Grey System Theory Research Articles

Experimental Study and Machine Learning-Based Prediction of the Abrasion Resistance of Manufactured Sand Concrete

To systematically analyze the impact of manufactured sand on the abrasion resistance of concrete, this paper investigates the correlation between sand type, sand ratio, stone powder content, compressive strength, and the abrasion resistance of manufactured sand concrete. Grey correlation analysis was conducted to assess the impact priority of each factor affecting the abrasion resistance, and prediction models for the abrasion resistance were developed using XGBoost, random forest, AdaBoost, and gradient boosting. The results indicate that compared to river sand concrete, C30 and C40 concrete prepared with limestone and diabase manufactured sand has 20% higher abrasion resistance due to the presence of stone powder and higher roughness and solidity. Within the range of 0.40 to 0.44, a lower sand ratio leads to higher abrasion resistance. For concrete prepared with manufactured sand containing 5% to 11% stone powder, the best abrasion resistance can be attained at a stone powder content of 9%, and microscopic analysis suggests the highest concrete density at this level. According to grey system theory, the influence of each affecting factor on the abrasion resistance follows the order: sand ratio > crushing value > roughness > compressive strength > stone powder content > 0.6. Compared to gradient boosting, random forest, and AdaBoost models, the XGBoost model adopted in this study showed relatively higher R2 and lower RMSE in both the training and testing sets, which proved its higher accuracy in predicting the abrasion loss of manufactured sand concrete. The machine learning models offer some guidance for predicting and enhancing the abrasion resistance of manufactured sand concrete in practical engineering.

Grey Bayesian network model for electromagnetic pulse vulnerability assessment of engine system

Firstly, in view of the uncertainty of electromagnetic pulse vulnerability assessment of engine system caused by limited experimental data and incomplete information, a grey Bayesian network model is proposed by introducing interval grey numbers in grey system theory to characterize the uncertainty of sensitivity threshold and fault logic relationship of engine system components, so as to improve the processing ability of Bayesian network for uncertain information. Secondly, taking broadband high-power microwave as an example, the interval grey number failure probability of engine system and the posterior failure probability of sensor interval grey number are calculated. The former represents the survivability of the engine system as a whole under strong electromagnetic pulse, and the latter reflects the vulnerability order of each sensor under the condition of engine system failure. The evaluation conclusion can provide a reference for the design of vehicle electromagnetic protection.

Online Unmanned Ground Vehicle Path Planning Based on Multi-Attribute Intelligent Reinforcement Learning for Mine Search and Rescue

Aiming to improve the efficiency of the online process in path planning, a novel searching method is proposed based on environmental information analysis. Firstly, a search and rescue (SAR) environmental model and an unmanned ground vehicle (UGV) motion model are established according to the characteristics of a mining environment. Secondly, an online search area path-planning method is proposed based on the gray system theory and the reinforcement learning theory to handle multiple constraints. By adopting the multi-attribute intelligent (MAI) gray decision process, the action selection decision can be dynamically adjusted based on the current environment, ensuring the stable convergence of the model. Finally, experimental verification is conducted in different small-scale mine SAR simulation scenarios. The experimental results show that the proposed search planning method can capture the target in the search area with a smoother convergence effect and a shorter path length than other path-planning algorithms.

Quantifying drivers of virtual reality acceptance in tourism planning using a grey system theory-based approach

PurposeThis study examines the role of Virtual Reality (VR) in tourism marketing, analyzing its psychological effects, demographic differences and factors influencing adoption. It seeks to identify and measure the main drivers and obstacles to VR acceptance, improving predictions of adoption trends in tourism planning and experiences across various demographics.Design/methodology/approachThis study employs a quantitative method, blending statistical analysis with Grey System Theory. It analyzes data from VR-experienced participants using ANOVA, regression models and dynamic grey relational analysis to produce actionable insights. Data were collected from July to September 2023 from participants experienced with VR in tourism.FindingsUser engagement, positive word-of-mouth and a desire for immersive experiences drive VR acceptance, especially among frequent travelers and tech enthusiasts. While VR can enhance trip enjoyment, its long-term impact varies. To increase VR adoption in tourism, strategies include targeting frequent travelers and online users, creating accessible and engaging content, leveraging positive feedback and partnering with influencers to develop interactive VR experiences that mitigate travel planning uncertainties and attract specific user groups.Practical implicationsThe study demonstrates how VR can expand access to tourism for those unable to travel, enhance engagement and streamline planning. By leveraging VR in marketing, providers can create immersive, targeted content, reduce uncertainties, attract tech-savvy travelers and boost appeal through influencer partnerships.Originality/valueThis study pioneers the integration of Grey System Theory with traditional statistical methods to elucidate the dynamics of VR adoption in tourism. By uniquely combining ANOVA, regression models and dynamic grey relational analysis, it offers a precise model to interpret complex data, highlighting the nuanced effects of demographic factors on technology acceptance. This approach not only advances the analytical frameworks in tourism marketing but also provides critical insights into the demographic and psychological drivers of VR adoption.

