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.

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.

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.

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.

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.

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. .

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.

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.

Heterogeneous traffic flow modeling with drivers’ timid and aggressive characteristics

The driver’s characteristics(e.g.,timid and aggressive)has been proven to greatly affect the traffic flow performance,whereas the underlying assumption in most of the existing studies is that all drivers are homogeneous.In the real traffic environment,the drivers are distinct due to a variety of factors such as personality characteristics.To better reflect the reality,we introduce the penetration rate to describe the degree of drivers’heterogeneity(i.e.,timid and aggressive),and proceed to propose a generalized heterogeneous car-following model with different driver’s characteristics.Through the linear stability analysis,the stability conditions of the proposed heterogeneous traffic flow model are obtained based on the perturbation method.The impacts of the penetration rate of drivers with low intensity,the average value and standard deviation of intensity parameters characterizing two types of drivers on the stability of traffic flow are analyzed by simulation.Results show that higher penetration of aggressive drivers contributes to traffic flow stability.The average value has a great impact on the stability of traffic flow,whereas the impact of the standard deviation is trivial.

Numerical and experimental investigation on the depressurization capacity of a new type of depressure-dominated jet mill bit

Efficient cuttings transport and improving rate of penetration(ROP)are two major challenges in horizontal drilling and extended reach drilling.A type of jet mill bit(JMB)may provide an opportunity to catch the two birds with one stone:not only enhancing cuttings transport efficiency but also improving ROP by depressuring at the bottom hole.In this paper,the JMB is further improved and a new type of depressure-dominated JMB is presented;meanwhile,the depressurization capacity of the depressure-dominated JMB is investigated by numerical simulation and experiment.The numerical study shows that low flow-rate ratio helps to enhance the depressurization capacity of the depressure-dominated JMB;for both depressurization and bottom hole cleaning concern,the flow-rate ratio is suggested to be set at approximately 1:1.With all other parameter values being constant,lower dimensionless nozzle-to-throat-area ratio may result in higher depressurization capacity and better bottom hole cleaning,and the optimal dimensionless nozzle-to-throat-area ratio is at approximately0.15.Experiments also indicate that reducing the dimensionless flow-rate ratio may help to increase the depressurization capacity of the depressure-dominated JMB.This work provides drilling engineers with a promising tool to improve ROP.

Analysis of Power Quality Problems in Large-Scale Application of Air-Source Heat Pump

With the implementation of electric energy alternatives,the large-scale application of electric energy substitution represented by air-source heat pumps has replaced traditional coal-fired heating,which is beneficial for the environment and alleviates air pollution.However,the large-scale application of airsource heat pumps has brought power quality problems such as voltage sags,harmonic pollution,and three-phase imbalance to the distribution network.This paper studies the fixed-frequency and variablefrequency air-source heat pump,introduces its working principle,analyzes the mechanism of its power quality problem.Moreover,the paper establishes a simulation model for the fixed-frequency heat pump and variable-frequency heat pump to connect to the distribution network.This research mainly studies the impact of large-scale fixed-frequency heat pumps on the depth of voltage sags in the distribution network and the impact of large-scale variable-frequency heat pumps on the harmonic content of the distribution network under different penetration rates and uses measured data to verify the reliability of the simulation results.This paper uses experimental data for the first time to verify the real power quality problems of large-scale heat pumps,which can provide a reference for determining the power quality standards for heat pumps connected to the power grid.At the same time,it can also provide a reference for the power quality management of the distribution network that is actually connected to electric heating.

Evaluation of Limestone Interval in the Drilled Surface Section of Bn-1 Oil Well

The first exploration oil well in any oil block consumes in general more time and cost than the other wells in the same block. Evaluating the drilled wells serves to improve the future operations. This paper evaluates the drilled surface section through real field data for the first exploration oil well drilled in one of the oil blocks, in Kurdistan north of Iraq. The surface section of the well was drilled with the conventional method to penetrate many different geological formations with tight intervals. Drilling efficiency and the difficulties encountered are discussed and explained using various data sources. All daily drilling reports concerning a specific interval were studied. This includes weight on bit, string rotation, mud pump flow and penetration rate. Evaluation was carried out by analyzing the used controllable drilling parameters with the formations features. Penetration of the Pila Spi formation (Middle Eocene) was the most difficult formation in the drilled section. Microsoft Office 365 Pro Plus used in making graphs and Excel tables. Evaluations showed that the conventional technology used left many negative effects, like increase in None Productive Time NPT, cost and ground water pollution. Simultaneous Casing Drilling method proposed as an alternative method for the future campaign.

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.

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.

