Actual Speed Research Articles

Innovative real-time deloading approach to wind systems with applications in primary frequency control

The integration of wind systems into the electrical grid faces a significant challenge due to the absence of inertia, a fundamental characteristic of this system. This lack of internal inertia directly impacts frequency stability. To address this challenge, various techniques have been developed, including storage systems, inertia emulation, and deloading methods, among others. Among these approaches, the latter method appears to be more suitable. This study introduces a novel deloading approach that offers several advantages. The proposed strategy is an online technique that is easy to implement. It does not necessitate mathematical models, estimations, prior knowledge of wind turbine (WT) characteristics, or the use of an extra sensor, thereby simplifying the approach. This approach is based on a WT cycle consisting of two consecutive operating modes: a maximum power measurement (MPM) mode and a power reserve (PR) mode. These modes are controlled by two controllers: a pitch angle controller (PAC) and a perturb and observe (P&O) algorithm controller. The cycle is managed and initiated using a monitoring signal with multiple configurations, providing various solutions and implementation possibilities. The effectiveness of this approach has been verified through numerous simulation tests conducted on both finite and isolated grids. These simulations employ theoretical and actual wind speed signals in the MATLAB Simulink environment, confirming the practicality and reliability of the proposed method.

The role of posted speed limit on pedestrian and bicycle injury severities: An investigation into systematic and unobserved heterogeneities

The posted speed limit, as a proxy of actual speed, is one of the most fundamental predictors of active travelers’ (pedestrian and bicyclist) injury outcomes when involved in crashes with motor vehicles. Although earlier studies predominantly considered posted speed limit as an exogenous variable and provided highly insightful findings, majorities of them assume the effects of active traveler behavior to remain the same across different posted speed limits, which in turn neglect the heterogeneity in active traveler behaviors on high-speed roads vs. low-speed roads. This study proposes to develop a latent segmentation-based active traveler injury severity model to relax the homogeneity assumption of the posted speed limit by active traveler behavior. Specifically, this study proposes to estimate a latent segmentation-based correlated random parameters generalized ordered logit model to examine active travel injury severity mechanisms. The proposed model accommodates systematic heterogeneity in the effects of posted speed limit, crash year and active traveler group by using a piecewise linear function in injury severity component of the latent segment model. The model is demonstrated by using active traveler crash data from Queensland, Australia, for the years 2015 through 2019. To demonstrate the implications of the estimated models, a number of hypothetical scenario analyses are performed with a specific focus on active traveler behavior and reduction in posted speed limits. The outcomes from the hypothetical scenario analysis highlighted that a 76 % (73 %) reduction in active traveler fatalities can be achieved by converting 50–60 km/hr roadways to 10–40 km/hr roadways in the urban areas (rural areas) of Queensland. The outcomes of the study will allow us to identify effective speed management strategies while targeting those with high-risk behavior.

A Short-Term Wind Speed Forecasting Framework Coupling a Maximum Information Coefficient, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Shared Weight Gated Memory Network with Improved Northern Goshawk Optimization for Numerical Weather Prediction Correction

In line with global carbon-neutral policies, wind power generation has received widespread public attention, which can enhance the security of supply and social sustainability. Since wind with non-stationarity and randomness makes power systems unstable, precise wind speed forecasting is an integral part of wind farm scheduling and management. Therefore, a compound short-term wind speed forecasting framework based on numerical weather prediction (NWP) is proposed coupling a maximum information coefficient (MIC), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), shared weight gated memory network (SWGMN) with improved northern goshawk optimization (INGO). Firstly, numerical weather prediction is adopted to acquire the predicted variables with different domains, including the predicted wind speed and other predicted meteorological variables, after which the error is calculated using the predicted and actual wind speeds. Then, the correlation between the predicted variables and the error is obtained using the MIC to select the correlation factors. Subsequently, CEEMDAN is employed to decompose the correlation factors, corresponding the actual factors and the error into a series of subsequences, which are regarded as the input series. After that, the input series is fed into the proposed SWGMN to forecast each subsequent error, respectively, in which the shared gate is proposed to replace the input gate, the forgetting gate and the output gate. Meanwhile, the proposed INGO based on northern goshawk optimization (NGO), the levy flight disturbance strategy and the nonlinear contraction strategy is applied to calibrate the parameters of the SWGMN. Finally, the forecasting values are acquired by summing the forecasted error and the predicted wind speed from the NWP. The experimental results depict that the errors are small among all the models. Compared with the traditional method, the proposed framework achieves higher prediction accuracy and efficiency. The application of this framework not only assists in optimizing the operation and management of wind farms, but also reduces the dependence on fossil fuels, thereby promoting environmental protection and the sustainable use of resources.

