Grey Theory Model Research Articles

Evaluating the carbon emission reduction in construction and demolition waste management in China

Construction and demolition waste reduction (C&DWR) is an important measure to protect the environment and promote sustainable development in the construction sector. The present policy in China remains a lack of emphasis on carbon tax and penalty for reducing construction waste. This paper proposes a construction and demolition waste (C&DW) management model to identify the waste reduction effects of various policies in China. It simulates the effects of single policies and combined policies on waste reduction and carbon emission by using the approach of integrating system dynamics and grey model theory. The results reveal that: (1) Penalties can effectively reduce the amount of illegal dump waste and carbon emissions from illegal dump waste, with an appropriate penalty value (in Chinese CNY) suggested as ∈160,320. (2) Subsidies can vastly increase the amount of waste recycled and a carbon tax can reduce carbon emissions; the recommended values are suggested as subsidies ∈30,45 and carbon tax ∈40,70. (3) Compared to other policies, the combined policy of penalty–waste disposal charging fee–subsidy–carbon tax (160, 60, 45, 70) achieves the greatest environmental benefits in terms of reducing waste and carbon emission. (4) Governments should improve waste minimization incentive policies and gradually introduce trials for a carbon tax policy, encourage the use of digital governance combined with blockchain technology to regulate C&DW. These findings provide valuable insights for policymakers to develop effective waste reduction strategies and promote sustainability through the implementation of C&DWR theories.

Acoustic optimization of a tee via a Helmholtz resonant cavity and noise prediction via a genetic algorithm coupled with the grey model

As a key local component in heating, ventilation and air conditioning (HVAC) systems, the acoustic characteristics of the tee largely determine the indoor acoustic environment quality. The aim of this study was to determine the impact of the Helmholtz resonant cavity (HRC) on the tee noise and sound transmission loss (STL). The noise was predicted using the Ffowcs Williams-Hawkings (FW-H) equation and large eddy simulation (LES), and an analysis was conducted on four design variables: the cavity width, cavity height, outlet flow ratio, and hole width. The results indicated that increasing the hole width had a positive effect on reducing the noise of the tee. HRC could result in a tee peak noise attenuation up to 34.81 dB. In addition, based on grey model (GM) and genetic algorithm (GA) theory, the noise of the Helmholtz resonant tee (HRT) was predicted, and a noise prediction formula for resonant cavity structure paraments was derived. This study provides a new perspective for optimizing the STL of the tee and a simplified prediction method for low-noise tee design.

Preference-based grey theory model and its application in waste disposal selection: a case study

Preference-based grey theory model and its application in waste disposal selection: a case study

Energy-saving and CO2 reduction strategies for new energy vehicles based on the integration approach of voluntary advocacy and system dynamics.

The low-carbon development of new energy vehicles (NEVs) is critical to achieving the goals of carbon peaking and carbon neutrality. As such, combining gray model theory with system dynamics (SD-GM) and based on the bidirectional-cycle prediction theory, we propose a NEV annual average mileage algorithm considering the impact of the epidemic in China, taking private cars as an example. Then, combining a voluntary advocacy strategy (VA) with the SD-GM theory (VA-SD-GM integration), we establish an energy-saving and carbon-reduction management model. To evaluate the proposed algorithm, we performed a dynamic simulation. The results indicate that the enhanced green scenario enabled significant energy-saving and CO2 reduction performance but would cause side effects in the long term. Compared with the enhanced green scenario, the linkage mode reduced the impact of parking space tension, the number of NEV trips, and the intensification of traffic congestion by approximately 33%, 50%, and 34%, respectively. It effectively suppressed the continuous increase in side effects and had a synergistic effect of carbon reduction, energy conservation, congestion alleviation, and side-effect reduction. The study provides a theoretical basis for optimizing the energy-saving and CO2 reduction path of NEVs.

