Search Results (36)

Traditional cementitious coating materials struggle to meet the performance criteria for protective coatings in complex environments. This study developed a polymer-modified cement-based coating material with polymer, silica fume (SF), and quartz sand (QS) as the principal admixtures. It also investigated the influence of material composition on the coating’s mechanical properties, durability, interfacial bond characteristics with concrete, and the durability enhancement of coated concrete. The results demonstrated that compared with ordinary cementitious coating material (OCCM), the interfacial bonding performance between 3% Styrene Butadiene Rubber Powder (SBR) coating material and concrete was improved by 42%; the frost resistance and sulfate erosion resistance of concrete protected by 6% polyurethane (PU) coating material were improved by 31.5% and 69.6%. The inclusion of polymers reduces the mechanical properties. The re-addition of silica fume can lower the porosity while increasing durability and strength. The coating material, mixed with 12% SF and 6% PU, exhibits mechanical properties not lower than those of OCCM. Meanwhile, the interfacial bonding performance and durability of the coated concrete have been improved by 45% and 48%, respectively. The grey relational analysis indicated that the coating material with the best comprehensive performance is the one mixed with 12% SF + 6% PU, and the grey correlation degree is 0.84. Full article

The accurate prediction of the residual strength of defective pipelines is a critical prerequisite for ensuring the safe operation of oil and gas pipelines, and it holds significant implications for the pipeline’s remaining service life and preventive maintenance. Traditional machine learning algorithms often fail to comprehensively account for the correlative factors influencing the residual strength of defective pipelines, exhibit limited capability in extracting nonlinear features from data, and suffer from insufficient predictive accuracy. Furthermore, the predictive models typically lack interpretability. To address these issues, this study proposes a hybrid prediction model for the residual strength of defective pipelines based on Bayesian optimization (BO) and eXtreme Gradient Boosting (XGBoost). This approach resolves the issues of excessive iterations and high computational costs associated with conventional hyperparameter optimization methods, significantly enhancing the model’s predictive performance. The model’s prediction performance is evaluated using mainstream metrics such as the Mean Absolute Percentage Error (MAPE), Coefficient of Determination (R²), Root Mean Square Error (RMSE), robustness analysis, overfitting analysis, and grey relational analysis. To enhance the interpretability of the model’s predictions, reveal the significance of features, and confirm prior domain knowledge, Shapley additive explanations (SHAP) are employed to conduct the relevant research. The results indicate that, compared with Random Forest, LightGBM, Support Vector Machine, gradient boosting regression tree, and Multi-Layer Perceptron, the BO-XGBoost model exhibits the best prediction performance, with MAPE, R², and RMSE values of 5.5%, 0.971, and 1.263, respectively. Meanwhile, the proposed model demonstrates the highest robustness, the least tendency for overfitting, and the most significant grey relation degree value. SHAP analysis reveals that the factors influencing the residual strength of defective pipelines, ranked in descending order of importance, are defect depth (d), wall thickness (t), yield strength (σ_y), external diameter (D), defect length (L), tensile strength (σ_u), and defect width (w). The development of this model contributes to improving the integrity management of oil and gas pipelines and provides decision support for the intelligent management of defective pipelines in oil and gas fields. Full article

Accurate State of Health (SOH) estimation of lithium-ion batteries (LIBs) is critical for ensuring the safety of electric vehicles and improving the reliability of battery management systems (BMS). However, the use of individual health features (HFs) and the selection of hyperparameters can increase the data processing burden on the BMS and reduce the accuracy of data-driven models. To address the above issue, this paper proposes a novel SOH estimation method for lithium-ion batteries based on the PSO–GWO–LSSVM prediction model with multi-dimensional health feature extraction. To comprehensively capture the battery aging mechanisms, four categories of health features—time, energy, similarity, and second-order features—are extracted from the LIBs charging segments. The correlation between HFs and SOH is comprehensively evaluated through Pearson and Spearman correlation analyses, followed by Gaussian filtering and outlier detection to enhance feature quality. With strong generalization and robustness, least squares support vector machine (LSSVM) is widely applied to nonlinear computations and function approximation. To improve LSSVM model accuracy and efficiency, this paper develops a novel prediction model that uses particle swarm optimization (PSO) combined with grey wolf optimization (GWO) algorithms to optimize the LSSVM model. The generalization performance of the proposed method is validated through comparative experiments using a battery dataset provided by the Center for Advanced Life Cycle Engineering (CALCE) Research Center at the University of Maryland. Experimental results show that the coefficient of determination (R²) consistently exceeds 0.985, with the average absolute error in SOH prediction for four batteries remaining around 0.5%. The comparative experiments demonstrate that the proposed method has a certain degree of accuracy, robustness, and generalization capability. Full article