Centerline measurement of rotary kilns based on the free station method

Abstract The rotary kiln is an important production equipment in the building materials and metallurgy industry, and its operating conditions, safety, and efficiency largely determine the economic benefits of its users and the safety of workers on site. At present, the testing methods for each rotary kiln part are relatively mature and can be used to realize accurate measurements in a dynamic environment. However, the main means of measurement is limited by the site conditions, and the actual detection speed is slow; the preparation work is cumbersome and will be subject to the measurement of the site obstacles and other environmental factors constraints. In this paper, we propose and design an optimized rotary kiln centerline measurement method using the free station method and verify its feasibility with respect to the optimization of the centerline offset, which is an important measurement item in rotary kiln measurement.

Predicting urban signal-controlled intersection congestion events using spatio-temporal neural point process

ABSTRACT The urban traffic signal-controlled intersections are of great significance for solving the problem of urban road congestion. Previous research on congestion prediction mainly aggregated data at the level of road segments or traffic flow at a coarse regulated time interval. Fine-grained prediction of congestion events at the lane-level and cycle-level enables detailed a understanding of spatio-temporal dependencies, leading to congestion reduction, improved efficiency. This paper presents a Spatio-Temporal Neural Point Process (STNPP) model that combines Graph Neural Networks and Neural Temporal Point Process to predict congestion events at urban intersections. The proposed model allows for complete prediction of congestion events, including their occurrence, development, dissipation. In the process of spatial correlation modeling, graph neural networks are used to model the spatial relationships between both region and intersections. The current intersection and its upstream/downstream areas are modeled separately. To model the temporal correlations at individual intersections, we focus on a specific lane and capture the evolution of congestion events using the Neural Point Process Gated Recurrent Unit (NPPGRU), which captures the temporal granularity changes of signal-controlled cycles in congestion events. Using actual traffic speed and signal-controlled data from Hangzhou city, we validate that the proposed method achieves stable predictive performance.

The Impact of Pedestrian Lane Formation by Obstacles on Fire Evacuation Efficiency in the Presence of Unfair Competition

After a fire breaks out, pedestrians simultaneously move towards the exit and quickly form a crowded area near the exit. With the intensification of pedestrians’ tendencies towards unfair competition, there is an increase in pushing and collisions within the crowd. The possibility of stampedes within the crowd also gradually increases. Analyzing the causes and psychological tendencies behind pedestrian pushing and collisions has a positive effect on reducing crowd instability and improving evacuation efficiency. This research proposes a modified social force model considering the unfair competition tendency of pedestrians. The model considers factors such as the gap between pedestrians’ actual and maximum achievable speed, effective radius, and their distance from the exit. In order to overcome the shortage of “deadlock” in the classical social force model in a high-density environment, this research introduces the feature of variable pedestrian effective radius. The effective radius of pedestrians dynamically changes according to the density of the surrounding crowd and queuing time. Through validation, the evacuation efficiency of this model aligns well with the actual situation and effectively reflects pedestrians’ pushing and squeezing behaviors in high-density environments. This research also analyzes how to strategically arrange obstacles to mitigate the exacerbating effect of unfair pedestrian competition on exit congestion. Five experiments were conducted to analyze how the relative position of obstacles and exits, the number of evacuation paths, and the size of the obstacle-free area before the exit affect evacuation efficiency in the presence of unfair pedestrian competition. The results show that evacuation efficiency can be improved when obstacles play a role in guiding or reducing the interaction of pedestrians in different queues. However, when obstacles hinder pedestrians, the evacuation efficiency is reduced to a certain extent.

