Grey System Research Articles

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.

The general conformable fractional grey system model and its applications

As a mathematical instrument, the fractional order grey system model can be employed to characterize uncertain real-world data. This paper presents new definitions for the generalized conformable fractional accumulation (GCFA) and the generalized conformable fractional difference (GCFD). For the first time, complex network theory explains the modelling mechanisms of accumulation. Then a novel generalized conformable fractional grey model (GCFGM(1,1)) is proposed based on the GCFA and GCFD, and the particle swarm optimizer (PSO) method is employed to optimize its fractional order. In this study, the proposed model is applied to the prediction of China’s overall energy consumption, natural gas consumption, and gross domestic product. Our research reveals that our proposed model achieves mean absolute percentage error (MAPE) values of less than 5% on real datasets from 9 different regions, outperforming other comparative models. These key empirical findings demonstrate the superiority of our model in accurately predicting relevant variables.

A novel fractional neural grey system model with discrete q-derivative

The challenge of predicting time series with limited data has evolved over time due to nonlinearity, complexity, and limited information. It can be perceived as a mapping of dynamical systems in one-dimensional space. This article proposes a neural grey system to tackle this challenge. The system enhances its ability to fit nonlinearity by employing polynomials, captures complexity through a fractional-order cumulant operator, and resolves information-poor uncertainty by utilizing grey system modeling techniques. The model effectively integrates research findings from neural computing, uncertainty theory, and complexity theory at a theoretical level. It accurately describes dynamic processes of complex systems. Additionally, we have reduced the complexity of calculations in the algorithm design. We selected a dataset of total retail sales of consumer goods to test the model’s validity and applicability. Our experiments demonstrate that the newly proposed grey forecasting model can effectively forecast time series with small samples, offering good forecasting outcomes and generalization ability.

Potential yields of two staple cereal crops worldwide under global warming

Most studies on model-estimated yield potential of crop under climate change are based on the principle of production function, for specific variety, from static biological dimension and at local or regional level, while few theoretically are based on time-series approach for world crop from dynamic evolutionary angle and on global scale. Therefore we conducted following research in 2019 to 2023 at Guiyang City of China: potential yields of rice and wheat worldwide by 2030 are projected creatively using Auto-regressive Integrated Moving Average and Trend-regressed model based on their history since 1961, in which the projections are tested by actual yields in recent two years and further validated by Gray System model; the impacts of global warming on past production of rice and wheat worldwide since 1961 are analyzed using binary regression model in which global mean temperature is treated as the independent variable while the crop yield as the dependent variable. Our results show that: in the future between 2022 and 2030, average yield of world rice is projected to be from 4876 kg/ha to 5195 kg/ha whereas that of world wheat from 3548 kg/ha to 3817 kg/ha; top (national) yield of world rice is projected to be from 10148 kg/ha to 10269 kg/ha whereas that of world wheat from 9914 kg/ha to 10042 kg/ha; or the ratio between average and top yields of world rice will be from 48.05% to 50.59% whereas that of world wheat from 35.79% to 38.01%; since 1961, global warming exerts a negative impact on average yield of world rice less than on its top, and a positive effect on average yield of world wheat while a negative one on its top, which partially drives the gap between average and top yields of world rice slightly narrowed and that of world wheat gradually closed. These findings indicate that: given that top yield is considered potential limit of average yield, to improve global production of these two staple cereal crops by 2030, the priorities should be given to rice as well as wheat worldwide in their both high and low yield countries.

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.