Leveraging industrial solid waste for the production of cementitious materials holds the potential to curtail the consumption of traditional cement. Orthogonal tests were conducted to investigate the effects of five factors, namely, steel slag–slag mass ratio, desulfurization gypsum content, water glass modulus, alkali content, and water–binder ratio, on the working performance, mechanical properties, and durability of alkali-activated ternary solid waste cementitious materials. Grey correlation degree (GCD) analysis was employed to investigate the impact of different factors on performance, while the micro-reaction mechanism was elucidated through X-ray diffraction (XRD) patterns and Fourier infrared spectroscopy (FT-IR) spectra. Principal component analysis (PCA) was employed to conduct dimensionality reduction on the fluidity, compressive strength, flexural strength, and 28-day drying shrinkage of the cementitious materials for assessing the comprehensive performance of the ternary solid waste cementitious material. The highest score was achieved with a steel slag mass ratio of 1:2, a desulfurization gypsum content of 10%, a water glass modulus of 1.0, an alkali content of 3%, and a water–binder ratio of 0.4 due to the excellent properties of the resulting materials, which made them suitable for a wide range of engineering applications. A comprehensive performance evaluation model of ternary solid waste cementitious materials was developed via the principal component regression (PCR) method. Ettringite and CaSO₄·2H₂O generated after adding desulfurization gypsum can significantly improve the specimens’ early strength, with the desulfurization gypsum content being the key influencing factor. The dry shrinkage of this ternary solid waste cementitious material was affected by various factors and showed no significant correlation with the mass loss rate. Full article

As coal mining depths continue to rise, consideration of WFFZ elevations is becoming increasingly important to mine safety. The goal was to accurately predict the height of the WFFZ to effectively prevent and manage possible roof water catastrophes and ensure the ongoing safety of the mine. To achieve this goal, we combined the particle swarm optimisation (PSO) algorithm with a backpropagation neural network (BPNN) in order to enhance the accuracy of the forecast. The present study draws upon the capacity of the PSO algorithm to conduct global searches and the nonlinear mapping capability of the BPNN. Through grey relational analysis (GRA), the order of the correlation degree was as follows: mining thickness > mining depth > overburden structure > mining width > mining dip. GRA has identified the degree of correlation between five influencing factors and the height of the WFFZ, among these, mining thickness, mining depth, overburden structure and mining width all show strong correlations, and the mining dip of the coal seam shows a good correlation. The weight ranking obtained by the PSO-BPNN method was the same as that obtained by the GRA method. Based on two actual cases, the relative errors of the obtained prediction results after PSO implementation were 2.97% and 3.47%, while the relative errors of the BPNN before optimisation were 18.46% and 4.34%, respectively, indicating that the PSO-BPNN method provides satisfactory prediction results and demonstrating that the PSO-optimised BPNN is easy to use and yields reliable results. In this paper, the height of the WFFZ model under the influence of five factors is only established for the Northwest Mining Area. With the continuous progress of technology and research, the neural network can consider more factors affecting the height of hydraulic fracturing development zones in the future to improve the comprehensiveness and accuracy of prediction. Full article

To achieve a hierarchical and quantitative evaluation of grid-wide power quality in the distribution network, reflecting the overall power quality level of the distribution network, a comprehensive evaluation method for power quality in a grid-wide system based on complex network theory is proposed. Firstly, based on the propagation characteristics of power quality disturbances, a power quality evaluation index system is constructed. Secondly, to reflect the constraint effect of the local power quality level of nodes on the overall power quality level of the distribution system, corresponding indices such as improved node degree, improved node electrical betweenness, and node self-healing capability are proposed based on complex network theory, and the power quality influence degree of nodes is calculated. Then, the GRA-ANP (Grey Relational Analysis–Analytic Network Process) subjective weight calculation method is improved by introducing grey relational analysis to address the impact of differences in different decision-making results. Based on power quality monitoring data, the entropy weight method is used for objective weighting. To avoid the partiality of a single weight evaluation result, the game equilibrium algorithm is employed to calculate the comprehensive weight of each power quality index. Subsequently, considering the correlation and dependency among indices, the VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) method is used to obtain the power quality grade of each node. Combining this with the calculation of the power quality influence degree of nodes, the overall power quality grade of the distribution network is determined, achieving a hierarchical and quantitative evaluation of power quality in the entire distribution system. Finally, through a case study analysis of an improved 13-node distribution network, it is verified that the proposed method can fully extract data information and produce comprehensive and accurate power quality assessment results by comparing it with other methods. This provides strong support for the safe and stable operation of the distribution system and the subsequent optimization and management of power quality. Full article