Prediction of concrete sulfuric acid corrosion evaluation index model based on grey system theory

Prediction of concrete sulfuric acid corrosion evaluation index model based on grey system theory

New discrete fractional accumulation Grey Gompertz model for predicting carbon dioxide emissions

Predicting carbon dioxide emissions is crucial for addressing climate change and achieving environmental sustainability. Accurate emission forecasts provide policymakers with a basis for evaluating the effectiveness of policies, facilitating the design and implementation of emission reduction strategies, and helping businesses adjust their operations to adapt to market changes. Various methods, such as statistical models, machine learning, and grey prediction models, have been widely used in carbon dioxide emission prediction. However, existing research often lacks comparative analysis with other forecasting techniques. This paper constructs a new Discrete Fractional Accumulation Grey Gompertz Model (DFAGGM(1,1) based on grey system theory and provides a detailed solution process. The Whale Optimization Algorithm (WOA) is used to find the hyperparameters in the model. By comparing it with five benchmark models, the effectiveness of DFAGGM(1,1) in predicting carbon dioxide emissions data for China and the United States is validated.

Grey relations

PurposeThe goal of this article is to introduce the notion of a grey relation between grey sets using grey numbers.Design/methodology/approachThis study uses the grey number to create novel ideas of grey sets. We suggest several operations that can be performed on it, including the union, intersection, join, merge, and composition of grey relations. In addition, we present the definitions of reflexive, symmetric, and transitive grey relations and analyze certain characteristics associated with them. Furthermore, we formulate the concept of the grey equivalence relation, apply it to the study of the grey equivalence class over the grey relation, and go over some of its features.FindingsWe present new algebraic aspects of grey system theory by defining grey relations and then analyzing their characteristic features.Practical implicationsThis paper proposes a new theoretical direction for grey sets according to grey numbers, namely, grey relations. This paper proposes a new theoretical direction for grey sets according to grey numbers, namely, grey relations. As such, it can be applied to create rough approximations as well as congruences in algebras, topologies, and semigroups.Originality/valueThe presented notions are regarded as new algebraic approaches in grey system theory for the first time. Additionally, some fundamental operations on grey relations are also investigated. Consequently, different types of grey relations, such as reflexive, symmetric, and transitive relations, are discussed. Then, the grey equivalence class derived from the grey equivalence relation is demonstrated, and its properties are examined.

Real-time application of grey system theory in intelligent traffic signal optimization

Real-time application of grey system theory in intelligent traffic signal optimization

Prediction of building carbon emission based on grey Markov theory

The increase in carbon emissions in the construction industry is one of the factors that lead to environmental problems and hinder the sustainable development of society and the construction industry. Therefore, there is an urgent need to rationally control carbon emissions by formulating scientific and effective energy-saving and emission reduction policies so as to safeguard the development of the industry. However, because of the uncertainty and complexity of CO2 emissions, more reliable prediction and assessment tools are needed to comprehensively analyze and predict CO2 emissions. This study combines the gray system theory and Markov principle to give full play to the advantages of the two methods, using Markov theory to determine the state transition probability, find out the characteristics of carbon emissions in the construction industry, and form a suitable gray Markov prediction model. The feasibility of the prediction model is demonstrated by calculating and examining the prediction model using data related to carbon emissions from the U.S. buildings from 2002 to 2022, which greatly improves the accuracy of CO2 emissions prediction.

