulations results consistently indicated that the penetration rate to trigger the partially drainage of typical silt varied at least three orders of magnitude.The numerical simulations also provide the reference values for the penetration resistance corresponding to zero dilation and zero viscosity at any given normalized penetration rate for silt in Yellow River Delta.These geotechnical properties can be used for the design of offshore platforms in Yel-low River Delta,and the understanding of cone penetration rate effects under the partially drained conditions would provide some technical support for geohazard evaluation of offshore platforms.

Prediction of rotary drilling penetration rate in iron ore oxides using rock engineering system

Prediction of the drilling penetration rate is one of the important parameters in mining operations. This parameter has a direct impact on the mine planning and cost of mining operations, Generally, effective parameters on the penetration rate is divided into two classes： rock mass properties and specifications of the machine, The chemical components of intact rock have a direct effect in determining rock mechan- ical properties, Theses parameters usually have not been investigated in any research on the rock drill- ability, In this study, physical and mechanical properties of iron ore were studied based on the amount of magnetite percent, According to the results of the tests, the effective parameters on the pen- etration rate of the rotary drilling machines were divided into three classes： specifications of the machi- nes, rock mass properties and chemical component of intact rock, Then, the rock drillahility was studied using rock engineering systems, The results showed that feed, rotation, rock mass index and iron oxide percent have important effect on penetration rate, Then a quadratic equation with 0,896 determination coefficient has been obtained, Also, the results showed that chemical components can he described as new parameters in rotary drill penetration rate,

PENETRATION RATE MEASUREMENT AND ITS INSTRUMENT DESIGN

The practical method for precise penetration rate measurement in a short time and the instrument design have been specially discussed in this paper.Considering the defects of the penetration rate meters available,via the balanced design in consideration of service life,costs,accuracy and reliability,a kind of penetration rate meter has been put forward.The meter consists of a magnetic clutch,a gear box and a raster disk system to pick up the signal.The meter has the following advantages-it might measure a low speed with higher accura-cy,it could disengage automatically without being divorced from the driving device during tripping rods at high speed,it has a circuit of rotary direction discrimination and of repeated counting prevention so as to eU-minate error caused by rod vibration.

Observation on Sperm Penetration Rate of Infertile Men with Varicocele at 1.5 to 2 Years after Varicolectomy

Objective To observe the sperm penetration rate among treated but 'unsuccessful' infertile men with varicocele Material & Methods Twenty nine infertile males with II°(n=17) or III°(n=12) varicocele were enrolled into this study. They underwent a varicolectomy, but their spouses had not achieved a pregnancy after 1.5~2 years of treatment. The sperm zona-free hamster oocyte penetration assay (SPA) was used to compare the sperm penetration rate (PR) before treatment and 1.5~2 years after treatment. Results After varicolectomy, enhanced PR were observed in 7 cases of the II°group and in 1 case of the III°group. However, the PR values of these 8 cases were still below 20% PR-level. Conclusion Among the treated but 'unsuccessful' men, some patients could obtain improvements on PR after varicolectomy. But PRs of men with III°grade varicele would still remain a low PR-level which indicate varicolectomy is ineffective in increasing PR among part of sever varicocele patients. SPA is a useful test for evaluating the fertilizing potential of treated infertile males with varicocele.

Hybrid ensemble soft computing approach for predicting penetration rate of tunnel boring machine in a rock environment

This study implements a hybrid ensemble machine learning method for forecasting the rate of penetration(ROP) of tunnel boring machine(TBM),which is becoming a prerequisite for reliable cost assessment and project scheduling in tunnelling and underground projects in a rock environment.For this purpose,a sum of 185 datasets was collected from the literature and used to predict the ROP of TBM.Initially,the main dataset was utilised to construct and validate four conventional soft computing(CSC)models,i.e.minimax probability machine regression,relevance vector machine,extreme learning machine,and functional network.Consequently,the estimated outputs of CSC models were united and trained using an artificial neural network(ANN) to construct a hybrid ensemble model(HENSM).The outcomes of the proposed HENSM are superior to other CSC models employed in this study.Based on the experimental results(training RMSE=0.0283 and testing RMSE=0.0418),the newly proposed HENSM is potential to assist engineers in predicting ROP of TBM in the design phase of tunnelling and underground projects.

