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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. [ABSTRACT FROM AUTHOR]

In rock and mining engineering, it is essential to predict rock cuttability and drillability for estimating drilling costs, selecting suitable drilling/cutting machine and tools, and achieving high productivity. To predict the drillability and cuttability, many destructive methods have been developed. However, they are often expensive and time-consuming. To explore a non-destructive method, this paper builds a series of empirical relationships between rock characteristic impedance and its drillability and cuttability indices by using a large quantity of measured data from various laboratory and field tests in previous publications. The relationships between rock’s UCS (uniaxial compressive strength) and its drillability and cuttability indices are also built for comparison. The results indicate that: (1) the penetration rates of both percussive drilling and rotary drilling depend on rock characteristic impedance, and they decrease with increasing impedance. (2) The specific energies in percussive drilling, rotary drilling and cutting all increase with increasing rock impedance. (3) Most of current drillability indices such as alfa, DRI, CLI and Shore hardness have a certain relationship with rock impedance. (4) Characteristic impedance has a more confident relation with drillability and cuttability than the UCS. Since determination of rock impedance does not necessarily require a destructive method, characteristic impedance has a great potential to be taken as an index to predict rock drillability and cuttability. [ABSTRACT FROM AUTHOR]

The integration of autonomous vehicles into urban traffic systems offers a significant opportunity to improve traffic efficiency and safety at signalized intersections. This study provides a comprehensive evaluation of how different autonomous vehicle driving behaviors—cautious, normal, aggressive, and platooning—affect key traffic metrics, including queue lengths, travel times, vehicle delays, emissions, and fuel consumption. A four-leg signalized intersection in Balgat, Ankara, was modeled and validated using field data, with twenty-one scenarios simulated to assess the effects of various autonomous vehicle behaviors at penetration rates from 25% to 100%, alongside human-driven vehicles. The results show that while cautious autonomous vehicles promote smoother traffic flow, they also result in longer delays and higher emissions due to conservative driving patterns, especially at higher penetration levels. In contrast, aggressive and platooning autonomous vehicles significantly improve traffic flow and reduce delays and emissions. Mixed-behavior scenarios reveal that different driving styles can coexist effectively, balancing safety and efficiency. These findings emphasize the need for optimized autonomous vehicle algorithms and signal control strategies to harness the potential benefits of autonomous vehicle integration in urban traffic systems fully, particularly in terms of improving traffic performance and sustainability. [ABSTRACT FROM AUTHOR]

Accurate prediction of tunnel boring machine (TBM) penetration rates is of great significance for intelligent TBM construction. Traditional empirical and theoretical models of TBM penetration rates are difficult to adapt to complex and changeable formation environments. To improve the adaptability, this paper proposes a TBM penetration rate prediction model based on the particle swarm optimization (PSO)-Elman algorithm fusion. Particle swarm optimization (PSO) was used to find the optimal connection weight matrix, which was inserted into the Elman network, and the TBM penetration rate was predicted by the machine learning method. This study examined field data from two distinct tunnel sections, focusing on their geological conditions, construction challenges, and environmental impacts. By analyzing the characteristics unique to these sites, the research offers a comparative perspective on tunnel engineering in diverse settings. Five parameters—uniaxial compressive strength (UCS), rock integrity index (Kv), cutter head thrust (Fn), cutter head speed (RPM), and penetration degree (P)—were selected as the input parameters. The TBM penetration rate was estimated by neural network training of the model. The results show that the PSO method effectively can overcome the problem of being prone to a local minimum using the single Elman method, and the PSO-Elman model has a fast convergence speed and high accuracy. In the 20 groups of experimental samples selected, the mean absolute percentage error (MAPE) was 3.38%, and the coefficient of determination (R2) was 0.936. The prediction quality was better than that of the single Elman method or the backpropagation neural network (BP) method. The study yields specific insights into efficient tunnel construction methodologies and practical neural network tools for risk management, highlighting innovative approaches in environmental preservation and safety enhancement in tunnel engineering. [ABSTRACT FROM AUTHOR]

Various models have been proposed to estimate the TBM penetration rate. Generally, the input parameters of these models can be divided into two categories: Machine parameters and geological engineering parameters. The engineering geological parameters will significantly influence the penetration rate if the machine operational parameters are kept within a reasonable near-optimal range. However, while some performance prediction models can be used for many common project settings, they have lower accuracy in certain applications. This study compared the observed penetration rate of a hard rock TBM with those predicted by MCSM, Norwegian University of Science and Technology (NTNU), Farrokh–Rostami, and Ramezanzadeh’s models in the Kerman water tunnel (KWT). Next, joint survey data are collected from the different zones of the KWT. For this purpose, a 3D discrete fracture network (DFN) model code was generated in Mathematica© version 13. The joint data’s orientation, persistence, and spacing were used to develop a 3D-DFN model for estimating the blockiness rate (BR) index. The BR index is the actual joint intensity in 2D (P21) and 3D (P32). In this study, the BR index is the newest rock mass parameter introduced and used to predict the penetration rate of TBM. This index can serve as a rock mass parameter that provides excellent and realistic results for predicting penetration rate (PR). The corresponding determination coefficient values of the PR with P32 and P21 are R2 = 0.96 and R2 = 0.98, respectively, and with CIA and UCS, are R2 = 0.42 and R2 = 0.49, respectively. Furthermore, using the DFN model showed its potential to be an accurate and reliable method for the overall estimation of the in-situ rock mass fragmentation, which highly controls the penetration rate of TBM. Research Highlights: The penetration rate of a hard rock TBM was compared with the NTNU, MCSM, Farrokh-Rostami, and Ramezanzadeh predictive models in the Kerman water tunnel (KWT). A 3D-Discrete fracture network (DFN) model code was generated. Four different zones of KWT were studied to discriminate rock mass parameters or intact rock parameters that affect the penetration rate of TBM. The BR index is the newest rock mass parameter that has been introduced and used to predict the penetration rate of TBM. The Blockiness Rate (BR) index as rock mass parameters has verified that it highly influences the penetration rate of TBM. [ABSTRACT FROM AUTHOR]