Experimental and three-dimensional performance analysis of a bio-inspired fish-ridge vertical axis wind rotor

The savonius vertical axis wind rotor has a simple structure, making it a suitable choice for power generation in small-scale, low wind speed environments. However, its wind energy conversion efficiency is low. To improve the conversion efficiency of the wind rotor using the proposed fish-bridge structure, the wind tunnel tests are conducted to collect actual wind and rotational speeds. In addition, Computational Fluid Dynamics (CFD) simulation is conducted to reveal potential operating mechanisms, with special emphasis on analyzing the effects of aspect ratio and end plate on the performance of the fish-ridge wind rotor. The CFD results show that the wind power coefficient of this wind rotor presents an increasing trend with the increase of the aspect ratio and the area of the end plate, and that with round end plates is improved by 24.26 % compared to that of the wind rotor without end plates, and that with an aspect ratio of 3.5 is improved by 32.58 % compared to that of the wind rotor with an aspect ratio of 1. By increasing the aspect ratio and the end plate area of the wind rotor, the contact airflow is raised and concentrated, which improves the aerodynamic performance of the wind rotor.

Priestly-Taylor Coefficient Evaluation for Konya Closed Basin

Measurement of evaporation in the field is difficult and expensive; thus, the empirical evaporation estimation methods have been developed. However, these estimation methods have both advantages and disadvantages. The main disadvantage is that their coefficients were determined by the climatic conditions of the study areas. One of these methods is Penman. The Penman method, accepted as a reference, has reached the closest estimations to the measurement of evaporation in the field of the different parts of the world. However, it needs lots of measured climatic data. The Priestley-Taylor method was derived to reduce the measured data needs of the Penman method. Priestly and Taylor represented the variables such as saturated and actual vapor pressures and wind speed with coefficient of 1.26. The researchers have continued to study on the calibration of the  coefficient for their studies’ area since this method has been known to underestimate evaporation value in areas where advection is effective. The present study consists of two stages. First, evaporation was tried to be estimated with these two methods by using the measured climatic data of five meteorological stations in the Konya Closed Basin. Estimated values were evaluated making comparison with the pan measurements. Although slightly higher values were estimated from the pan measurements with each method, the Penman method was found to be relatively more consistent on the basis of statistical indicators. Second,  coefficient was obtained as 1.28 for the study area by using three artificial intelligence-based optimization algorithms. The Penman method was used for comparison in this stage. It was concluded that there was no need for any calibration of the  coefficient and the original one was found to be valid for the study area as well.

Robust Control of Motion in Presence of Uncertain Parameters and Control Constraints

The paper describes a novel, simple servo drive position controller, using only knowledge about the structure of the nonlinear model and the constraints met by individual components of the model. The desired behaviour of the position and velocity signals is obtained by imposing a time-varying constraint on the signal aggregating information about position and velocity tracking errors. The method allows you to determine the maximum control (servo drive current) necessary to achieve the control goal under the existing initial conditions and the selected reference trajectory. The control is constrained and consists in appropriate reaction when the trajectory approaches the barrier, the shape of which is responsible for the imposed properties of the transient and quasisteady state tracking error. In addition to the derivation of the control, a discussion of its possible variants and basic properties is presented. Control with time-varying constraints has been introduced, which allows the control objectives to be met with limited conservatism of the imposed constraints. The influence of technical factors related to actual speed and position measurements was discussed and the operation of the real drive on a laboratory stand was presented.

Single mask and low voltage electrothermal micromotor

This paper introduces a single-mask-and-low-voltage electrothermal micromotor driven by four symmetrical V-shaped actuators and ratchet mechanism. The 2.5 mm-diameter micromotor has been fabricated successfully by using silicon on insulator (SOI) wafers and SOI-MEMS technology. The play of micromotor is tested under microscope and indicated that it operates quite smoothly. The actual rotation speeds (rpm) of the micromotor are examined with the driving frequencies range from 1 Hz to 15 Hz at a driving voltage of 12.5 V and compared with the calculation. Experimental results show that the theoretical and measured angular speeds are quite close while the applied frequencies are lower or equal to 10 Hz.

Detailed modelling and performance analysis of power flow topology in a hybrid electric vehicle having series-parallel architecture