Grey System Model GM (1,1) for Predicting Student Performance

The purpose of this study is to predict student quiz assignments in the General Physics I course during one semester using the Grey system GM (1,1) model. The prediction model integrates the first-order two-variable grey differential equation model from the Grey system theory. Additionally, a MATLAB program for the GM (1,1) prediction model was developed. The predicted results were compared with the original GM (1,1) model, showing good agreement.

Funding Innovation and Risk: A Grey-Based Startup Investment Decision.

As found in behavioral decision theory, venture capitalists (VCs) rely on heuristics and bias, owing to their bounded rationality, either by limited alternatives or information and resources. India's booming startup scene challenges VCs in decision-making owing to information overload from numerous evolving ventures, which hinders informed judgment. VC investment behavior, due diligence, and cognitive factors related to decision-making have always drawn the attention of researchers. We provide an alternative approach for an optimal decision by VCs by identifying the attributes that influence investment or funding decisions at an early stage of a venture in tech-based industries. Through a literature review, we identify eight attributes, both on internal and external criteria, that venture investors consider when making investment decisions. Based on interviews with 20 experts, we further identify eight key tech-based sectors. Using grey system theory, we then determine the rankings of eight tech startups for investors' early-stage investment decisions. This study presents a linguistic variable-based approach of grey numbers to decide weights and ratings, the grey possibility degree to compare and rank different tech startups, and based on the results, suggests the ideal tech startup. We find that agritech ranks first; thus, investors should prefer venturing into such startups for early-stage investment. E-commerce and edutech ranked second and third, respectively, followed by electric vehicle infrastructure, insurtech, fintech, space tech, and software as a service.

A generalized grey model with symbolic regression algorithm and its application in predicting aircraft remaining useful life

As a sparse data analysis method, a grey model faces challenges in interpretability for its effective application in uncertain systems. This study proposes a generalized grey model (GGM) based on symbolic regression, designed to improve the intelligence and adaptability of grey models. The GGM serves as a unified framework, integrating various grey model families and addresses regression challenges to determine the model structure. Symbolic regression in the GGM identifies symbolic input-output relationships, offering an interpretable approach for structure determination. By leveraging the non-uniqueness principle in grey system theory and employing structural penalty parameters, the model balances complexity and interpretability. A comparative analysis between GGM and conventional grey function models is conducted focusing on the differences in modeling, structure identification, and parameter optimization. Validation on the M3 competition dataset demonstrated the GGM's superior performance, achieving a significant reduction in prediction error compared to other grey forecasting models. Additionally, a rigorous analysis of aircraft lifespan data underscored the robustness and accuracy of GGM in practical engineering applications.

Study on the Effect of Fly Ash on Mechanical Properties and Seawater Freeze–Thaw Resistance of Seawater Sea Sand Concrete

When using seawater and sea sand as mixes, the mechanical properties and durability of concrete are adversely affected because the raw materials themselves contain harmful ions. Fly ash is the tailings formed in the process of industrial production, the use of which does not require the burning of clinker, reducing CO2 emissions. Moreover, it belongs to a new type of cementitious materials with low emissions and high environmental protection. Fly ash enhances the properties of concrete and reduces the effect of harmful ions on concrete. Based on the above considerations, the corresponding specimens were prepared and subjected to cubic compressive strength, flexural strength, and seawater freezing and thawing resistance tests by using fly ash admixture as the main variable. A combination of macro-analysis and micro-analysis was used to investigate the effect of fly ash on the performance of seawater sea sand concrete. The results showed that fly ash significantly enhanced the mechanical properties and resistance to seawater freezing and thawing of seawater sea sand concrete. The best improvement in compressive strength and resistance to seawater freezing and thawing was achieved at a substitution rate of 20%. The maximum increase in compressive strength was 13.22%. The maximum reduction in mass loss rate was 57.26% and the strength loss rate was 43.14% after the specimens were subjected to seawater freezing and thawing 75 times. The maximum enhancement in flexural strength was 17.06% for a substitution rate of 10%. Through microanalysis, it can be seen that the incorporation of coal ash can enhance the compactness of concrete through the microaggregate effect as well as the volcanic ash reaction to promote the secondary hydration reaction, so as to strengthen the seawater freeze–thaw resistance of seawater sea sand concrete. Finally, the damage prediction model established using the mean GM (1, 1) model of gray system theory meets the requirements of the first level of prediction accuracy and can accurately predict the damage of seawater sea sand concrete under seawater freezing and thawing.