Abstract Accurate penetration rate prediction enhances rock-breaking efficiency and reduces disc cutter damage in tunnel boring machine (TBM) construction. However, this process faces significant challenges such as the high uncertainty of ground conditions and the complexity of maintaining optimal TBM operation in long and large tunnels. To address these challenges, we propose TCN-SENet++, a novel hybrid multistep real-time penetration rate prediction model that combines a temporal convolutional network (TCN) and a squeeze-and-excitation (SENet) block for aided tunneling. This study aims to demonstrate the application of TCN-SENet++, as well as other models such as RNN, LSTM, GRU, and TCN, for TBM penetration rate prediction. The model was developed using actual datasets collected from the Yin-Song diversion project. We employ a 30-s time step to predict the future time steps of the penetration rate (1st, 3rd, 5th, 7th, and 9th). The features that influence the penetration rate, such as the cutterhead torque, thrust, and cutterhead power, were considered. A comparative analysis using the mean absolute error and mean squared error revealed that the TCN-SENet++ model outperformed the other models, including RNN, LSTM, GRU, TCN, and TCN-SENet+. In comparison, TCN-SENet++ achieved average MSE reductions of 18%, 6%, 3%, 1%, and 2%, respectively. The TCN-SENet++ model demonstrated fewer errors in the new project, validating its effectiveness and suitability for real-time penetration rate prediction in TBM construction.

The sustainability of oil and gas well drilling operations is a vital issue from an economic and safety point of view. One of the practical methods to predict the continuation of the drilling operation is the analysis of the penetration rate of the drilling operations. Statistical and experimental studies have shown that the drilling depth, the drill weight, and the rotary table's rotation speed are the parameters that directly affect the penetration rate. However, due to their mutual influence on each other and the occurrence of the sticking-sliding phenomenon, predicting the penetration rate in the appropriate range is a complex and challenging issue. In this paper, using the field data of oil and gas wells located in the southwest of Iran, the effect of these parameters on each other was studied in the framework of statistical modeling, neural networks, and neural fuzzy systems. Using the ANFIS method and with a correlation coefficient of over 90%, a successful four-dimensional model of drill penetration rate was presented in terms of changes in drilling depth, the weight of the drill, and the rotational speed of the rotary table. By analyzing this model, it was found that the penetration rate of the drill decreases with the increase of the drilling depth, and to keep it within the acceptable range, the rotational speed and weight of the drill should be changed based on the presented policy.

One of the TBM performance prediction models that has been introduced in recent years and used for estimating penetration rate of TBM in hard rock conditions is the QTBM model. This model was introduced by Barton in the early 2000s. There have been many reviews of this model and the estimated penetration rates in various projects. This study was also conducted to check the validity of this model and propose modifications to models for different geological conditions. For this purpose, a database of TBM field performance along with geological information was prepared from 109 tunnel sections (with reliable geological and machine data). The results of calculated penetration rate by the QTBM model were compared with the field observation in the database. The results showed that this model offers low accuracy in predicting the machine penetration rate in different geological conditions. Subsequently, the relationship between input parameters of the QTBM model with the penetration rate was re-examined to see the impact of individual input parameters on the penetration rate of the machine. A new model is proposed, where the input parameters with lower impact on the penetration rate were removed and an attempt was made to obtain an empirical relationship between the more influential parameters and the field penetration index (FPI). New empirical relationships are more accurate in predicting machine penetration rates due to their simplicity and the need for fewer input data. [ABSTRACT FROM AUTHOR]

Predicting the drill penetration rate is a fundamental requirement in mining operations, profoundly impacting both the cost-effectiveness of mining activities and strategic mine planning. Given the intricate web of factors influencing rotary drilling performance, the necessity for advanced modeling techniques becomes evident. To this end, the hybrid extreme gradient boosting (XGBoost) was utilized to gauge the penetration rate of rotary drilling machines, utilizing random search, grid search, Harris Hawk optimization (HHO), and the dragonfly algorithm (DA) as metaheuristic algorithms. Our research draws from extensive data collected in copper mine case studies, encompassing both field and investigational data. This dataset incorporates critical material properties, such as tensile strength (TS), uniaxial compressive strength (UCS), as well as vital rock-mass characteristics including joint direction (JD), joint spacing (JS), and bit diameter (D). Our investigation evaluates the reliability of these prediction methods using various performance indicators, including mean absolute error (MAE), root mean square error (RMSE), average absolute relative error (AARE), and coefficient of determination (R2). The multivariate analysis reveals that the HHO-XGB model stands out, demonstrating superior prediction accuracy (MAE: 0.457; RMSE: 2.19; AARE: 2.29; R2: 0.993) compared to alternative models. Furthermore, our sensitivity analysis emphasizes the substantial impact of uniaxial compressive strength and tensile strength on the drill penetration rate. This underlines the importance of considering these material properties in mining operations. In conclusion, our research offers robust models for forecasting the penetration rate of similar rock formations, providing invaluable insights that can significantly enhance mining operations and planning processes. [ABSTRACT FROM AUTHOR]