Comparison of accuracy and computational performance between the machine learning algorithms for rate of penetration in directional drilling well

Oil and gas reservoirs are of the main assets of countries possessing them.Production from these reservoirs is one of the main concerns of engineers,which can be achieved by drilling oil and gas reservoirs.Construction of hydrocarbon wells is one of the most expensive operations in the oil industry.One of the most important parameters affecting drilling cost is the rate of penetration(ROP).This paper predicts the rate of penetration using artificial intelligence and hybrid models on Kaboud oil field well#7 in the directional stage.In this study,different models were constructed through various approaches based on 1878 dataset obtained from drilling operation in the well#7.Then,the accuracy of the constructed models was compared with each other.It was found that the MLP-ABC algorithm predicts the rate of penetration more accurately,by far,as compared with other methods.The MLP-ABC algorithm achieves impressive ROP prediction accuracy(RMSE=0.007211 m/h;AAPD=0.1871%;R^(2)=1.000 for the testing subset).Consequently,it can be concluded that this method is applicable to predict the drilling rate in that oilfield.

Applications and theoretical perspectives of artificial intelligence in the rate of penetration

Artificial Intelligence(AI)is becoming popular for the Rate of Penetration(ROP)estimation,hence,the need to study the best techniques and their advantages over empirical models.Various literatures were analysed to determine the prevalence of AI in ROP computation and compare the computation accuracies with empirical models.Artificial Neural Network(ANN)accounted for over 92%of the AI techniques used for ROP computation and Weight on Bit(WOB)mostly influenced the computation accuracy.The accuracy of AI algorithms is better than the empirical models thus,will improve the drilling efficiency,reduce cost and enhance the development of pad wells.

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.

Confined compressive strength model of rock for drilling optimization

The confined compressive strength(CCS)plays a vital role in drilling optimization.On the basis of Jizba's experimental results,a new CCS model considering the effects of the porosity and nonlinear characteristics with increasing confining pressure has been developed.Because the confining pressure plays a fundamental role in determining the CCS of bottom-hole rock and because the theory of Terzaghi's effective stress principle is founded upon soil mechanics,which is not suitable for calculating the confining pressure in rock mechanics,the double effective stress theory,which treats the porosity as a weighting factor of the formation pore pressure,is adopted in this study.The new CCS model combined with the mechanical specific energy equation is employed to optimize the drilling parameters in two practical wells located in Sichuan basin,China,and the calculated results show that they can be used to identify the inefficient drilling situations of underbalanced drilling(UBD)and overbalanced drilling(OBD).

Improving TC drill bit's efficiency and resistance to wear by graphene coating

Displaying a two-dimensional pure crystal carbon structure,Graphene is the strongest,yet thinnest substance discovered by scientists.Coating tungsten carbide(TC)drill bits with graphene to evaluate the effect of graphene on the wear,as well as the rate of penetration of the drilling bit was examined in this research.Two evaluation approaches were employed:one with employing ANSYS Software and the second by employing atomic pressure chemical vapor deposition(APCVD synthesis)in the laboratory to produce a monolayer graphene coating.The simultaneous software-based and lab-based testing were performed to increase the credibility of the results and minimize the potential errors.Conducting the simulation using ANSYS,the maximum shear elastic strain,equivalent elastic strain,equivalent(von mises)stress,total deformation and maximum shear stress were investigated prior and after the gra-phene coating was applied on TC simulated bit.Total deformation was only slightly increased,while the maximum shear elastic strain was almost doubled,reflecting that the bit's wear was significantly reduced after the coating.Lab-based APCVD synthesis results showed 34%increase in compressive strength of the coated bit,in comparison to the uncoated one.The failure occurred for uncoated bit at 35 MPa,where the coated bit experienced failure at 46.9 MPa.The Von Mises stress test conducted on the coated and uncoated samples also indicated that this stress was 41%less for the coated bit,in comparison to the uncoated one.Finally,two small-scale drilling operations,one using a 1inch graphene-coated TC bit and the other using a 1inch non-coated TC bit,were performed on a granite block,to evaluate the performance of the graphene-coated bit in practice.In a chosen 120-min time frame,27 consecutive holes could be drilled by the graphene-coated TC bit,while 19 consecutive holes could be drilled by the uncoated TC bit,in identical drilling conditions.This implies a 42%increase in ROP.

Analysis of highway performance under mixed connected and regular vehicle environment

Purpose–This study aims to study the connected vehicle(CV)impact on highway operational performance under a mixed CV and regular vehicle(RV)environment.Design/methodology/approach–The authors implemented a mixed trafficflow model,along with a CV speed control model,in the simulation environment.According to the different traffic characteristics between CVs and RVs,this researchfirst analyzed how the operation of CVs can affect highway capacity under both one-lane and multi-lane cases.A hypothesis was then made that there shall exist a critical CV penetration rate that can significantly show the benefit of CV to the overall traffic.To prove this concept,this study simulated the mixed traffic pattern under various conditions.Findings–The results of this research revealed that performing optimal speed control to CVs will concurrently benefit RVs by improving highway capacity.Furthermore,a critical CV penetration rate should exist at a specified traffic demand level,which can significantly reduce the speed difference between RVs and CVs.The results offer effective insight to understand the potential impacts of different CV penetration rates on highway operation performance.Originality/value–This approach assumes that there shall exist a critical CV penetration rate that can maximize the benefits of CV implementations.CV penetration rate(the proportion of CVs in mixed traffic)is the key factor affecting the impacts of CV on freeway operational performance.The evaluation criteria for freeway operational performance are using average travel time under different given traffic demand patterns.