Risk Assessment Method of Power Communication Network Based on Gray Cloud Theory and Matter-Element Extension Model Coupling

Risk Assessment Method of Power Communication Network Based on Gray Cloud Theory and Matter-Element Extension Model Coupling

Research on Subsidence Prediction Method of Water-Conducting Fracture Zone of Overlying Strata in Coal Mine Based on Grey Theory Model

The development height and settlement prediction of water-conducting fracture zones caused by coal seam mining play an important role in the stability of overburden aquifers and the safety of roadways. Based on the engineering geological data of the J60 borehole in the Daliuta Coal Mine and the mining conditions of the 2−2 coal seam, China, this study established a similar material test model of mining overburden. The deformation characteristics of overlying strata in the mining process of coal seam were studied by using distributed optical fiber sensing technology, and the development height of water flowing fractured zone was determined. According to the equidistant sampling characteristics of Brillouin optical time domain reflection technology and the principle of the grey theory model, the settlement prediction model of the water-conducting fracture zone was established. By analyzing and comparing the prediction accuracy of the GM (1, 1) model, grey progressive model, and metabolic model, the optimal method for settlement prediction of the water-conducting fracture zone was discussed. The results show that, for the metabolic model, with the increase in the number of test sets and the decrease in the number of prediction sets, the mean square error ratio c and the small error probability p of the prediction accuracy evaluation parameters display a downward trend. The accuracy is related to the sudden change in the settlement of the water-conducting fracture zone caused by the breaking of the key stratum of the overlying rock. The optimal time of test sets selected for the best settlement prediction model is 7~8, and that of prediction sets selected is 5~6. For the GM (1, 1) model and the grey progressive model, the prediction accuracy of mining overburden subsidence is grade 4, which is not suitable for settlement prediction of water-flowing fractured zones.

Grey–Markov prediction model based on time-continuous Markov model and Levenberg–Marquardt algorithm

Grey–Markov model, which combines the advantages of grey system theory and Markov model, has a wide range of applications in various fields. However, reliable predicting can hardly be given for the reason that transition probabilities of traditional Grey–Markov model are obtained by transition frequency with small sample size and the information of small sample size is not fully utilized. To solve this problem, we improve Grey–Markov model based on time-continuous Markov model. First, based on the residual errors of GM (1, 1), we construct the least error square objective function with Kolmogorov forward equations. Second, we improve Levenberg–Marquardt algorithm to obtain the optimal transition intensities and calculate corresponding transition probabilities of Markov model. Third, we obtain predicted value with normal distribution quantile. Not only the improved prediction model has better interpretability, but case study is applied to verify its validity by comparing it with traditional Grey–Markov model.

A procedure for risk assessment of check dam systems: A case study of Wangmaogou watershed.

Flood-based hydrodynamic damage to check dam systems on the Loess Plateau of China occurs frequently, and there is a strong desire to carry out risk assessments of such check dam systems. This study proposes a weighting method that combines the analytic hierarchy process, entropy method, and TOPSIS to assess the risk of check dam systems. The combined weight-TOPSIS model avoids weight calculation only considers the influence of subjective or objective preference and the bias of the single weighting method. The proposed method is capable of multi-objective risk ranking. It is applied to the Wangmaogou check dam system located in a small watershed on the Loess Plateau. The result of risk ranking matches the reality of the situation. The gray correlation theory model is utilized to rank the risks in the same research area and compared with the results of the combined weight-TOPSIS model. The combined weight-TOPSIS model is more favorable to risk assessment than the gray correlation theory model. The resolution level and decisive judgment of the combined weight-TOPSIS model are more advantageous. These results are in line with the actual conditions. It proves that the combined weight-TOPSIS model can provide a technical reference for the risk assessment of check dam systems in small watersheds.

A web-based system to determine risk of investment in international rail construction projects

Manual evaluation of investment risk make results and solutions are not timely. The objective of the study is to explore intelligent risk data collecting and risk early warning of international rail construction. First, this study has identified risk variables by content mining. Second, risk thresholds are calculated by the quantile method based on data from 2010 to A.D. 2019. Third, this study has developed risk early warning system by the gray system theory model, the matter-element extension method and the entropy weight method. Fourth, the risk early warning system is verified using Nigeria coastal railway project in Abuja. This study found that: (1) the framework of the developed risk warning system contains a software and hardware infrastructure layer, a data collection layer, an application support layer, and an application layer. (2) 37 investment risk variables are recognized; (3) 12 risk variables thresholds intervals are not equally divided between 0 and 1, the others are evenly distributed; (4) based on the application of Nigeria coastal railway project in Abuja, the system verification results are consistent with real situations, which is shown that risk early warning system is reasonable and feasible. These findings offer a good reference for intelligent risk management.