Abstract The potential increase/decrease in energy consumption of traffic operation modes is a hot topic in the studies of connected and autonomous vehicles (CAVs), but there are few theoretical studies on the fuel consumption of human driving vehicles (HDVs) and CAV mixed platoon as a result of limited data. Based on the power distribution of light‐duty vehicles, this study developed a fuel consumption model for HDV and CAV mixed platoons by theoretical hypothesis considering the oscillation along with the platoon. Firstly, a vehicle‐specific power (VSP) distribution conforming to normal distribution is constructed with hypothesis‐based VSP standard deviation and constant VSP average value at each speed bin under different platoon arrangement conditions. Then, the fuel factors (mL/km) are calculated by weighted fuel rate (mL/s) according to the VSP distribution. Finally, a library of fuel consumption values for multiple penetration rates, platoon intensities, and speeds was established. In the case, at a speed of 30 km/h, the fuel consumption in the CAV group was reduced by 0.8–5%, 1.9–12.5%, 8.6–12%, and 12.4%, respectively, at the penetration rate of 20%, 50%, 80%, and 100%, compared to the HDV group. The fuel consumption reduction effect of CAV was negatively correlated with the speed.

The integration of autonomous vehicles into urban traffic systems offers a significant opportunity to improve traffic efficiency and safety at signalized intersections. This study provides a comprehensive evaluation of how different autonomous vehicle driving behaviors—cautious, normal, aggressive, and platooning—affect key traffic metrics, including queue lengths, travel times, vehicle delays, emissions, and fuel consumption. A four-leg signalized intersection in Balgat, Ankara, was modeled and validated using field data, with twenty-one scenarios simulated to assess the effects of various autonomous vehicle behaviors at penetration rates from 25% to 100%, alongside human-driven vehicles. The results show that while cautious autonomous vehicles promote smoother traffic flow, they also result in longer delays and higher emissions due to conservative driving patterns, especially at higher penetration levels. In contrast, aggressive and platooning autonomous vehicles significantly improve traffic flow and reduce delays and emissions. Mixed-behavior scenarios reveal that different driving styles can coexist effectively, balancing safety and efficiency. These findings emphasize the need for optimized autonomous vehicle algorithms and signal control strategies to harness the potential benefits of autonomous vehicle integration in urban traffic systems fully, particularly in terms of improving traffic performance and sustainability.

The potential increase/decrease in energy consumption of traffic operation modes is a hot topic in the studies of connected and autonomous vehicles (CAVs), but there are few theoretical studies on the fuel consumption of human driving vehicles (HDVs) and CAV mixed platoon as a result of limited data. Based on the power distribution of light‐duty vehicles, this study developed a fuel consumption model for HDV and CAV mixed platoons by theoretical hypothesis considering the oscillation along with the platoon. Firstly, a vehicle‐specific power (VSP) distribution conforming to normal distribution is constructed with hypothesis‐based VSP standard deviation and constant VSP average value at each speed bin under different platoon arrangement conditions. Then, the fuel factors (mL/km) are calculated by weighted fuel rate (mL/s) according to the VSP distribution. Finally, a library of fuel consumption values for multiple penetration rates, platoon intensities, and speeds was established. In the case, at a speed of 30 km/h, the fuel consumption in the CAV group was reduced by 0.8–5%, 1.9–12.5%, 8.6–12%, and 12.4%, respectively, at the penetration rate of 20%, 50%, 80%, and 100%, compared to the HDV group. The fuel consumption reduction effect of CAV was negatively correlated with the speed. [ABSTRACT FROM AUTHOR]

In the past few years, offshore site investigations have extensively utilized full-flow penetrometers like the Ball and T-bar penetrometers to assess penetration resistance and subsequently analyze the strength characteristics of marine clay. The relationship between penetration rate and the measured resistance to penetration and shear strength in clays has been extensively documented through full-flow penetration tests. Although previous studies have shown empirical correlations between undrained shear strength and penetration resistance, the resistance factor utilized in these correlations is typically suggested for cohesive soils that are overconsolidated or normally consolidated, rather than underconsolidated soils. The effects of penetration rate undrained penetration resistances in underconsolidated marine clay are investigated in this study by considering the outcomes of variable rate penetration testing and twitch penetration testing using full-flow penetrometers in laboratory model tests. The discussion focuses on penetration resistances depending on the normalized velocity of the full-flow penetrometers (Ball and T-bar). [ABSTRACT FROM AUTHOR]

