Intelligent Prediction Method Research Articles

Numerical simulation and intelligent prediction of a 500 t/d municipal solid waste incinerator

After classifying municipal solid waste (MSW), understanding how changes in its characteristics affect the incineration process becomes essential. Optimizing and adjusting operating parameters is crucial to ensure the smooth operation of MSW incinerators. In this study, we developed a three-dimensional numerical simulation model for a 500 t/d MSW incinerator, based on a two-fluid model that accounts for grate motion. We examined the effects of load and moisture on the combustion process. Using the results from the numerical simulation and on-site operational data, we established a machine learning model to predict incinerator conditions based on load, waste particle size, and moisture content. The average relative errors for the training and test sets were approximately 0.33 % and 0.44 %, respectively. Finally, we compared the influence of different operating parameters on flue gas temperature. Our numerical simulation model and intelligent prediction method offer valuable guidance for optimizing existing MSW incinerators and designing new ones.

Intelligent prediction of rail corrugation evolution trend based on self-attention bidirectional TCN and GRU

Analyzing the evolution trend of rail corrugation using signal processing and deep learning is critical for railway safety, as current traditional methods struggle to capture the complex evolution of corrugation. This present study addresses the challenge of accurately capturing this trend, which relies significantly on expert judgment, by proposing an intelligent prediction method based on self-attention (SA), a bidirectional temporal convolutional network (TCN), and a bidirectional gated recurrent unit (GRU). First, multidomain feature extraction and adaptive feature screening were used to obtain the optimal feature set. These features were then combined with principal component analysis (PCA) and the Mahalanobis distance (MD) method to construct a comprehensive health indicator (CHI) that reflects the evolution of rail corrugation. A bidirectional fusion model architecture was employed to capture the temporal correlations between forward and backward information during corrugation evolution, with SA embedded in the model to enhance the focus on key information. The outcome was a rail corrugation trend prediction network that combined a bidirectional TCN, bidirectional GRU, and SA. Subsequently, a multi-strategy improved crested porcupine optimizer (CPO) algorithm was constructed to automatically obtain the optimal network hyperparameters. The proposed method was validated with on-site rail corrugation data, demonstrating superior predictive performance compared to other advanced methods. In summary, the proposed method can accurately predict the evolution trend of rail corrugation, offering a valuable tool for on-site railway maintenance.

Experimental study on shear mechanical characteristics and its size effect of concrete joints based on BP neural network method

In order to study the shear mechanical characteristics and its size effect on concrete joints, this paper uses the RDS-200 shear test system developed by GCTS company to carry out the indoor direct shear tests on concrete joint specimens with five kinds of JRC and five grades of joint surface size under five normal stress conditions. The results show that the pre-peak shear stiffness of concrete joints is not significantly affected by JRC and normal stress, but is significantly affected by the joint surface size. The pre-peak shear stiffness decreases with the increase of joint surface size, when the joint surface size increases from 35 mm to 95 mm, the pre-peak shear stiffness of specimens with JRC being 0 ∼ 2, 4 ∼ 6, 8 ∼ 10, 12 ∼ 14 and 16 ∼ 18 decreased by 66.96 %, 63.87 %, 71.67 %, 64.57 % and 66.32 %, respectively, with an average of 66.68 %. Moreover, with the increase of joint surface size, the decrease rate of pre-peak shear stiffness decreases gradually, which is basically exponential, that is because the pre-peak shear stiffness has negative size effect. In addition, the peak shear strength of concrete joints increases with the increase of normal stress and JRC, and the relationship between peak shear strength and normal stress conforms to the Mohr-Coulomb criterion. Furthermore, there is no obvious size effect on the peak shear strength of concrete joints in the range of 35–95 mm. An intelligent prediction method for predicting the peak shear strength and pre-peak shear stiffness of concrete joints considering JRC, joint surface size and normal stress was established. The prediction results show that the BP Neural Network optimized by genetic algorithm can effectively predict the peak shear strength and pre-peak shear stiffness of concrete joints, the average error of peak shear strength is about 4.91 %, and the average error of pre-peak shear stiffness is 9.37 %. The conclusions can provide some reference for the evaluation of shear instability of rock joints in surface and underground rock engineering.