Despite massive investment and carbon-neutral transition goals set around the world, gasoline-based internal combustion engine vehicles still form an absolute majority in the transportation sector. With the advent of technology, access to electricity, uncertainties in fuel prices and health awareness, people worldwide are moving towards a better, reliable, cost-effective and environmentally friendly mode of transportation, a hybrid electric vehicle. Such a vehicle, with its powerful electric motor and compact gasoline-based engine, offers better efficiency in terms of operating cost and reliability. Considering the advent of hybrid electric vehicles taking pace, studies related to its architecture types, power flow dynamics, control and modelling of its various components will form an essential part of the automobile industry and research. This paper proposes a power flow topology for a hybrid electric vehicle with series-parallel architecture. The developed vehicle model with such an architecture type consists of three main sub-systems: the electrical system, the control system and the mechanical system. The presented power flow topology being modelled and analysed in detail in the Simulink tool is being implemented via a mode logic controller, which forms part of the central control system. The developed hybrid electric vehicle model demonstrates various modes of operation, from starting to accelerating to de-accelerating and then finally coming to a complete rest. Each mode yields and explains the following: the vehicle reference speed; the engine and generator turn functions on/off; the dc bus and battery voltage; the motor, battery, and generator current; the motor, generator, and engine speed; engine torque; engine power; throttle demand; and the vehicle’s actual speed. The results thus obtained show that the waveforms associated with such topology, during its various modes of operation, are pretty stable and acceptable, thereby depicting and validating the operation of the developed hybrid electric vehicle model with proposed power flow topology in a precise and transparent manner.

Multi-level optimal energy management strategy for a grid tied microgrid considering uncertainty in weather conditions and load

Microgrids require efficient energy management systems to optimize the operation of microgrid sources and achieve economic efficiency. Bi-level energy management model is proposed in this paper to minimize the operational cost of a grid-tied microgrid under load variations and uncertainties in renewable sources while satisfying the various technical constraints. The first level is day ahead scheduling of generation units based on day ahead forecasting of renewable energy sources and load demand. In this paper, a recent meta-heuristic algorithm called Coronavirus Herd Immunity Optimizer (CHIO) is used to solve the problem of day-ahead scheduling of batteries, which is a complex constrained non-linear optimization problem, while the Lagrange multiplier method is used to determine the set-point of the Diesel Generator (DG). The second level of the proposed EMS is rescheduling and updating the set-points of sources in real-time according to the actual solar irradiance, wind speed, load, and grid tariff. In this paper, a novel real-time strategy is proposed to keep the economic operation during real-time under uncertainties. The obtained results show that the CHIO-based bi-level EMS demonstrates an optimal economic operation for a grid-connected microgrid in real-time when there are uncertainties in weather, utility tariffs, and load forecasts.

A novel strategy for real-time optimal scheduling of grid-tied microgrid considering load management and uncertainties

This paper proposes an efficient bi-level energy management strategy (EMS) to optimize the operation cost of a grid-connected microgrid, considering the system operational constraints and uncertainties for renewable energy sources and load demand. The first level is optimal day-ahead scheduling based on two stages: the first stage is finding the optimal operating points of sources during the next day while the second one is controllable loads management. The second level of the proposed EMS is rescheduling and updating the set-points of sources in real-time according to the actual solar irradiance, wind speed, load, and grid tariff. In this paper, a novel real-time strategy is proposed to keep the economic operation during real-time under uncertainties. Also, a recent meta-heuristic algorithm called Honey Badger Algorithm (HBA) is used to solve the problem of day-ahead scheduling of batteries, which is a complex constrained non-linear optimization problem. Results obtained demonstrate that the HBA based bi-level EMS provides the real-time optimal economic operation of a grid tied microgrid under uncertainties in weather, utility tariff and load forecasts.

An unexpected digital divide? A look at internet speeds and socioeconomic groups

As societies and economies continue to digitize, the need to understand the digital divide beyond binary availability or adoption metrics is increasing. Understanding the quality and performance of home internet—measured by internet speeds—among different socioeconomic groups contributes to the complex digital equity landscape at a time when all U.S. states and territories are completing digital equity plans. Using 2021 speed test data from Ookla, this study conducts spatial regressions at the census tract level to find that most of the sociodemographic factors associated with lower home internet adoption rates (e.g., rural, poor, older) remain when looking at actual internet speeds. However, an unexpected finding—supported by emerging research—that White non-Hispanic residents are using the internet at slower speeds compared to the two largest minority groups of Blacks and Hispanics suggests a need to potentially reassess the design of digital equity programs and policy interventions.