A machine for tunnel boring machine (TBM) is recognized as productive equipment for tunnel construction. A dependable and precise tunnel boring machine's performance (such as penetration rate (ROP)) prediction could reduce the cost and help choose the suitable construction method. Hence, this research develops new integrated artificial intelligence methods, i.e., biogeography-based multilayer perceptron neural network (BMLP) and biogeography-based support vector regression (BSVR), to forecast TBMPR. Using the biogeography-based optimization (BBO) algorithm aims to improve the developed model's performance by determining the optimized neuron number of hidden layers for MLP models and the ideal values of the essential variables of SVR method. The results show that advanced methods can productively make a nonlinear relation among the ROP and its forecasters to obtain a satisfying forecast. Amongst the BMLP models with several hidden substrates, BM5L with five hidden substrates could attain the total ranking score (TRS) greatest rate, with root mean squared error (RMSE) and coefficient of determination (R2) equal to 0.017 and 0.9969. Simultaneously, the BSVR was the supreme model because of the fewer RMSE (0.00497 m/hr) and a larger R2 (0.999) compared with BMLP models. Overall, the acquired TRSs show that the BSVR outperforms the BMLP in terms of performance. As a consequence, the BSVR model may have been chosen as the suggested model if it had been able to accurately forecast the observed value even better than BM5L. [ABSTRACT FROM AUTHOR]

One of the most substantial issues faced during the excavation of deep tunnels in weak geomechanical conditions is the squeezing phenomenon. Thrust force determination of the shielded tunnel boring machines (TBMs) in such conditions is of utmost importance. In this study, the Beheshtabad water conveyance tunnel in the central part of Iran is chosen as a case study. The creep parameters of Burger's model (CVISC) are determined using laboratory creep tests on samples from the 19th zone of the tunnel with high squeezing potential, and the results were verified using finite difference simulation of the test. A new approach based on the convergence-confinement method is used to calculate the required thrust force of a double-shield TBM using radial displacement profile (RDP) and ground reaction curve (GRC). The effect of various penetration rates on thrust reduction as well as the impact of different over-boring values and standstill times has been evaluated. The results show that in small values of over-boring, due to high overburden pressure and weak geomechanical conditions, required thrust values are incredibly high and the increment of penetration rate is almost ineffective, and the negative effect of standstill time is more evident. Higher penetration rates have a significant impact on thrust reduction in all cases which is a noticeable factor to avoid TBM jamming under squeezing conditions. In higher over-boring values, thrust reduction is positively conducted through increasing the penetration rate and the negative effect of standstill time is minor. [ABSTRACT FROM AUTHOR]

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In mechanized tunneling, the ability to accurately predict the performance of the tunnel boring machine (TBM) is critical, because of its impacts on the project schedule and cost. A substantial amount of research has been conducted on predicting the penetration rate of TBMs in rocks but sometimes the actual cutting process of the disc cutter has been ignored, as most of the available models have been developed based on the assumption that the chipping process is the dominant mode of rock breakage. This might be true in many cases, but there are also situations where tensile fractures do not extend long enough to form chips, instead the tracks get deeper, and fines and rock powder are generated instead of chipping. This situation happens when the cutter load is insufficient to form chips, or so called sub-critical loading occurs. Two of the recent tunneling projects where subcritical cutter load has been the dominant mode are the southern extension of Tehran subway Line 6 (SEL6), and the southern lot of Kerman water conveyance tunnel (KrWCT). The issue in SEL6 was insufficient thrust of the machine and in KrWCT the high strength of the rock. Evaluation of the FPIs in SEL6 shows that the FPIs were less than 100 (kN/cutter/mm/rev) and the prediction errors of the existing models are low. However, in KrWCT project in rocks with higher strength and higher FPIs, the prediction errors are considerable. This indicates that the common models cannot offer an accurate estimate of the performance of TBM in subcritical loading in rocks with high strength. This study attempts to use statistical and machine learning methods to develop a new relation for TBM performance prediction in such conditions based on actual machine operating data and rock engineering geological features. New equations are introduced for the estimation of FPI in subcritical loading conditions to achieve more reliable and accurate prediction of TBM penetration rate. Highlights: Issues of hard rock TBM performance predictions in subcritical cutter load conditions. Development of a new performance prediction model for subcritical loading conditions using regression analysis and AI algorithms. Consideration of geological and geotechnical parameters and machine specification in model development. [ABSTRACT FROM AUTHOR]

• A probability distribution model of platoon size of connected vehicles is proposed. • A fundamental diagram model of the mixed traffic flow is developed. • An analytical model of the capacity of mixed traffic flow is proposed. • A numerical experiment is designed to discuss the impact of penetration rate and maximum platoon size on road capacity. This paper proposes a capacity analysis model for a mixed traffic flow environment that considers the heterogeneity and maximum platoon size of connected vehicles. Firstly, we explore how the organization mode of platoon affects car-following characteristics in mixed traffic flow with connected and automated vehicles, connected human-driven vehicles, automated vehicles, and human-driven vehicles. Different car-following models are used to describe the car-following characteristics of different types of vehicles. Secondly, the probability distribution model is developed for the size of platoons considering the penetration rate and the maximum platoon size of connected and automated vehicles, connected human-driven vehicles. Thirdly, the fundamental diagram of mixed traffic flow is further derived based on the probability distribution of platoon size and car-following models. Then, the capacity model of mixed traffic flow is proposed. Finally, a numerical experiment is designed to evaluate the impact of different penetration rates and maximum platoon sizes of connected and automated vehicles, connected human-driven vehicles on the capacity of the mixed traffic flow. The results show that (1) with a low penetration rate of connected and automated vehicles, connected human-driven vehicles, the change in the maximum platoon size has no noticeable effect on the distribution of platoon size of connected and automated vehicles, connected human-driven vehicles. (2) Road capacity increases with the maximum platoon size and the penetration rate of connected vehicles. (3) Compared with connected human-driven vehicles, the penetration rate of connected and automated vehicles has a more significant impact on road capacity. [ABSTRACT FROM AUTHOR]