Accumulatively Confirmed Cases Used for Grey Modeling Prediction of the Medium and Long Term Future Epidemic Trend of Infectious Diseases

The author used the actual data obtained from the prevention and control of COVID-19 in China and the cumulative number of confirmed cases obtained at different time intervals to predict the medium and long term (20 days, 40 days and 60 days) future epidemic trend by grey modeling. Research objectives: Grey modeling theory is applied to the modeling and prediction of infectious diseases, appropriate data obtained from the prevention and control of infectious diseases are selected for the simulation and prediction of the medium and long term future epidemic trend of infectious diseases, and an effective method for the prediction of the future epidemic trend of infectious diseases is sought. Research Methods: The author used the actual data obtained from the prevention and control of COVID-19 in China. The trend curve was drawn by statistical data, the trend of epidemic was visually analyzed and observed, and the best series for grey modeling prediction was determined. Then GM(1,1) grey modeling was carried out on the selected series, and the error and accuracy of the built model were tested. Finally, the predicted value of the model was actually verified. Research results: According to the series graph, we selected the cumulative number of confirmed cases with time intervals of 20 days, 40 days and 60 days to model and forecast the future medium and long term epidemic trend of COVID-19 in China, and built the prediction models of cumulative confirmed cases respectively. The average error of the GM(1,1) prediction model established by the cumulative number of confirmed cases at the time node with a time interval of 40 days is too high, reaching 0.6422, and the simulation accuracy is only 37%. It has no practical significance for forecasting. The prediction model of GM(1,1), established by the cumulative number of confirmed cases with a time interval of 20 days, has a large average simulation error of 0.3336 and a simulation accuracy of 67%. Through practical verification, the prediction accuracy of GM(1,1) can reach 99.54%, which has a certain practical value for prediction. The prediction model of GM (1,1) based on the cumulative number of confirmed cases at time nodes with a time interval of 60 days, the average simulation error of GM (1,1) prediction model was 0.01167, and the simulation accuracy was 98.83%. Multiple parameters in the accuracy analysis reached the index of the first-level model. The actual verification of the model showed that the cumulative number of confirmed cases at the predicted time node was 102271. In practice, 107094 cases were recorded, and the predicted number was 4823 cases less than the actual number. The relative error was 0.045, and the prediction accuracy reached 95.49%. Satisfactory gray modeling prediction effect was obtained.

Research on the Application of Grey Modeling Theory in the Prediction of Future Epidemic Trend of Infectious Diseases

Grey theory is a kind of grey system theory founded by Professor Deng Jurong, a Chinese scholar in the eighties century. It is a new method to study the uncertainty problem with few data and poor information. In recent years, grey modeling forecasting method has been widely used in industry, agriculture, science and technology, economic and social development planning and analysis, hydrology, geology, breeding and natural disaster prediction and so on. The prediction of epidemic trend of infectious diseases is an important work in the prevention and control of infectious diseases. Therefore, the author of this paper studied the application of grey modeling theory in the prediction and prediction of epidemic trend. Objective: To introduce the grey modeling prediction theory into the prediction of epidemic trend of infectious diseases, and to provide a new prediction method of epidemic trend of infectious diseases. The methods are as follows: The GM(1.1) model was established with the grey system theory, and the grey modeling was carried out on the short and medium term confirmed cases and the wave time of the epidemic outbreak in China in 2021, and the simulation accuracy and precision were analyzed. Results: The average simulation accuracy of the established short-term prediction model reached 73.2% and the prediction accuracy reached 99.85%. The average accuracy of the simulation value of the model reached 97.67% and the prediction accuracy reached 97.33% for the time of wave occurrence in the epidemic. This paper also analyzes the reasons for the low simulation value and prediction accuracy of the grey model forecasting method for the medium and long term confirmed cases of the epidemic.

Application of multi-index grey target decision method in prediction of coal temperature in goaf

ABSTRACT The index gas analysis is an important method of predicting coal spontaneous combustion (CSC). The temperature programmed experiment was used to simulate the CSC process in goaf, and the characteristics of each index gas and coal temperature change were investigated. The corresponding relationship between the target center distance and the coal temperature was determined using the multi-index weighted gray target decision theory. The findings indicate that the selected gases (CO, C2H4, C2H6) and gas combination (format fire coefficient (R), φ(CO)/φ(CO2), φ(C2H4)/φ(CH4), φ(C2H4)/φ(C2H6)) can be used as index gases for CSC prediction. The critical temperature was 70°C and the dry cracking temperature was 110°C. The combined action of coal and oxygen is divided into three stages: slow oxidation (30–70°C), accelerated oxidation (70–110°C), and strong oxidation (after 110°C). The combination of the programmed temperature experiment and the weighted gray target decision theory model determines the relationship between the off-target distance and the coal temperature. The lower the degree of CSC, the better the decision scheme, and the smaller the off-target distance. The results of Xiaobaodang’s experimental analyses are applied to the field, and the actual coal temperature in the goaf is predicted to be between 37–62°C. The risk level of CSC in the goaf is determined more intuitively and precisely, providing a theoretical foundation for improving the mining area’s fire prevention and extinguishing scheme.