Small-sample linear profile error uncertainty assessment based on grey system

The uncertainty assessment of the profile error of the cam profile, as defined in the national standard method, is difficult to carry out under conditions of small sample size and absence of probability distribution assumptions. This paper proposes a small-sample assessment model for the uncertainty of the profile error based on grey system. Firstly, the coordinate transformation is conducted using Vector Alignment Method to reduce systematic errors, and the non-uniform rational B-splines curve interpolation is utilized to fit the cam profile curve and perform error assessment. Subsequently, based on the error assessment results, Grey Information Measurement Model (GIMM) for the uncertainty of the profile error in small samples is established. This model employs Grey Relational Analysis to eliminate outliers and evaluates the uncertainty of the profile error by solving grey correlation coefficients. Maximum-Minimum Information Measure Method is used to assess the optimal sample size. Finally, numerical experiments and experimental tests were conducted on the uncertainty of camshaft profile error in automobiles. A total of 15 sets of profile data were compared with Guide to the Representation of Uncertainty in Measurement (GUM) and Monte Carlo Method (MCM) under different sample sizes. The results showed that GIMM achieved evaluation with only 8 sets of data samples under small sample and poor information conditions, with an uncertainty of 0.6338 μm, compared to 0.6346 μm for GUM and 0.6391 μm for MCM. The acceptance rate of GIMM reached 95.2%. This model outperforms other methods, providing a simplified and reliable assessment of cam profile error uncertainty.

Low-Cost Grey Water Treatment System Design And Treatability Studies In Relation To Recycling And Reuse In Rural Area of Angul District of Odisha

As global water scarcity intensifies, there is a growing need for sustainable water management solutions, particularly in rural areas where access to clean water is often limited. This study focuses on the development of a low-cost greywater treatment system tailored for rural settings, emphasizing the recycling and reuse of greywater. The research encompasses the design, implementation, and treatability studies of the proposed system, aiming to provide an affordable and effective solution for improving water sustainability in resource-constrained communities. The designed greywater treatment system incorporates cost-effective technologies and locally available materials to ensure its feasibility in rural contexts. The treatability studies explore the system's efficiency in removing contaminants commonly found in greywater, evaluating its potential to meet water quality standards suitable for various non-potable applications. Emphasis is placed on the system's adaptability to local conditions, promoting community engagement and ownership for long-term sustainability. By addressing the unique challenges faced by rural areas, this research contributes to the broader goal of achieving water security through decentralized, affordable, and environmentally conscious greywater treatment solutions. The findings from this study can inform policymakers, engineers, and local communities on practical approaches to enhance water quality, reduce environmental impact, and promote water recycling and reuse in rural settings.

Correction to: Exploring the Methodological Foundation of a Systemic Approach in Grey Systems Theory

Correction to: Exploring the Methodological Foundation of a Systemic Approach in Grey Systems Theory

Uncertain Causality Analysis of Critical Success Factors of Special Education Mathematics Teaching

Abstract This study determines the critical success factors of teaching mathematics to special education (SPED) students wherein a list of success factors is identified through a literature survey and analyzes the causal relationships among the identified factors to evaluate the key success factors using the integration of the grey system theory and decision-making trial and evaluation laboratory (DEMATEL), named as the grey-DEMATEL. Results reveal that professional development (PD), institutional support (IS), and individual factors (IF) are categorized as net causes. They influence other factors considered as net effects, including mentoring, self-efficacy, teaching capacity, student feedback, teaching knowledge, instructional accommodation, and use of technology. Among those net causes, only PD yields as the key factor, while IS and IF are the minor key factors. Thus, decision-makers must allocate resources that provide PD to teachers in delivering SPED mathematics teaching. A sensitivity analysis suggests these findings are robust to linguistic evaluation scale changes. The insights outlined in this study would aid educational managers and decision-makers of educational institutions in carefully designing initiatives to improve the quality of mathematics education provided to SPED students. Some potential directions for future research agenda are also discussed.