In this study, the penetration rate of the excavation machine in Tabriz Metro Line 2 using geotechnical parameters and neural networks is estimated. For this purpose, through comprehensive analysis, including borehole drilling, field and laboratory tests, and consideration of similar projects, the geotechnical parameters for soil and rock layers have been determined. Preprocessing data techniques, such as normalization, have been applied to address challenges such as noise and bias in raw data. Also, neural networks with varying architectures were evaluated using mean square error and correlation coefficient as evaluation metrics. The architecture (1-12-8) of this research demonstrates superior performance with a mean square error of 1.630 and a correlation coefficient of 0.932. This shows a strong relationship between predicted and actual penetration rate values. The findings of this research highlight the effectiveness of neural networks in estimating the penetration rate. Accurate estimations of the non-linear penetration rate were achieved by employing a single-layer neural network with multiple neurons using appropriate transfer functions. Overall, this research contributes to the understanding of geotechnical considerations for urban train routes and demonstrates the accuracy of neural networks for penetration rate estimation. These insights have implications for the design and engineering of similar projects.

This article investigates the effect of ITO‐Cu gate structure on the penetration rate of LCD panels. Firstly, the ITO tail range is determined through finite difference time domain simulation. During the experiment, the water content during the ITO PVD film formation process is increased to improve the ITO single film penetration rate. In addition, due to the continuous Cu film formation process leading to substrate rise, ITO crystallizes prematurely and etching is difficult. This article obtains the ITO crystallization temperature through experiments, simultaneously controlling the thickness of Cu film can effectively avoid the problem of ITO crystallization. The experimental results show that the penetration rate of ITO/Cu structured TFT‐LCD is 3%~5% higher than that of Mo/Cu structured TFT‐LCD. [ABSTRACT FROM AUTHOR]

Reasonable and accurate prediction of TBM (tunnel boring machine) tunneling penetration rate is one of the key issues to realize TBM intelligent control. The uncertainty of small-diameter TBM construction in mixed face ground is stronger than conventional geological conditions, and traditional prediction methods do not consider the uncertainty of the construction process. The interval prediction method was introduced, and an interval prediction model of TBM penetration rate based on four different Bootstrap methods combined with the KELM-ANN model was proposed. Taking the Anyang water conveyance tunnel project of the South-to-North Water Diversion Project as an example, 142 sets of measured data were selected to verify the effectiveness of the interval prediction model. The results show that the prediction results of the model based on the Rademacher distribution are better than those of the other three methods. Not only can better point prediction results be obtained, but also a clearer and more reliable interval can be constructed to completely enclose the measured value of the penetration rate; With the improvement of the confidence level, the uncertainty and risk that can be accommodated in the interval also gradually increases. By changing the width of the interval, the impact of the uncertainty factors in the TBM construction process on the penetration rate can be better quantified and explained. The results can provide reference for TBM tunnel performance prediction and tunnel parameter optimization. [ABSTRACT FROM AUTHOR]

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This study employs a supervised learning method to predict the tunnel boring machine (TBM) penetration rate (PR) with high accuracy. To this end, the extreme gradient boosting (XGBoost) model is optimized based on two swarm intelligence algorithms, i.e., the sparrow search algorithm (SSA) and the whale optimization algorithm (WOA). Three other machine learning models, including random forest (RF), support vector machine (SVM), and artificial neural network (ANN) models, are also developed as the drawback. A database created in Shenzhen (China), comprising 503 entries and featuring 10 input variables and 1 output variable, was utilized to train and test the prediction models. The model development results indicate that the use of SSA and WOA has the potential to improve the XGBoost model performance in predicting the TBM performance. The performance evaluation results show that the proposed WOA-XGBoost model has achieved the most satisfactory performance by resulting in the most reliable prediction accuracy of the four performance indices. This research serves as a compelling illustration of how combined approaches, such as supervised learning methods and swarm intelligence algorithms, can enhance TBM prediction performance and can provide a reference when solving other related engineering problems. [ABSTRACT FROM AUTHOR]

The proper functioning of the gastrointestinal tract plays an important role in strengthening the immune system. It is an undeniable fact that lactic acid microorganisms are necessary for the proper functioning of the gastrointestinal tract, the source of which are mainly dairy products. However, there is a problem with the digestibility of lactose; therefore, alternative sources and carriers of probiotics are of particular interest. Due to its dietary and natural properties, protein marshmallow can serve as such a carrier. Therefore, the direction of this study is to identify the dependence of technological factors on the rheological properties of the product and the growth of lactic acid microorganisms in confectionery products enriched with lyophilised strains. According to the results of the study, the following was determined: the optimal technology to produce enriched Belevskaya pastille with a mixture of Lactobacillus acidophilus makes it possible to obtain a product with the necessary rheological properties, utilising a mass drying mode in a dehydrator at 50 °C for 16 h. The strains L. acidophilus M3 and L. acidophilus M4 were the most resistant to a high concentration of bile (40%) in the substrate. Based on the analysis of variance and the obtained regression equations, it was revealed that the growth of lactic acid microorganisms in the product was strongly influenced by the amount of ferment introduced (R² = 0.96). The level of penetration is influenced by factors such as the amount of probiotic starter introduced, the drying time and the interaction of drying time factors on the amount of starter added. The higher the level of penetration, the crumblier the product. The resulting functional product can be characterized as symbiotic since the main raw material of plant origin contains a large amount of fibre, which acts as a prebiotic, and the strain of microorganism, which acts as a probiotic. The data described in the article can be applied in the technological processes of similar products to regulate the structure of the product and vary the dosage of enrichment with probiotic starter cultures. [ABSTRACT FROM AUTHOR]