Cabin air dynamics: Unraveling the patterns and drivers of volatile organic compound distribution in vehicles.

Volatile organic compounds (VOCs) are ubiquitous in vehicle cabin environments, which can significantly impact the health of drivers and passengers, whereas quick and intelligent prediction methods are lacking. In this study, we firstly analyzed the variations of environmental parameters, VOC levels and potential sources inside a new car during 7 summer workdays, indicating that formaldehyde had the highest concentration and about one third of the measurements exceeded the standard limit for in-cabin air quality. Feature importance analysis reveals that the most important factor affecting in-cabin VOC emission behaviors is the material surface temperature rather than the air temperature. By introducing the attention mechanism and ensemble strategy, we present an LSTM-A-E deep learning model to predict the concentrations of 12 observed typical VOCs, together with other five deep learning models for comparison. By comparing the prediction-observation discrepancies and five evaluation metrics, the LSTM-A-E model demonstrates better performance, which is more consistent with field measurements. Extension of the developed model for predicting the 10-day VOC concentrations in a realistic residence further illustrates its excellent environmental adaptation. This study probes the not-well-explored in-cabin VOC dynamics via observation and deep learning approaches, facilitating rapid prediction and exposure assessment of VOCs in the vehicle micro-environment.

Intelligent prediction of unfrozen water content of artificially frozen clay along one-dimensional column based on LF-NMR

The unfrozen water content (UWC) plays a crucial role in frozen soil engineering, however, traditional unfrozen water measurement or calculation methods are time-consuming and costly, and it is difficult to express the non-linearity among the influencing factors. Currently, LF-NMR is widely recognized as an effective tool for measuring unfrozen water. In this study, a calibration curve that can describe the relationship between the NMR signal and water content was derived. Moreover, the effects of temperature, initial water content, and soil height on UWC are explored along one-dimensional large-diameter columns based on LF-NMR data. Combined with the ML algorithm and the UWC test data under different factors based on LF-NMR, an intelligent prediction method that can consider the nonlinear characteristics of multiple factors is proposed. The results showed that the NMR accuracy is 176.64 semaphore per 1% water content, the error between the water content calculated from the calibration curve and that obtained from the drying method is small (average error is 0.97%), and the linearity degree of the calibration curve is good (R2 = 0.999). Moreover, due to the strong correlation between unfrozen water and temperature, initial water content, and other factors, the results indicated that the GPR model can better describe this correlation and has the best prediction effect, which was evaluated with the quantitative indicators: R2 = 0.96, RMSE = 1.07, MAE = 1.00, and again verifies the superiority of this model in combination with other literature data. Overall, this paper offers a technical basis for controlling and preventing freezing and thawing disasters in cold regions and artificial freezing engineering projects.

Intelligent prediction and evaluation method of optimal frequency based on PSO-BPNN-AdaBoost model

To achieve rapid and accurate determination of the optimal compaction frequency for high-speed railway subgrade materials, a method based on the PSO-BPNN-AdaBoost model for intelligent frequency estimation is proposed. Firstly, the Particle Swarm Optimization (PSO) algorithm is introduced to obtain the optimal hyperparameters of the Backpropagation Neural Network (BPNN), and then the PSO-BPNN-AdaBoost model is established by integrating the AdaBoost ensemble algorithm. Secondly, taking graded gravel fill material as an example, the Grey Relational Analysis algorithm (GRA) is employed to identify the main controlling features affecting f op as input features for the model, and the predictive performance of the model is evaluated. Finally, the model’s reliability is verified through ablation analysis. The results indicate that the PSO-BPNN-AdaBoost model demonstrates higher predictive accuracy. The main controlling features influencing f op are revealed to be the maximum particle size (d max), gradation parameters (b, m), coarse aggregate elongation index (EI), Los Angeles Abrasion (LAA), water absorption rates (W ac, W af).