Tennis. Analytics when applying the most powerful shot

Actuality. Many coaches mistakenly assume that the flight of the ball on a straight line trajectory with a flat feed and impact will hit the tennis net. As a result, it is considered that adolescents do not need to teach a flat serve, but to teach immediately spin serve with the rotation of the ball.
 Purpose of the article: to show analytical and theoretical researches of flat shots in tennis their ability for the best application.
 Methodology. Integration of knowledge of various sciences and their methods to solve the problems of physical education and sports training.
 Scientific novelty. An analytical method over of calculation of descriptions of motion is brought by means of kinematics. Factors affecting the trajectory of the ball's flight have been educed. The analytical dependence of the instantaneous and final speed, range and altitude of the ball on the mechanical parameters without taking into account the air resistance is derived. The analytical analysis of these shots is expounded with initial velocity, depending on the angle of slope to horizon under that there is a blow to the ball in the distance h from the plane of court. Correcting coefficients over are brought. A percent on that actual speed can change in a flat blow and serve is given. The advantages and disadvantages of these strikes are given. The terms of implementation of flat serve, her advantages and defects, are examined.
 Conclusions. Most powerful and at the same time most difficult for implementation flat shots at that a ball is not given by a rotation are considered. It is necessary to take into account that rectilineal and curvilinear trajectories outbowed only in a vertical plane are named flat. A trajectory of flight of the ball sent by a player can be different. The decisive influence on it is the rotation of the ball during its flight. The rotation of the ball during its flight has a decisive influence on it. Factors affecting the trajectory of the ball's flight have been clarified.

The computational and energy cost of simulation and storage for climate science: lessons from CMIP6

Abstract. The Coupled Model Intercomparison Project (CMIP) is one of the biggest international efforts aimed at better understanding the past, present, and future of climate changes in a multi-model context. A total of 21 model intercomparison projects (MIPs) were endorsed in its sixth phase (CMIP6), which included 190 different experiments that were used to simulate 40 000 years and produced around 40 PB of data in total. This paper presents the main findings obtained from the CPMIP (the Computational Performance Model Intercomparison Project), a collection of a common set of metrics, specifically designed for assessing climate model performance. These metrics were exclusively collected from the production runs of experiments used in CMIP6 and primarily from institutions within the IS-ENES3 consortium. The document presents the full set of CPMIP metrics per institution and experiment, including a detailed analysis and discussion of each of the measurements. During the analysis, we found a positive correlation between the core hours needed, the complexity of the models, and the resolution used. Likewise, we show that between 5 %–15 % of the execution cost is spent in the coupling between independent components, and it only gets worse by increasing the number of resources. From the data, it is clear that queue times have a great impact on the actual speed achieved and have a huge variability across different institutions, ranging from none to up to 78 % execution overhead. Furthermore, our evaluation shows that the estimated carbon footprint of running such big simulations within the IS-ENES3 consortium is 1692 t of CO2 equivalent. As a result of the collection, we contribute to the creation of a comprehensive database for future community reference, establishing a benchmark for evaluation and facilitating the multi-model, multi-platform comparisons crucial for understanding climate modelling performance. Given the diverse range of applications, configurations, and hardware utilised, further work is required for the standardisation and formulation of general rules. The paper concludes with recommendations for future exercises aimed at addressing the encountered challenges which will facilitate more collections of a similar nature.

Optimizing Driving Parameters of the Jumbo Drill Efficiently with XGBoost-DRWIACO Framework: Applied to Increase the Feed Speed.

The jumbo drill is a commonly used driving equipment in tunnel engineering. One of the key decision-making issues for reducing tunnel construction costs is to optimize the main driving parameters to increase the feed speed of the jumbo drill. The optimization of the driving parameters is supposed to meet the requirements of high reliability and efficiency due to the high risk and complex working conditions in tunnel engineering. The flaws of the existing optimization algorithms for driving parameter optimization lie in the low accuracy of the evaluation functions under complex working conditions and the low efficiency of the algorithms. To address the above problems, a driving parameter optimization method based on the XGBoost-DRWIACO framework with high accuracy and efficiency is proposed. A data-driven prediction model for feed speed based on XGBoost is established as the evaluation function, which has high accuracy under complex working conditions and ensures the high reliability of the optimized results. Meanwhile, an improved ant colony algorithm based on dimension reduction while iterating strategy (DRWIACO) is proposed. DRWIACO is supposed to improve efficiency by resolving inefficient iterations of the ant colony algorithm (ACO), which is manifested as falling into local optimum, converging slowly and converging with a slight fluctuation in a certain dimension. Experimental results show that the error by the proposed framework is less than 10%, and the efficiency is increased by over 30% compared with the comparison methods, which meets the requirements of high reliability and efficiency for tunnel construction. More importantly, the construction cost is reduced by 19% compared with the actual feed speed, which improves the economic benefits.