The mechanical and chemical characteristics of concrete undergo changes in extreme environmental conditions, which often reduce the strength and durability of concrete. Therefore, the use of additives that increase the reliability of concrete in acute conditions is required. It is also important to use tests that are able to measure the permeability and resistance of concrete both in situ and in the laboratory without damaging the concrete. Therefore, it was decided to investigate the effect of some additives on the durability of concrete, which are recently widely used in Iran. There is not much research on the effect of these materials on the durability and reliability of concrete in extreme environmental conditions. In this research, by using in-situ cylindrical chamber and twist-off tests, the effect of different cycles of freezing and thawing on the permeability and surface resistance of ordinary concretes and concretes was evaluated. Contains additives. Contrary to DIN 1048 and BS EN 12390-8 standards, which require breaking concrete to measure permeability, the volume and depth of water penetration can be measured in the cylindrical chamber test without damaging the concrete. The obtained results show that by using the new cylindrical chamber test, the penetration depth of concrete can be measured with high accuracy without the need to break the concrete sample. Also, about 78% decrease in the strength of the surface layer of concrete occurred in the first 50 cycles and the remaining 22% occurred in the next 100 cycles. The permeability of concrete containing microsilica gel and supergel was calculated to be about 50% and 30% lower than the permeability of normal concrete, respectively. [ABSTRACT FROM AUTHOR]

As massive tunneling projects become more and more popular, predicting the performance of Tunnel Boring Machine (TBM) has been a problem that arose recently. A TBM is a modern piece of machinery that is specially assembled to excavate a tunnel more efficiently and safely. However, the performance of TBM is very difficult to estimate due to the different geological formations and geotechnical factors. This research aims to predict the penetration rate (PR) of TBM utilizing statistical and artificial intelligence methods that are based on the rock mass and rock material properties: rock mass rating, rock quality designation, and rock strength. To achieve this goal, we used two neural network-based models: artificial neural network (ANN) and group method of data handling (GMDH), to forecast the TBM PR values. Then, we compared the performance of these two models using the well-known indices and a ranking system and selected the model with the highest degree of performance. As a result, an ANN model with one hidden layer and seven neurons showed the highest level of capability in predicting TBM PR. Correlation coefficient values of 0.947 and 0.921 for the training and testing phases, respectively, were obtained for the best model in this study. Our research can serve as a fundamental study for future geotechnical engineers or researchers who would like to predict TBM performance with similar rock mass and material properties to this study.

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Copyright of Journal of Agricultural Economics & Social Sciences is the property of Egyptian National Agricultural Library (ENAL) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Tsunami occurs frequently all over the world in recent years, which has caused serious damage to coastal buildings and infrastructure. Therefore, it is of great significance to research on measures for onshore buildings against tsunamis. In this study, the multi-material ALE method is used to establish a numerical model for air-tsunami-structure interactions, and the influence of multistory building忆s penetration form and rate on tsunami impact load is investigated. The numerical results show that tsunami impact load is significantly affected by the penetration rate of a building when the tsunami is relatively weak, and the tsunami impact load decreases with the increase of the penetration rate. When a tsunami exceeds a certain strength, the effect of reduction of the tsunami impact loads on buildings becomes weaker with the increase of penetration rate when the perforation is uniformly arranged. However, the effect of reduction of tsunami impact loads on buildings will remain significant when the lower part of a building is completely open, and the tsunami impact load can be reduced to about 20% of that on impervious buildings. In addition, the results also show that the water runup when tsunami impacts a building decreases with the increase of the penetration rate, and the bow wave generated by reflection in front of the building is meanwhile weakened. [ABSTRACT FROM AUTHOR]

Abstract Background The availability of private vehicles with autonomous features is widespread nowadays. Various car manufacturers are providing attributes like collision warning, city automatic emergency braking, adaptive cruise control, pedestrian detection, lane-keeping assistance and lane departure warning, rear cross-traffic and blind-spot warning in their high-end models. Purpose Such features can automatically manage the macroscopic fundamental traffic parameters such as speed, headway, etc adaptively. Consequently leading to a heterogeneous traffic stream with diverse car-following behaviour comprising completely manual/traditional (TVs) and autonomous vehicles (AVs). This questions the applicability of classic traffic flow theory relationships on such heterogeneous traffic streams. Methodology This paper focuses on developing the macroscopic fundamental diagram for such heterogeneous traffic streams based on the quantified penetration rate (QPR) for autonomous vehicles. The penetration rate is devised by taking into account user demographics, land usage and road network properties. QPR is used as an input for heterogeneous urban traffic stream scenarios to calculate the aggregated urban traffic network dynamics of flow and density for the same network. Travel time versus flow characteristics is evaluated based on calibrated hyperbolic urban link travel time function for both interrupted and uninterrupted flows following the aggregated speed and density output from MFDs for heterogeneous traffic streams. Also, two scenarios are generated for comparison to explain the improvement in the network characteristics together with a sensitivity analysis. Results Compared to the base scenario there could be 25-35% of AVs on the road networks based on the analysis in coming fifteen years. This increment in usage impacts the capacity of road networks positively by increasing it up to 59%. Conclusions Results obtained after the application of the suggested model approach to the real network can be used to define a realistic method for multi-vehicle equilibrium assignment models for heterogeneous traffic streams including autonomous vehicles instead of approximating the penetration rates.