Intelligent prediction method for underbreak extent in underground tunnelling

Underground tunnel excavation using the drill-and-blast method often results in underbreak occurrences due to improper blasting parameters and complex geological environment. This underbreak phenomenon has a profound impact on tunnel safety, stability and construction costs. Traditional approaches for predicting underbreak extent (UE) through field measurements and theoretical models exhibit limitations in terms of accuracy and efficiency. Consequently, it is urgent to develop a novel approach for UE prediction in tunnelling operations. In this paper, extreme gradient boosting (XGBoost) is first applied as the foundational algorithm, and slime mould algorithm (SMA) is implemented for tuning hyper-parameters of XGBoost. Meanwhile, six chaotic maps are integrated with SMA to initialize the population and improve the optimization performance. Subsequently, a dataset of 250 samples is collected from three underground mines in China, involving ten influential factors (RMR, uniaxial compressive strength (UCS), vertical principal stress (σv), lateral pressure coefficient (λ), excavation area (EA), powder factor (PF), specific charge (SC), advance length (AL), periphery hole burden (HB)and periphery hole spacing (HS)). Additionally, four indices, i.e., coefficient of determination (R2), variance accounted for (VAF), mean absolute error (MAE) and root mean square error (RMSE), are used to evaluate the comprehensive performance of these proposed COSMA-XGBoost models and four common machine learning models. Finally, a parametric sensitivity analysis is performed, and the optimal intelligent model is applied in practical tunnelling projects. The results indicate that chaotic optimized SMA models have better convergence ability and higher accuracy compared with the original SMA model. Notably, the PSMA-XGBoost model outperforms other models as the R2, VAF, MAE and RMSE values for the testing set are 0.979, 95.79%, 4.176 and 4.884, respectively, whereas 0.968, 94.25%, 4.274 and 5.012, respectively for training set. EA, RMR and UCS exhibit significant effects on UE. In practical tunneling projects, the engineering application of the PSMA-XGBoost model demonstrates its feasibility and efficiency in predicting UE within underground tunnels.

Collaborative Intelligent Prediction Method for Remaining Useful Life of Hard Disks Based on Heterogeneous Federated Transfer

Collaborative Intelligent Prediction Method for Remaining Useful Life of Hard Disks Based on Heterogeneous Federated Transfer

基于深度神经网络的微扰动压入式沉井顶压力智能预测方法

基于深度神经网络的微扰动压入式沉井顶压力智能预测方法

Optimizing rock fragmentation in open-pit mines through fuzzy intelligent prediction method

Blasting is one of the most important steps in mining operation and it directly affects final results (extraction ore body and costs). Various parameters such as rock mass and explosive properties, and blast geometry influence blasting results. A number of effective parameters in fragmentation should be taken into account to design a suitable blasting pattern, reduce the secondary costs and minimize the adverse effects such as flyrock, back break and ground vibration. Fuzzy theory is a widely used technique in many engineering subjects in which there exist concepts of quality and uncertainly. In this study, the information obtained from blasting operation in B anomaly Sangan Iron Mines have been used. In this model, the blasting pattern parameters such as burden, spacing, hole depth, stemming, charging length, ratio of (K/B), number of rows, specific charge and charge per delay ratio were considered as the input parameters in fuzzy model. Then, the results of fuzzy model were compared with statistical models. Finally, the results of the two models produced from mine blasting operation were compared and evaluated with real values. The correlation coefficient index for two models were 97.8% and 72.19%, and the RMSE were 2.613 and 9.18, respectively.

Retracted: Research on Intelligent Prediction Method of Financial Crisis of Listed Enterprises Based on Random Forest Algorithm

Retracted: Research on Intelligent Prediction Method of Financial Crisis of Listed Enterprises Based on Random Forest Algorithm

An intelligent prediction method of surface residual stresses based on multi-source heterogeneous data

An intelligent prediction method of surface residual stresses based on multi-source heterogeneous data

Intelligent prediction method for fracture pressure based on stacking ensemble algorithm