Hyperspectral estimation model of soil organic matter content based on principal gradient grey information

PurposeIn order to explore a new estimation approach of hyperspectral estimation, this paper aims to establish a hyperspectral estimation model of soil organic matter content with the principal gradient grey information based on the grey information theory.Design/methodology/approachFirstly, the estimation factors are selected by transforming the spectral data. The eigenvalue matrix of the modelling samples is converted into grey information matrix by using the method of increasing information and taking large, and the principal gradient grey information of modelling samples is calculated by using the method of pro-information interpolation and straight-line interpolation, respectively, and the hyperspectral estimation model of soil organic matter content is established. Then, the positive and inverse grey relational degree are used to identify the principal gradient information quantity of the test samples corresponding to the known patterns, and the cubic polynomial method is used to optimize the principal gradient information quantity for improving estimation accuracy. Finally, the established model is used to estimate the soil organic matter content of Zhangqiu and Jiyang District of Jinan City, Shandong Province.FindingsThe results show that the model has the higher estimation accuracy, among the average relative error of 23 test samples is 5.7524%, and the determination coefficient is 0.9002. Compared with the commonly used methods such as multiple linear regression, support vector machine and BP neural network, the hyperspectral estimation accuracy of soil organic matter content is significantly improved. The application example shows that the estimation model proposed in this paper is feasible and effective.Practical implicationsThe estimation model in this paper not only fully excavates and utilizes the internal grey information of known samples with “insufficient and incomplete information”, but also effectively overcomes the randomness and grey uncertainty in the spectral estimation. The research results not only enrich the grey system theory and methods, but also provide a new approach for hyperspectral estimation of soil properties such as soil organic matter content, water content and so on.Originality/valueThe paper succeeds in realizing both a new hyperspectral estimation model of soil organic matter content based on the principal gradient grey information and effectively dealing with the randomness and grey uncertainty in spectral estimation.

Durability evaluation and lifetime prediction of recycled coarse aggregate self-compacting concrete after freeze-thaw and sulfate erosion coupling

This paper aims to study the durability of recycled coarse aggregate self-compacting concrete (RCASCC) after being subjected to freeze-thaw and sulfate coupling. The durability comprehensive evaluation index of RCASCC based on the entropy weight method was defined, and the variation law of the durability comprehensive evaluation index concerning RCA substitution rate, freeze-thaw environment, and coupling frequency was obtained. GM (1, 1) model based on grey system theory was used to evaluate the durability of RCASCC under the coupling effects of freeze-thaw and sulfate attack. The results show that there are some differences in the accuracy of the four GM (1, 1) models. It is recommended that the EGM model can be used to predict the durability of the RCASCC under the coupled effects of freeze-thaw and sulfate erosion. Moreover, the working life of RCASCC under the coupling effects in the outdoor environments of northeast, north, and northwest China was calculated, when the comprehensive evaluation index of durability decreases to 75% as the failure criterion. A multi-criteria analysis employing TOPSIS and VIKOR methods was conducted to evaluate the durability of RCASCC under these coupling effects. A multi criteria analysis was conducted on the durability of RCASCC under the coupling effects of freeze-thaw and sulfate erosion using TOPSIS and VIKOR methods. Under these two rankings, the splitting tensile strength is the index that changes the most after coupling effects, while the compressive strength changes relatively little. The importance of dynamic elastic modulus and weight is different due to different materials and evaluation methods. RC100-H typically exhibits the largest variation, while NC-H and RC50-NM typically exhibit smaller variations.

STUDY ON CONSTRUCTION MONITORING AND CONTROL OF MULTI-SPAN PRESTRESSED CONCRETE CONTINUOUS BEAM BRIDGE

This article focuses on the construction monitoring and control of a pre-stressed concrete continuous beam bridge, consisting of 13 spans. The goal is to ensure that the bridge structure meets the design requirements throughout the entire construction process. By comparing the theoretical and measured values of the bridge’s alignment and stress during the cantilever construction, closure, and completion phases, it can be observed that the deflection deformation of the bridge is generally in agreement with the theoretical calculations. After the completion of the entire bridge, the measured elevations of each section have an error range of -18mm to 20mm compared to the design elevations, which satisfies the specifications. A comparison analysis of the measured and theoretical stress values at the root and mid-span of the cantilever indicates that the stress difference at the root is within the range of -0.2MPa to 0.2MPa, and the stress differences at the mid-span after completion are 0.03MPa (upper) and 0.09MPa (lower), all of which meet the structural design and code requirements. By establishing a gray GM (1,1) model and using gray system theory, the deflection error during the construction process is predicted and controlled. The prediction accuracy of different methods is compared to determine a reasonable prediction method suitable for long-span pre-stressed continuous beam bridges, providing reference for similar engineering projects.