Penetration rate is one of the most important parameters in determining excavation time in tunnelling operations. Providing a prediction model or a mathematical relationship can give a better understanding of this issue. A mathematical equation between input and output parameters can be optimized by using algorithms such as Particle Swarm Optimization and Firefly. Since drilling operations interact between ground and machine, therefore, the effective parameters on the penetration rate are divided into two general categories such as machine and geological factors. Effective geological factors include internal friction angle, cohesion, specific gravity, shear modulus and groundwater level. In addition, the important parameters of TBM are torque, thrust jacks, and rotation speed. By defining an initial mathematical function, two optimization algorithms, which look for the most optimal mode, the goal here is the same as the mean square error (MSE). Finally, by examining and comparing the performance of two algorithms, using the coefficient of determination and the mean square error, it found that the Firefly algorithm has a better performance than the Particle Swarm Optimization algorithm.

In recent years, improving intersection traffic safety has become a major focus for researchers. However, it remains a significant concern due to the increasing number of vehicles and the introduction of autonomous and cooperative driving systems. Advancements in artificial intelligence (AI) and vehicle-to-everything (V2X) technologies offer promising solutions to reduce collisions between vehicles. As V2X technologies are slowly being integrated into traffic safety systems, questions arise about their impact on safety effectiveness. This research area is relatively unexplored but has potential to enhance intersection safety. In this paper, we introduce the Intersection Traffic Safety Framework (ITSF), a safety-oriented system devised to mitigate collisions at road intersections. This framework achieves this through the implementation of a collision avoidance mechanism that harnesses vehicle-to-infrastructure (V2I) communications. Additionally, it incorporates a machine learning model tasked with distinguishing between vehicles posing a risk and those that do not, as part of the collision avoidance process. Furthermore, this work assesses the performance of this framework by considering critical factors like the penetration rate of V2X-enabled vehicles, end-to-end latency, and the responsiveness of drivers to safety alerts. The proposed approach proves highly effective in ensuring road users’ safety with a penetration rate of 60% or higher, resulting in a significant reduction in collisions and nearly 98% accuracy in classifying risky vehicles. Additionally, the algorithm remains successful even when some drivers neglect warnings, showcasing its robustness in minimizing collision rates.

The performance of mechanized excavation depends on the soil abrasivity and the resistance of cutting tools against wear. The wear has a negative effect on excavation machine parameters, such as penetration rate, and reduces the machine's efficiency. Worn tools require replacement, which interrupts the project and incurs high maintenance costs. For this reason, many efforts have been made to understand the interaction between soil and cutting tools. Various wear-measuring devices have been designed and built to measure soil abrasivity and cutting tool wear. In this research, to study the mechanism of tunnel excavation in the laboratory, a tunnel boring machine laboratory simulator was designed and built, and the effect of the operating parameters of the excavation machine on the average wear of cutting tools was studied. The features of this machine are its horizontal excavation, the low rotation speed of the cutterhead, continuous contact of the cutters with fresh soil during the test, and continuous injection of materials with a specific injection pressure during the test. Using the granulation of soil prepared from Tabriz metro line 2 in three moisture content of zero, 7, and 13%, the effect of the rotation speed of the cutterhead, rotation time, and penetration rate on the wear of cutting tools was studied. The investigations showed that with the increase in the rotation speed of the cutterhead, the average wear increases. Also, the increasing rotation time has caused more friction between the cutting tools and the cutterhead with the soil, and the wear has increased. The wear decreased with the increase in the penetration rate, despite the increase in the intensity of the conflict between the soil particles and the cutting tools. The results obtained from this research by using a tunnel boring machine laboratory simulator are in good agreement with previous studies.

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 graphene 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.

Purpose. One of the most common problems in mechanized excavation is the cutting tool wear, which has a great impact on the final cost of the project and its duration. Also, one of the most important factors affecting the wear of cutting tools is the operating parameters of the tunnel boring machine (TBM). Within the framework of this research, a tunnel boring machine laboratory simulator was designed and constructed to investigate the tunnel excavation process in the laboratory. Methods. A few of the features of this device are that it operates horizontally, has a low rotation speed, keeps the pins in contact with fresh soil throughout the test, and has the possibility of measuring the torque of the device during the test. A study of the cutting tool wear was conducted using granulation prepared from Tabriz metro line 2, as well as using operating parameters of mechanized excavation machines, such as penetration rate and cutter head rotation speed. Findings. The research results showed that by reducing the rotation speed of the cutter head from 35 to 10 rpm, the average wear of cutting tools is reduced by 63%. Also, by reducing the excavation time from 80 to 10 minutes, the cutting tool wear is reduced by 58%. The wear of cutting tool increases with increasing moisture content from 0 to 10%, and then decreases with increasing moisture content from 10 to 25%. Originality. During this research, a new device was designed and built to simulate tunnel excavation mechanisms. This laboratory simulator measures wear percentage, penetration rate and torque. Practical implications. There has been significant progress in predicting soil abrasion rates, but there are few accepted models for predicting cutting tool wear and soil abrasion rates. During the design and construction of the tunnel boring machine laboratory simulator, the effect of operating parameters on wear of cutting tool was examined. [ABSTRACT FROM AUTHOR]