Research on the calculation and evaluation method of transmission efficiency of noncircular planetary gear

The torque fluctuation characteristics of noncircular planetary gear (NPG) systems are difficult to obtain, and there is no clear analysis method and evaluation method for transmission efficiency, which seriously affects their service performance. Given that, this article establishes a torque fluctuation model for NPG systems based on the principle of gear meshing and differential geometry theory and methods, taking into account factors such as actual working conditions and speed loads. And proposed, the comprehensive transmission rate, a new method for evaluating the efficiency of NPG systems. By analyzing the actual working conditions of NPG systems, the influence mechanism of alternating load and speed on torque fluctuation and transmission efficiency was explored. The research results indicate that the alternating load can cause an increase in system impact and vibration. The comprehensive transmission rate of NPG systems decreases with increasing rotational speed and increases with increasing load, and is more sensitive to changes in rotational speed. The trend of comprehensive transmission rate and transmission efficiency of NPG systems is basically consistent, indicating that the proposed method of using comprehensive transmission rate to evaluate transmission efficiency is a practical and feasible analysis method. The research results can provide a new method for dynamic measurement and evaluation of torque and transmission efficiency of non-circular gear transmission systems.

A wind speed forecasting model using nonlinear auto-regressive model optimized by the hybrid chaos-cloud salp swarm algorithm

Wind energy forecasting is significantly affected by the strong volatility, intermittency and variability of the wind speed sequence itself. Therefore, to improve forecast accuracy and stability, based on nonlinear auto-regressive model with exogenous inputs (NARX) and the hybrid chaos-cloud salp swarm algorithm (CC-SSA), a short-term wind speed prediction method is proposed. Firstly, to reduce the complexity of the original wind speed data and generate subcomponents with different patterns and low complexity, a mixed modal decomposition method is carried out by combining variational modal decomposition (VMD) based on Pearson correlation coefficient and generalized S-transform (GST) based on adaptive sample entropy. Therefore, the complementary advantages of different mode decompositions are obtained by combining the two different subcomponents into a mixed component. Secondly, by using cloud model and chaotic map, the improved CC-SSA algorithm is proposed to improve the convergence performance of salp swarm algorithm (SSA). Finally, by using CC-SSA algorithm to optimize the weights of NARX, the CC-SSA-NARX forecasting model is established to predict wind speed. The experimental results show that for the 1-Step, 2-Step, and 3-Step datasets of the actual wind speed in the studied region, the MAE, MAPE, RMSE, and R2 of the CC-SSA-NARX model are 0.23, 6 %, 0.27, and 0.97 respectively. The proposed model present the highest prediction index among the other 7 comparative models, showing high accuracy and generalization ability in short-term wind speed prediction, it can provide a certain reference for enhancing the stability of wind power generation and promoting the sustainable development of the wind power industry to some extent.

Data-driven intelligent prediction of TBM surrounding rock and personalized evaluation of disaster-inducing factors

With the widespread application of tunnel boring machines (TBM) in underground tunnel construction, the accurate and real-time identification of geological indicators has become a key factor for ensuring construction safety. In this study, we introduced the nomogram method into the field of civil engineering for the first time to predict surrounding rock grades. Against the backdrop of a major water conservancy project in China, an in-depth analysis of construction data from the ascending phase was conducted to establish the relationship between surrounding rock grade predictions and key construction parameters. Compared with traditional models, the nomogram method has shown advantages in terms of prediction accuracy. Furthermore, by providing a personalized scoring mechanism for specific construction data, the nomogram method not only achieves the real-time prediction of rock grades but also clearly reveals the dynamic relationships between input parameters and rock grades, clarifying the entire prediction process. We identified the main factors related to rock grade prediction among TBM construction parameters, including the slurry conveyor belt speed, jacking pressure, cutterhead torque, cutterhead thrust, thrust pressure, and actual rotation speed. The training process revealed that the nomogram prediction model achieved optimal performance with seven input parameters, balancing accuracy and training time. According to the prediction results, the nomogram model’s area under the curve index is 0.92, providing greater accuracy than the traditional random forest model (0.89). Therefore, this study enhances the transparency and interpretability of model predictions by providing personalized assessments of construction parameters for predicting surrounding rock grades, overcoming the black-box issue of existing prediction methods and offering a novel and effective tool for rock grade prediction.