Reinforcement Learning Empowered Grey System Theory: Advancing Intelligence in Traffic Prediction for Wireless Sensor Networks

Reinforcement Learning Empowered Grey System Theory: Advancing Intelligence in Traffic Prediction for Wireless Sensor Networks

Near miss prediction in commercial aviation through a combined model of grey neural network

Owing to unprecedented level of safety management, there have been few commercial aviation accidents recently, obstructing accurate prediction of safety trends. The innovative approach of proactive safety management was employed to replace reactive safety management for predicting commercial aviation near misses. In light of positive correlation between flight time and commercial aviation near-miss with different severity levels, this study aims to predict total / serious / non-serious near-miss per million flight hours. Considering grey system theory is good at prediction without adequate data, and artificial neural networks can handle nonlinear data well, the combined models of grey neural networks were developed for forecasting three data sequences over time respectively. Based on the empirical results of commercial aviation near-miss forecasting, BP neural network had the potential to enhance accuracy of grey prediction model. The three statistical measures of MAE/MSE/MAPE denoted the combined model of grey neural network outperformed the single model of GM (1, 1) or BP neural network. Predictions with a high degree of accuracy is beneficial to determining actual trends of commercial aviation near-misses at present or in future. Quantitative data will be offered for making specific decisions on maintaining preventive measures or promoting commercial aviation safety performance by introducing additional management actions.

Spatial degradation characteristics and numerical prediction for life of MWCNTs-reinforced concrete by salt freezing

Nanotechnology is of great importance towards the multiscale enhancement of mechanical strength, anti-cracking, corrosion and/or abrasion resistance of concrete. To optimize the durability of concrete structure in a frigid and saline environment, the multi-walled carbon nanotubes (MWCNTs) were introduced along with the evaluation of spatial deterioration characteristics and numerical prediction for its service life in this work. The degree and mechanism of salt freeze-thaw damage relief of MWCNTs-reinforced concrete (CNTC) were investigated by establishing relief rate (Rf, RE and Rp) and damage parameters (Df, DE and Dp) in terms of compressive strength, relative dynamic elastic modulus and porosity. Macroscopic results showed that the presence of MWCNTs substantially delayed the evolution of spatial damage of concrete. The optimum dosage of MWCNTs for the degradation mitigation was also determined to be 0.05 wt% from the microscale scanning electron microscopy, mercury intrusion and mesoscale X-CT scanning techniques, which was responsible to the well-dispersed MWCNTs without entanglement. The synergic effects of filling, nucleation and bridging of MWCNTs in hydrating concrete decreased the porosity and effectively inhibited micro-crack propagation of concrete under the salt freezing effects. Besides, the evolutionary relationships between Df, DE and Dp were established, and a three parameters-dependent prediction index D had been established on the basis of the entropy method and grey system theory GM(1,1) model. The experimental results were in good agreement with the prediction model, and further showed that the salt- freeze-thaw damage of CNTC was significantly reduced.

Forecasting End-of-Life Vehicle Generation in the EU-27: A Hybrid LSTM-Based Forecasting and Grey Systems Theory-Based Backcasting Approach

End-of-life vehicle (ELV) forecasting constitutes a crucial aspect of sustainable waste management and resource allocation strategies. While the existing literature predominantly employs time-series forecasting and machine learning methodologies, a dearth of studies leveraging deep learning techniques, particularly Long Short-Term Memory (LSTM) networks, is evident. Moreover, the focus on localized contexts within national or municipal boundaries overlooks the imperative of addressing ELV generation dynamics at an international scale, particularly within entities such as the EU-27. Furthermore, the absence of methodologies to reconcile missing historical data presents a significant limitation in forecasting accuracy. In response to these critical gaps, this study proposes a pioneering framework that integrates grey systems theory (GST)-based backcasting with LSTM-based deep learning methodologies for forecasting ELV generation within the EU until 2040. By introducing this innovative approach, this study not only extends the methodological repertoire within the field but also enhances the applicability of findings to supranational regulatory frameworks. Moreover, the incorporation of backcasting techniques addresses data limitations, ensuring more robust and accurate forecasting outcomes. The results indicate an anticipated decline in the recovery and recycling of ELVs, underscoring the urgent need for intervention by policymakers and stakeholders in the waste management sector. Through these contributions, this study enriches our understanding of ELV generation dynamics and facilitates informed decision-making processes in environmental sustainability and resource management domains.