Fracture pressure is an important reference for wellbore stability analysis and hydraulic fracturing. Considering the low prediction accuracy, significant deviations, and limited applicability of traditional methods for predicting formation fracture pressure, this paper proposes an intelligent prediction method for fracture pressure using conventional well logging data based on the Stacking ensemble algorithm. The base learners of the model include RF, KNN, and LSTM algorithms with low correlation. The meta-learner adopts the XGBoost algorithm. The effectiveness of the model is validated using the fracture pressure data from Dagang Oilfield. The prediction results indicate that the stacking algorithm outperforms individual algorithms. After optimization with genetic algorithm, the R2 of the stacking model is 0.989, RMSE is 0.009%, and MAE is 0.32%. The global sensitivity analysis results show that AC and DEN in the well logging data have higher sensitivity to the fracture pressure. When using intelligent fracture pressure prediction methods, it is essential to ensure the accuracy of AC and DEN data. The work demonstrates the reliability and effectiveness of the method proposed for the intelligent prediction of fracturing pressure using conventional well logging data through Stacking ensemble algorithm to overcome the limitations of traditional methods.

A Short-term Load Curve Intelligent Prediction Method Based on FFT and GA Composite Algorithm and Its Application

In order to reduce the system operation cost of predicting short-term load curves and improve the feasibility of artificial intelligence prediction, the FFT and GA composite algorithm is designed as an artificial intelligence algorithm with the goals of low time complexity, small required sample data, and high flexibility. The algorithm uses mature Fast Fourier Transform and Genetic Algorithm, and improves the flexibility of the prediction method by dynamically adding kernel functions. It can be used for load curve prediction of 96 data points or smaller granularity, and can also reuse the calculation results of big data prediction methods for larger data granularity. By improving the feasibility of individual low-reward data predictions, it can improve the accuracy, sensitivity, and adaptability of energy management plans, accelerate the high-quality development of the power grid, improve the level of digitization, and promote demand response.

Analysis of machine learning-based applications for intelligent construction of shield tunnels

With the quick advancement of the level of information science in shield tunnel construction, the monitoring methods of shield equipment during tunnel boring work are increasingly improved and the recorded construction data includes not only information on the internal workings of the shield equipment, but also on its interaction with the external strata. Machine learning data analysis is powerful and has a wider range of applications and scope than traditional data analysis methods in the civil construction industry. Through the use of machine learning methods, the data and information collected can be mined and analysed in depth to find the intrinsic connections and linkages that can help improve the safety and efficiency ragarding shield tunnel construction. This work presents a literature analysis of current situations of machine learning for shield tunnel construction at home and abroad, briefly describes the basic principles of machine learning methods, summarises and analyses the research situation in shield tunnel construction, reviews the progress of machine learning-based shield equipment condition analysis, intelligent prediction and control methods for shield tunneling parameters and shield tunneling surface deformation prediction, and summarises the current research The study also summarises the shortcomings of current research. Finally, an outlook on the development of shield tunneling towards intelligence is presented.

Flow regime identification using fuzzy and neuro-fuzzy inference applied on differential pressure sensor

Intelligent identification of two-phase flow regime is indispensable for the safe design of industrial systems. This work presents the development and examination of intelligent prediction methods based on fuzzy logic designed to operate on a differential pressure sensor signal for two-phase flow regime identification. The effectiveness of intelligent paradigms for flow regime identification is established using experiments carried out in a 0.9 mm circular glass tube kept horizontally. Air and water are the two-phase fluids. Three flow regimes, namely bubbly, slug and annular flows are observed for various combinations of superficial fluid velocities. The pressure drop in the flow system is measured and recorded online using a differential pressure sensor connected to a data acquisition system. Signal features such as peak current, the difference in current and signal frequency are extracted after extensive analysis. Fuzzy rules are outlined and flow regime output is analyzed. The bottleneck observed in these techniques is qualitatively designated in terms of accuracy and required high human effort. A solution to this bottleneck is found out using adaptive neuro-fuzzy inference system (ANFIS). ANFIS applied on pressure sensor signal is found to provide an accurate characterization of flow regime.