Assessment of Grey Forecasting Model: Study Case for Electrification Rate in Indonesia from 2012 to 2021

In 2021, Indonesia was 99.45% electrified. That year's aim was 100%. Due of Indonesia's 17,000 islands, electrifying rural settlements is tough. Depending on network size and demand, Indonesia's energy mix varies, however it often includes coal. After adopting the Paris Climate Agreement, Indonesia vowed to increase renewable energy to 23% by 2025. Indonesia's renewable energy production has increased. The government expects coal to be important in coming decades. The GM (1, 1) model of Grey theory was used to estimate Indonesia's electrification rate from 2012 to 2021. The model's average residual error is above 5%, according to the calculation. Indonesia's electrification rate is expected to grow annually. According to the trials, the recommended technique boosts the forecasting accuracy of the original Grey models and gives Indonesia a helpful reference for designing the action plan.

A damping grey multivariable model and its application in online public opinion prediction

Online public opinion plays pivotal role in social stability, and predicting hotness of online opinion accurately can provide theoretical and practical guidance for government and enterprises. A damping accumulated multivariable grey model is proposed to forecast the online public opinion trends in this paper. Firstly, the dynamic damping trend factor is introduced into the accumulation process, so that the model can adjust the accumulating order of different sequences more flexibly. Secondly, considering that the accumulated sequences have grey exponential rate property, the damping grey multivariable model is established by optimizing the structure of the background values. Finally, due to the assumption that the relevant factor variables are grey constants, the systematic error occurs in the traditional grey multivariate model, the time response equation is given to reduce error by using the composite quadrature method. Two real cases are used for empirical analysis to verify the effectiveness of the new model. And the forecasting accuracy and robustness of the new model is better than those of other prediction models. Therefore, the model is an effective method dealing with nonlinear problems, which further improves the grey modeling theory and can be applied to the prediction of online opinion.

Non-Probabilistic Uncertainty Quantification of Fiber-Reinforced Composite Laminate Based on Micro- and Macro-Mechanical Analysis

In this paper, the main aim is to study and predict macro elastic mechanical parameters of fiber-reinforced composite laminates by combining micro-mechanical analysis models and the non-probabilistic set theory. It deals with uncertain input parameters existing in quantification models as interval variables. Here, several kinds of micro-mechanical mathematical models are introduced, and the parameter vertex solution theorem and the Monte Carlo simulation method can be used to perform uncertainty quantification of macro elastic properties for composites. In order to take the correlations between macro elastic properties into consideration, the obtained limited sample points or experimental data are utilized on the basis of the grey mathematical modeling theory, where correlated uncertain macro parameters can be treated as a closed and bounded convex polyhedral model. It can give out a clear analytical description for feasible domains of correlated macro elastic properties in the process of uncertainty quantification. Finally, two numerical examples are carried out to account for the validity and feasibility of the proposed quantification method. The results show that the proposed method can become a powerful and meaningful supplement for uncertainty quantification of composite laminates and provide data support for further uncertainty propagation analysis.

Grey model prediction for the interfacial properties of carbon fiber-reinforced bismaleimide resin under thermo-oxidative aging

The overall performance of composites will be seriously affected when their interfacial properties degrade after thermo-oxidative aging (TOA). In this paper, the interfacial properties of carbon fiber (CF)/bismaleimide (BMI) resin aged for 10, 30, 90, 120 and 180 days at 200°C and 250°C were quantitatively studied by transverse fiber bundle tensile (TFBT) testing. In addition, a prediction model of interfacial strength based on the grey model theory was established. The results show that the interfacial strength for CF/BMI resin decreases exponentially with the extension of aging time, and the strength of the TFBT specimens aged at 200°C for 180 days and 250°C for 90 days decreases by 82.01% and 84.65%, respectively. This is caused by the oxidation decomposition of BMI resin and the mismatch of the thermal expansion coefficient between the CF and BMI resin. In addition, the grey model is used to predict the interfacial properties of CF-reinforced BMI resin under TOA. The prediction accuracy is higher than that of the traditional exponential model, and the prediction error is less than 10%. To sum up, this work can provide a reference for studying the evolution of interfacial properties of other kinds of fiber-reinforced polymer composites under extreme environments.