On some operations on grey graphs with application

PurposeThe objective of this paper is to formulate the precise meanings of grey graphs and examine some of their properties.Design/methodology/approachThis article introduces innovative concepts of grey sets based on the grey number. We establish the grey graphs and examine their essential properties as isomorphisms of these graphs. Additionally, we explore the notion of a grey-complete graph and demonstrate certain properties of self-complementary grey-complete graphs.FindingsWe showcase novel facets of grey system theory through the establishment of the structures of grey graphs, and the subsequent analysis of their distinctive traits.Practical implicationsThis article provides us with a new theoretical direction for grey system theory according to grey numbers. Thus, we present test examples that explain the routes between cities and the electrical wires between homes. Furthermore, the concept of grey graphs can be applied in several areas of engineering, computer science, neural networks, artificial intelligence, and medical diagnosis.Originality/valueThe proposed concepts are considered novel mathematical directions in grey system theory for the first time. Some operations of grey graphs are also explored.

Tourism Competitiveness and Mobile Data: A Grey Relational Analysis for European Entrepreneurship Pre & During Pandemic

Abstract The tourism country’s competitiveness is important especially when countries strive for bigger market shares as European countries are. The COVID-19 pandemic has had devastating effects on tourism and the entire tourism industry must be rethinking and reshaping given some opportunities and challenges to the entrepreneurs, local communities, local administrations, governments of competitiveness destinations. In this paper we measured if the Google and Apple mobility indices cand predict (or not) the movement of TTCI during pandemic compared with pre-pandemic TTCI ranks by using grey system theory and statistical methods. The aim of the paper is to validate the Grey Relational Analysis (GRA) as identifier of the good predictor (statistically significant for p-value< 0.05) for European tourism competitiveness (measured by TTCI) in particular conditions, especially before and during COVID-19 pandemic time by take into consideration the Google Mobility and Apple Mobility data and their relationship with overnight stays for 11 European countries. To validate the GRA as method for good and accurate predictor for tourism competitiveness for TTCI 2021 (during COVID-19 pandemic time) a GLM – General Linear Model ANOVA with interaction effects and Tukey HSD Post Hoc Multiple Comparisons was applied. Our results validated the powerful of GRA for tourism competitiveness, statistically significant according to GLM with interaction effects, and emphasis (especially for entrepreneurs) that the overnight stays will give the right rank on top of tourism competitiveness, prior to other ITC support as Google and Apple Mobility indices proved by reflection of overnights on TTCI 2021 – during pandemic.

The grey decision model and its application based on generalized greyness of interval grey number

PurposeIn order to solve the decision-making problem that the attributive weight and attributive value are both interval grey numbers, this paper tries to construct a multi-attribute grey decision-making model based on generalized greyness of interval grey number.Design/methodology/approachFirstly, according to the nature of the generalized gresness of interval grey number, the generalized weighted greyness distance between interval grey numbers is given, and the transformation relationship between greyness distance and real number distance is analyzed. Then according to the objective function that the square sum of generalized weighted greyness distances from the decision scheme to the best scheme and the worst scheme is the minimum, a multi-attribute grey decision-making model is constructed, and the simplified form of the model is given. Finally, the grey decision-making model proposed in this paper is applied to the evaluation of technological innovation capability of 6 provinces in China to verify the effectiveness of the model.FindingsThe results show that the grey decision-making model proposed in this paper has a strict mathematical foundation, clear physical meaning, simple calculation and easy programming. The application example shows that the grey decision model in this paper is feasible and effective. The research results not only enrich the grey system theory, but also provide a new way for the decision-making problem that the attributive weights and attributive values are interval grey numbers.Practical implicationsThe decision-making model proposed in this paper does not need to seek the optimal solution of the attributive weight and the attributive value, and can save the decision-making labor and capital investment. The model in this paper is also suitable for the decision-making problem that deals with the coexistence of interval grey numbers and real numbers.Originality/valueThe paper succeeds in realizing the multi-attribute grey decision-making model based on generalized gresness and its simplified forms, which provide a new method for grey decision analysis.