Accelerating renewables: Unveiling the role of green energy markets

The innovative local grey forecasting model for energy production prediction emerges as a pivotal tool in the shift towards renewable energy, embodying the essence of adaptability and precision for the urgent need for sustainable solutions. The integration of such forecasting models is imperative for comprehending the complexities of energy production and market dynamics, ultimately facilitating a seamless transition to a sustainable energy future. Given this, the current study investigates the connections between renewable energy consumption and its determinants, focusing on the role of green energy markets across 12 countries from 2003 to 2021. Employing a novel quantile regression approach, this study analyzes the impact of green energy markets, GDP per capita, urban population, and environmental policies on renewable energy consumption, introducing a renewable energy consumption index for novel insights. Rooted in grey system theory, this study offers a unique approach to predicting energy outputs, crucial for guiding the strategic decisions in green energy markets. The quantile regression analysis reveals that green energy markets have a differential impact on renewable energy consumption whereas Renewable Energy Installed Capacity positively affects renewable energy consumption across all quantiles. Environmental Stringency Policies consistently show a negative impact across all quantiles. By providing reliable predictions of energy production, these findings empower policymakers, investors, and operators to make strategic, data-driven decisions that align with the fluctuating demands of the market and the overarching goals of sustainability and environmental stewardship.

Understanding the effect of non-carbon-based nanoinclusions on chloride penetration resistance and chloride binding capacity of ultra-high performance cementitious composite

Ultra-high performance cementitious composite (UHPCC) with high durability has been widely employed to prevent the chloride corrosion of reinforced concrete structures exposed to the marine environment. Adding nanoinclusions is expected to fundamentally eliminate the defects (i.e., pores and microcracks) caused by the formulation principle of UHPCC, thereby reducing connected pathways for chloride diffusion. Furthermore, the diffusion resistance of chloride ions largely depends on the chloride binding capacity (CBC) of cement-based composites. Regrettably, the effects of non-carbon-based nanoinclusions on chloride penetration resistance (CPR) and CBC of UHPCC still lack comparison and analysis. This study investigated the effect of non-carbon-based nanoinclusions on the CPR and CBC as well as microstructures of UHPCC, including nano-SiO2 (NS), nano-ZrO2 (NZ), nano-TiO2 (NT), and boron nitride (BN). Correlation between pore structure parameters and CPR of composites was established based on the grey system theory. The results of rapid chloride migration (RCM) tests indicate that due to the incorporation of 1 % NS, 3 % SiO2-coated NT, 1 % NZ, and 0.5 % BN, the CPR of UHPCC is improved by 89.6 %, 66.5 %, 87.5 %, and 73.6 %, respectively. The results of the grey correlation analysis highlight the critical role of harmless and less harmful pores with a diameter below 50 nm in preventing the diffusion of chloride ions. The modifying mechanisms of nanoinclusions can be attributed to the nano-core effects, contributing to reducing chloride diffusion pathways and increasing disconnectivity and tortuosity of the pathways. Firstly, nanoinclusions can reduce pores and microcracks, improving the homogeneity of pore size distribution and inhibiting the growth of CH crystals, thus enhancing the compactness of the cement paste matrix. Secondly, nanoinclusions enriched in the interfacial transition zone (ITZ) can improve the homogeneity of ITZ by optimizing the Ca/Si ratios of C–S–H gels and improving the interfacial bond between cement paste and aggregate. Additionally, Langmuir and Freundlich isotherms can accurately describe the CBC of composites. Only NS among nanoinclusions can significantly improve the CBC of UHPCC. This can be attributed to the involvement of pozzolanic NS in cement hydration, promoting the formation of C–S–H gels for physically adsorbing chloride ions while increasing the diffusion resistance of chloride ions in the pathways.