Utilizing partial least square and support vector machine for TBM penetration rate prediction in hard rock conditions

Rate of penetration(ROP) of a tunnel boring machine(TBM) in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project. The objectives of this work are to compare the accuracy of prediction models employing partial least squares(PLS) regression and support vector machine(SVM) regression technique for modeling the penetration rate of TBM. To develop the proposed models, the database that is composed of intact rock properties including uniaxial compressive strength(UCS), Brazilian tensile strength(BTS), and peak slope index(PSI), and also rock mass properties including distance between planes of weakness(DPW) and the alpha angle(α) are input as dependent variables and the measured ROP is chosen as an independent variable. Two hundred sets of data are collected from Queens Water Tunnel and Karaj-Tehran water transfer tunnel TBM project. The accuracy of the prediction models is measured by the coefficient of determination(R2) and root mean squares error(RMSE) between predicted and observed yield employing 10-fold cross-validation schemes. The R2 and RMSE of prediction are 0.8183 and 0.1807 for SVMR method, and 0.9999 and 0.0011 for PLS method, respectively. Comparison between the values of statistical parameters reveals the superiority of the PLSR model over SVMR one.

Interpretation and characterization of rate of penetration intelligent prediction model

Accurate prediction of the rate of penetration(ROP)is significant for drilling optimization.While the intelligent ROP prediction model based on fully connected neural networks(FNN)outperforms traditional ROP equations and machine learning algorithms,its lack of interpretability undermines its credibility.This study proposes a novel interpretation and characterization method for the FNN ROP prediction model using the Rectified Linear Unit(ReLU)activation function.By leveraging the derivative of the ReLU function,the FNN function calculation process is transformed into vector operations.The FNN model is linearly characterized through further simplification,enabling its interpretation and analysis.The proposed method is applied in ROP prediction scenarios using drilling data from three vertical wells in the Tarim Oilfield.The results demonstrate that the FNN ROP prediction model with ReLU as the activation function performs exceptionally well.The relative activation frequency curve of hidden layer neurons aids in analyzing the overfitting of the FNN ROP model and determining drilling data similarity.In the well sections with similar drilling data,averaging the weight parameters enables linear characterization of the FNN ROP prediction model,leading to the establishment of a corresponding linear representation equation.Furthermore,the quantitative analysis of each feature's influence on ROP facilitates the proposal of drilling parameter optimization schemes for the current well section.The established linear characterization equation exhibits high precision,strong stability,and adaptability through the application and validation across multiple well sections.

TBM penetration rate prediction based on the long short-term memory neural network

Tunnel boring machines(TBMs)are widely used in tunnel engineering because of their safety and efficiency.The TBM penetration rate(PR)is crucial,as its real-time prediction can reflect the adaptation of a TBM under current geological conditions and assist the adjustment of operating parameters.In this study,deep learning technology is applied to TBM performance prediction,and a PR prediction model based on a long short-term memory(LSTM)neuron network is proposed.To verify the performance of the proposed model,the machine parameters,rock mass parameters,and geological survey data from the water conveyance tunnel of the Hangzhou Second Water Source project were collected to form a dataset.Furthermore,2313 excavation cycles were randomly composed of training datasets to train the LSTM-based model,and 257 excavation cycles were used as a testing dataset to test the performance.The root mean square error and the mean absolute error of the proposed model are 4.733 and 3.204,respectively.Compared with Recurrent neuron network(RNN)based model and traditional time-series prediction model autoregressive integrated moving average with explanation variables(ARIMAX),the overall performance on proposed model is better.Moreover,in the rapidly increasing period of the PR,the error of the LSTM-based model prediction curve is significantly smaller than those of the other two models.The prediction results indicate that the LSTM-based model proposed herein is relatively accurate,thereby providing guidance for the excavation process of TBMs and offering practical application value.