In the past few years, offshore site investigations have extensively utilized full-flow penetrometers like the Ball and T-bar penetrometers to assess penetration resistance and subsequently analyze the strength characteristics of marine clay. The relationship between penetration rate and the measured resistance to penetration and shear strength in clays has been extensively documented through full-flow penetration tests. Although previous studies have shown empirical correlations between undrained shear strength and penetration resistance, the resistance factor utilized in these correlations is typically suggested for cohesive soils that are overconsolidated or normally consolidated, rather than underconsolidated soils. The effects of penetration rate undrained penetration resistances in underconsolidated marine clay are investigated in this study by considering the outcomes of variable rate penetration testing and twitch penetration testing using full-flow penetrometers in laboratory model tests. The discussion focuses on penetration resistances depending on the normalized velocity of the full-flow penetrometers (Ball and T-bar).

A power balance control method is proposed for renewable energy source (RES) integrated power systems based on deep reinforcement learning (DRL), with the reasonable utilization rate of renewable energy optimized. This method considers the dispatching problems of a high-proportion renewable energy grid from a new perspective. It is proposed that the dispatching of a high-proportion renewable energy grid must consider the reasonable utilization rate of renewable energy and conduct reasonable abandonment of wind and light. And in the offline training of DRL scheduling, the reasonable utilization rate is used as the element of the state vector to train the final power grid DRL control strategy. The control strategy has verified its effectiveness in the IEEE 14-bus system with the supporting datasets.

Background: The availability of private vehicles with autonomous features is widespread nowadays. Various car manufacturers are providing attributes like collision warning, city automatic emergency braking, adaptive cruise control, pedestrian detection, lane-keeping assistance and lane departure warning, rear cross-traffic and blind-spot warning in their high-end models. Purpose: Such features can automatically manage the macroscopic fundamental traffic parameters such as speed, headway, etc adaptively. Consequently leading to a heterogeneous traffic stream with diverse car-following behaviour comprising completely manual/traditional (TVs) and autonomous vehicles (AVs). This questions the applicability of classic traffic flow theory relationships on such heterogeneous traffic streams. Methodology: This paper focuses on developing the macroscopic fundamental diagram for such heterogeneous traffic streams based on the quantified penetration rate (QPR) for autonomous vehicles. The penetration rate is devised by taking into account user demographics, land usage and road network properties. QPR is used as an input for heterogeneous urban traffic stream scenarios to calculate the aggregated urban traffic network dynamics of flow and density for the same network. Travel time versus flow characteristics is evaluated based on calibrated hyperbolic urban link travel time function for both interrupted and uninterrupted flows following the aggregated speed and density output from MFDs for heterogeneous traffic streams. Also, two scenarios are generated for comparison to explain the improvement in the network characteristics together with a sensitivity analysis. Results: Compared to the base scenario there could be 25-35% of AVs on the road networks based on the analysis in coming fifteen years. This increment in usage impacts the capacity of road networks positively by increasing it up to 59%. Conclusions: Results obtained after the application of the suggested model approach to the real network can be used to define a realistic method for multi-vehicle equilibrium assignment models for heterogeneous traffic streams including autonomous vehicles instead of approximating the penetration rates. [ABSTRACT FROM AUTHOR]

Scientific prediction of the penetration rate (PR) of tunnel boring machines (TBMs) is of great significance to the selection of tunnel construction methods, the prediction of construction progress, and the evaluation of benefits. In view of the high nonlinearity, fuzziness, and complexity of the TBM excavation process, this paper attempts to present a new algorithm that integrates partial least squares regression (PLSR) with boosted regression trees (BRT) for predicting the PR of TBMs. For this purpose, 150 datasets were obtained from the Lanzhou water conveyance tunnel project in China. Six impact factors were selected as the input layers of the proposed model by single-factor correlation analysis. To develop the PLSR-BRT model, two principal components of the influencing parameters were extracted via the PLSR method, and the BRT algorithm was employed to establish the predictive model. In addition, other models such as PLSR back propagation neural network (BPNN), BRT, PLSR, support vector regression (SVR), and artificial neural network (ANN) were adapted for use in this problem to verify the predictive accuracy of the proposed model. The results demonstrated that the PLSR-BRT model achieved the best predictive performance compared with the other models, with coefficient of determination value (R2) and root mean square error (RMSE) of 0.96 and 1.78, respectively, for the testing set. The PLSR-BRT model can maximally avoid both overfitting and inadequate fitting. In view of engineering applications, the complexity and redundancy of the field dataset can be solved via reducing the dimensionality of input parameters, and the training samples could be expanded by a boosted method so that the discontinuously acquired geological parameters are unable to restrain the model performance. This method serves as an effective approach for TBM PR prediction and has excellent potential for a variety of scientific and engineering applications. [ABSTRACT FROM AUTHOR]