An improved integration strategy for prediction of shear wave velocity using petrophysical logs: Integration of spatiotemporal and small sample nonlinear feature

Shear wave velocity (Vs) plays an important role in seismic data inversion and accurately determining petrophysical parameters. However, owing to the high acquisition cost of S-wave logging, Vs-related data cannot be obtained from all wells, especially the older ones. Therefore, developing an efficient and fast intelligent prediction method for Vs using existing petrophysical logging data is necessary. Owing to the advantages of conventional machine learning (CML) and deep learning (DL) methods, this study developed a data-driven integrated learning (IL) method for Vs prediction, which uses sensitive logging data as input, builds multiple sample sets, analyzes the performance differences of the CML and DL methods for predicting Vs in each dataset, and optimizes the support vector machine (SVM) in the CML method and convolutional neural network-long short-term memory (CNN-LSTM) in the DL method to construct an integrated CNN-LSTM-SVM neural network (ICLSNN). The spatiotemporal feature extraction capability of CNN-LSTM was used to effectively extract the deposition information of the formation from the logging data, and the small sample nonlinear feature extraction capability of SVM was used to alleviate the limitations of overfitting in DL for data-driven intelligent Vs prediction. The ICLSNN showed a higher Vs prediction accuracy than SVM or CNN-LSTM alone. The Vs prediction results of the single well in the discovery without training were better than those of the other IL and empirical models, proving the strong generalization applicability of the proposed model. This method compensates for the limitations of individual machine learning or DL models for Vs prediction and provides a novel research idea. The effective implementation of this scheme will facilitate the efficient prediction of Vs in areas with few wells.

Prediction of ground subsidence by shield tunneling using ensemble learning

This study addresses a significant issue related to shield construction, namely the impact of shield construction parameters on ground subsidence. Shield tunnels, owing to their concealed construction, often lead to ground subsidence, causing severe disruptions to ground facilities, potential collapse, and consequent casualties and property loss. Most calculation methods for ground subsidence have a delay, making it difficult to predict ground subsidence in advance based on construction parameters. Therefore, this study collates over 1500 cases of shield construction in China and summarises the impact mechanisms of shield construction on ground subsidence through a detailed study of natural factors, engineering geology, types of structures, and types of construction parameters. Based on the influence law of the data, this study establishes three intelligent prediction methods to forecast ground subsidence. To select the hyperparameters in the models more effectively, a Bayesian optimisation algorithm was utilised. Through a comparative analysis of the models, a subsidence prediction method that is more in line with the construction characteristics and exhibits high accuracy was determined. The findings of this study serve as a valuable resource for predicting ground subsidence resulting from shield construction, enabling timely adjustments to construction parameters and the implementation of preventive measures to mitigate excessive subsidence.

Intelligent prediction methods for N–M interaction of CFST under eccentric compression

Intelligent prediction methods for N–M interaction of CFST under eccentric compression

A novel combined intelligent algorithm prediction model for the tunnel surface settlement

To ensure the safety and stability of the shield tunnel construction process, the ground settlement induced by the shield construction needs to be effectively predicted. In this paper, a prediction method combining empirical mode decomposition (EMD), chaotic adaptive sparrow search algorithm (CASSA), and extreme learning machine (ELM) is proposed. First, the EMD is used to decompose the settlement sequence into trend vectors and fluctuation vectors to fully extract the effective information of the sequence; Second, the sparrow search algorithm is improved by introducing Cubic chaotic mapping to initialize the population and adaptive factor to optimize the searcher’s position formula, and the chaotic adaptive sparrow search algorithm is proposed; Finally, the CASSA-ELM prediction model is constructed by using CASSA to find the optimal values of weights and thresholds in the extreme learning machine. The fluctuation components and trend components decomposed by EMD are predicted one by one, and the prediction results are superimposed and reconstructed to obtain the predicted final settlement. Taking a shield interval in Jiangsu, China as an example, the meta-heuristic algorithm-optimized ELM model improves the prediction accuracy by 10.70% compared with the traditional ELM model. The combined EMD-CASSA-ELM prediction model can greatly improve the accuracy and speed of surface settlement prediction, and provide a new means for safety monitoring in shield tunnel construction. Intelligent prediction methods can predict surface subsidence more automatically and quickly, becoming a new development trend.