Investigating the effects of gradual deployment of market penetration rates(MPR)of connected vehicles on delay time and fuel consumption

Purpose–This research paper aims to investigate the effects of gradual deployment of market penetration rates(MPR)of connected vehicles(MPR of CVs)on delay time and fuel consumption.Design/methodology/approach–A real-world origin-destination demand matrix survey was conducted in Boston,MA to identify the number of peak hour passing vehicles in the case study.Findings–The results showed that as the number of CVs(MPR)in the network increases,the total delay time decreases by an average of 14%and the fuel consumption decreases by an average of 56%,respectively,from scenarios 3 to 15 compared to scenario 2.Research limitations/implications–The first limitation of this study was considering a small network.The considered network shows a small part of the case study.Originality/value–This study can be a milestone for future research regarding gradual deployment of CVs’effects on transport networks.Efficient policy(s)may define based on the results of this network for Brockton transport network.

Enhancing Accumulation and Penetration Efficiency of Next-Generation Antibiotics to Mitigate Antibiotic Resistance in Pseudomonas aeruginosa PAO1

This study explores the efficacy of advanced antibiotic compounds against P. aeruginosa, focusing on Antibiotic B, an enhanced derivative of Ceftriaxone. The study measured the intracellular uptake of Antibiotic B and introduced a novel adjuvant, Influximax, which augmented its antibacterial activity. Results showed a diminished potential for resistance emergence with Antibiotic B, particularly when used in combination with Influximax. The study suggests that optimizing antibiotic delivery into bacterial cells and leveraging syner-gistic adjuvant combinations can enhance drug resistance combat. .

Reliability-based congestion pricing model under endogenous equilibrated market penetration and compliance rate of ATIS

A reliability-based stochastic system optimum congestion pricing(SSOCP) model with endogenous market penetration and compliance rate in an advanced traveler information systems(ATIS) environment was proposed. All travelers were divided into two classes. The first guided travelers were referred to as the equipped travelers who follow ATIS advice, while the second unguided travelers were referred to as the unequipped travelers and the equipped travelers who do not follow the ATIS advice(also referred to as non-complied travelers). Travelers were assumed to take travel time, congestion pricing, and travel time reliability into account when making travel route choice decisions. In order to arrive at on time, travelers needed to allow for a safety margin to their trip.The market penetration of ATIS was determined by a continuous increasing function of the information benefit, and the ATIS compliance rate of equipped travelers was given as the probability of the actually experienced travel costs of guided travelers less than or equal to those of unguided travelers. The analysis results could enhance our understanding of the effect of travel demand level and travel time reliability confidence level on the ATIS market penetration and compliance rate; and the effect of travel time perception variation of guided and unguided travelers on the mean travel cost savings(MTCS) of the equipped travelers, the ATIS market penetration, compliance rate, and the total network effective travel time(TNETT).

Tree-Based Solution Frameworks for Predicting Tunnel Boring Machine Performance Using Rock Mass and Material Properties

Tunnel Boring Machines(TBMs)are vital for tunnel and underground construction due to their high safety and efficiency.Accurately predicting TBM operational parameters based on the surrounding environment is crucial for planning schedules and managing costs.This study investigates the effectiveness of tree-based machine learning models,including Random Forest,Extremely Randomized Trees,Adaptive Boosting Machine,Gradient Boosting Machine,Extreme Gradient Boosting Machine(XGBoost),Light Gradient Boosting Machine,and CatBoost,in predicting the Penetration Rate(PR)of TBMs by considering rock mass and material characteristics.These techniques are able to provide a good relationship between input(s)and output parameters;hence,obtaining a high level of accuracy.To do that,a comprehensive database comprising various rock mass and material parameters,including Rock Mass Rating,Brazilian Tensile Strength,and Weathering Zone,was utilized for model development.The practical application of these models was assessed with a new dataset representing diverse rock mass and material properties.To evaluate model performance,ranking systems and Taylor diagrams were employed.CatBoost emerged as the most accurate model during training and testing,with R2 scores of 0.927 and 0.861,respectively.However,during validation,XGBoost demonstrated superior performance with an R2 of 0.713.Despite these variations,all tree-based models showed promising accuracy in predicting TBM performance,providing valuable insights for similar projects in the future.