Strategic Alliances for Sustainable Development: An Application of DEA and Grey Theory Models in the Coal Mining Sector

Sustainable development is a global trend and an economic priority for many governments. Although new energies can be considered good investments in green growth, they may lead to financial barriers to developing countries. The purpose of the study concentrates on an alternative solution that increases the efficiency performance of current fossil energy industries. The study has combined two models of Data Envelopment Analysis (DEA) and Grey Theory in determining inefficient units to propose potential strategic alliances for sustainable development in the Vietnam Coal industry. Besides considering inputs and outputs in the models, the location of coal mines is also a key indicator in recommending good alliances. The results show that the collaborations between the Cao Son coal mine and the Coc Sau coal mine, and between the Nui Beo coal mine and the Vang Danh coal mine, bring the best improvement for sustainable development. The study suggests detailed strategies in action that enterprises and policymakers can refer to, to apply in practice.

Weighting analysis on air pollution among recent years in China via grey theory models

The priority of national development has always been economic development during the recent decades in China, so a lot of energy consumption was needed to support rapid economic development, accompanied by a number of different air pollution emissions, including SO2, NO2 CO, and PM2.5. Based on awareness of environmental protection and sustainable energy development, researchers aimed to propose strategies to reduce the amount of air pollution generation in China derived from intense industrialization. The calculation of the correlation coefficient showed high values, so the correlation of air pollutants is highly related. This study aimed to realize the outcome of the implementation of air pollution reduction acts. Therefore, the grey relational grade and grey entropy were used to analyze the air pollution trend from 2013 to 2020 due to the specific function of the grey theory. The results of this study showed that the curves of SO2, PM2.5, and CO showed a clear decreasing trend. Furthermore, the SO2 curve still decreased in the periods of 2018–2020. The most important index for air pollutants is SO2 after the calculation of grey entropy, which corresponds to the fact that the coal resource is the main energy source in the fossil fuel industries for economic growth. However, the effects of these implementations regarding air pollution reduction sufficiently control air pollution emission, especially SO2 emission.

China Coastal Bulk (Coal) Freight Index Forecasting Based on an Integrated Model Combining ARMA, GM and BP Model Optimized by GA

The China Coastal Bulk Coal Freight Index (CBCFI) is the main indicator tracking the coal shipping price volatility in the Chinese market. This index indicates the variable performance of current status and trends in the coastal coal shipping sector. It is critical for the government and shipping companies to formulate timely policies and measures. After investigating the fluctuation patterns of the shipping index and the external factors in light of forecasting accuracy requirements of CBCFI, this paper proposes a nonlinear integrated forecasting model combining ARMA (Auto-Regressive and Moving Average), GM (Grey System Theory Model) and BP (Back-Propagation) Model Optimized by GA (Genetic Algorithms). This integrated model uses the predicted values of ARMA and GM as the input training samples of the neural network. Considering the shortcomings of the BP network in terms of slow convergence and the tendency to fall into local optimum, it innovatively uses a genetic algorithm to optimize the BP network, which can better exploit the prediction accuracy of the combined model. Thus, establishing the combined ARMA-GM-GABP prediction model. This work compares the short-term forecasting effects of the above three models on CBCFI. The results of the forecast fitting and error analysis show that the predicted values of the combined ARMA-GM-GABP model are fully consistent with the change trend of the actual values. The prediction accuracy has been improved to a certain extent during the observation period, which can better fit the CBCFI historical time series and can effectively solve the CBCFI forecasting problem.

Linkage mechanism of public transport subsidy: considering passenger ridership, cost, fare and service quality

ABSTRACT Under the long-term implementation of the low fare policy for public transport in China, the phenomenon of unsustainable subsidies is increasingly obvious. We take into account three related stakeholders including the public, operators and government, and comprehensively consider various factors affecting public transport subsidies, and establish a linkage mechanism of the subsidy based on passenger ridership, cost, fare and service quality. We propose a combination forecasting model (CFM) composed of quadratic exponential smoothing model (QESM) and Grey theory model (GM) to forecast passenger ridership. The results show that the CFM improves the forecast accuracy of passenger ridership, the factors considered in the linkage mechanism are more comprehensive, and the results are reasonable. By using this linkage mechanism, the government or operators can establish long-term or mid-term development plans to provide a systematic and theoretical reference, which is conducive to the healthy and stable development of public transport.