Effects of data smoothing and recurrent neural network(RNN)algorithms for real-time forecasting of tunnel boring machine(TBM)performance

Tunnel boring machines(TBMs)have been widely utilised in tunnel construction due to their high efficiency and reliability.Accurately predicting TBM performance can improve project time management,cost control,and risk management.This study aims to use deep learning to develop real-time models for predicting the penetration rate(PR).The models are built using data from the Changsha metro project,and their performances are evaluated using unseen data from the Zhengzhou Metro project.In one-step forecast,the predicted penetration rate follows the trend of the measured penetration rate in both training and testing.The autoregressive integrated moving average(ARIMA)model is compared with the recurrent neural network(RNN)model.The results show that univariate models,which only consider historical penetration rate itself,perform better than multivariate models that take into account multiple geological and operational parameters(GEO and OP).Next,an RNN variant combining time series of penetration rate with the last-step geological and operational parameters is developed,and it performs better than other models.A sensitivity analysis shows that the penetration rate is the most important parameter,while other parameters have a smaller impact on time series forecasting.It is also found that smoothed data are easier to predict with high accuracy.Nevertheless,over-simplified data can lose real characteristics in time series.In conclusion,the RNN variant can accurately predict the next-step penetration rate,and data smoothing is crucial in time series forecasting.This study provides practical guidance for TBM performance forecasting in practical engineering.

Hard rock tunnel boring machine penetration test as an indicator of chipping process efficiency

The transition from grinding to chipping can be observed in tunnel boring machine(TBM) penetration test data by plotting the penetration rate(distance/revolution) against the net cutter thrust(force per cutter) over the full range of penetration rates in the test.Correlating penetration test data to the geological and geomechanical characteristics of rock masses through which a penetration test is conducted provides the ability to reveal the efficiency of the chipping process in response to changing geological conditions.Penetration test data can also be used to identify stress-induced tunnel face instability.This research shows that the strength of the rock is an important parameter for controlling how much net cutter thrust is required to transition from grinding to chipping.It also shows that the geological characteristics of a rock will determine how efficient chipping occurs once it has begun.In particular,geological characteristics that lead to efficient fracture propagation,such as fabric and mica contents,will lead to efficient chipping.These findings will enable a better correlation between TBM performance and geological conditions for use in TBM design,as a basis for contractual payments where penetration rate dominates the excavation cycle and in further academic investigations into the TBM excavation process.

Discussion of reasonable drilling parameters in impregnated diamond bit drilling

The impregnated diamond(ID)bit drilling is one of the main rotary drilling methods in hard rock drilling and it is widely used in mineral exploration,oil and gas exploration,mining,and construction industries.In this study,the quadratic polynomial model in ID bit drilling process was proposed as a function of controllable mechanical operating parameters,such as weight on bit(WOB)and revolutions per minute(RPM).Also,artificial neural networks(ANN)model for predicting the rate of penetration(ROP)was developed using datasets acquired during the drilling operation.The relationships among mechanical operating parameters(WOB and RPM)and ROP in ID bit drilling were analyzed using estimated quadratic polynomial model and trained ANN model.The results show that ROP has an exponential relationship with WOB,whereas ROP has linear relationship with RPM.Finally,the optimal regime of mechanical drilling parameters to achieve high ROP was confirmed using proposed model in combination with rock breaking principal.

Drilling cost optimization in a hydrocarbon field by combination of comparative and mathematical methods

In drilling operation, a large saving in time and money would be achieved by reducing the drilling time, since some of the costs are time-dependent. Drilling time could be minimized by raising the penetration rate. In the comparative optimization method, by using the records of the first drilled wells and comparing the criteria like penetration rate, cost per foot and specific energy, the drilling parameters of the next wells being drilled can be optimized in each depth interval. In the mathematical optimization technique, some numerical equations to model the penetration rate, bit wear rate and hydraulics would be used to minimize the drilling cost and time as much as possible and improve the results of the primary comparative optimization. In this research, as a case study the Iranian Khangiran gas field has been evaluated to optimize the drilling costs. A combination of the mentioned optimization techniques resulted in an optimal well which reduced the drilling time and cost considerably in comparison with the wells already drilled.