A concrete production system (CPS) fault in dam engineering is one of the important factors influencing dam construction quality, which may directly affect the concrete-pouring construction progress and construction efficiency of the dam, and can even cause construction quality defects in the dam body. Reasonable classification and identification are of great significance to ensure the construction progress and quality of concrete dams. In this study, based on the concrete production logs of multiple concrete dams and literature reviews, a fault classification system for a CPS is proposed by comprehensively considering its mechanical structure characteristics and operating characteristics. The faults of the CPS are divided into 4 large categories and 22 subcategories. Additionally, the causes of CPS faults are summarized as human factors, environmental factors, mechanical component service life factors, and other factors. Based on the grey correlation analysis (GCA) method, a fault identification model of the CPS is established. With the actual production system fault statistical data of Shatuo hydropower station, the correlation coefficients for the four types of faults and the four influencing factors are calculated to determine the key faults of the CPS. The research results of the case study show that the service life factors of mechanical components have the greatest impact on batching metering system faults and mixer faults, with high grey correlation degrees of 84.66% and 76.85%, respectively. Environmental factors have the greatest impact on material delivery system faults and pneumatic system faults, with high grey correlation degrees of 90.81% and 94.9%, respectively. This paper provides theoretical support for the realization of fault pattern recognition of CPSs and provides a guiding reference for targeted fault handling. Full article

There is a mutual causal relationship between population shrinkage and the level of regional social–economic–ecological development and their coordinated development. It is of great significance to reveal the correlation effect between population shrinkage and regional development for the adjustment and optimization of the relationship between regional population and social, economic and ecological development. Taking 142 counties in the three provinces of Northeast China as samples, the population contraction was identified and classified in different segments, and a comprehensive evaluation index system was constructed. The entropy method, coupled coordination model, grey correlation degree model, bivariate spatial autocorrelation model and other analysis methods were used. This paper measures the level of social, economic, ecological and synthetical development and the coordination degree among the three in different periods, and it analyzes the spatio-temporal correlation with population shrinkage. The obstacle degree model is used to analyze the main factors affecting the coordinated development under different population shrinkage levels. The results show that: (1) The number of counties with a shrinking population accounted for 57.04% from 2000 to 2010, showing a “Nearly half of the increase and half of the decrease” situation; from 2010 to 2020, the number of counties with population contraction type accounted for 99.3%, and the region entered a state of comprehensive contraction, and the contraction amplitude increased significantly. (2) From 2000 to 2010, the degree of population shrinkage was negatively correlated with the level of social, economic, synthetical and coordinated development but positively correlated with the level of ecological development. From 2010 to 2020, the degree of population shrinkage was still negatively correlated with the level of social, economic, synthetical and coordinated development, but it is not significantly correlated with the level of ecological development. During the study period, the correlation between population shrinkage and social development level was strong, while that between population shrinkage and ecological development level was weak. (3) During the study period, the social and economic system factors were the main obstacles in the process of coordinated development. From 2000 to 2010, the common important obstacle factors of the three types of population shrinkage level counties were the number of industrial enterprises above designated size, average night light index and gross regional product, and the common main obstacle factor was population density. From 2010 to 2020, the common important obstacle factors of the three types of population shrinking counties were the number of industrial enterprises above designated size and the per capita balance of loans from financial institutions at the end of the year, and the obstacle levels of indicators in different types of population shrinking counties are significantly different. Full article

The Shouyang Block was selected as the research subject. Comprehensive analysis was conducted using coalbed methane (CBM) well production data, geochemical test data on water produced from the coalbed methane well, and fundamental geological information. The findings reveal the water dynamics in the Shouyang Block are characterized by weak groundwater runoff or retention in most areas. The groundwater head height exhibits a gradual decrease from the north to south, which is closely associated with the monoclinic structure of the Shouyang Block. Overall, water production is relatively high. As the average water production increases, the average gas production gradually decreases. A concentration of high water production wells is observed in the northern part of the Shouyang Block, which gradually increases towards the southeast direction. A comprehensive analysis was conducted on the factors influencing water production, including total water content of coal seams, coal seam porosity, groundwater stability index, groundwater sealing coefficient, D value of the fracture fractal dimension, fault fractal dimension, and sand–mud ratio. The correlation degree was calculated and ranked in order of magnitude through grey correlation analysis. The order of factors that influence water production, from strongest to weakest, is as follows: sand–mud ratio > porosity > fractal dimension of fault > fracture fractal dimension D value > groundwater sealing coefficient > groundwater stability index > total water content of coal seams. The dissolution amounts of carbonate and sulfate are both small, and the water source may mainly come from the sandstone aquifer. Attention should be paid to the distribution and lithological combination of sandstone aquifers in coal-bearing strata in the future exploration and development process of the Shouyang Block. This will help to avoid the potential influence of fault structures and enable the identification of favorable areas for low water and high gas production. Full article

Dongting Lake wetland is a typical lake wetland in the Middle and Lower Yangtze River Plain in China. Due to the influence of natural and human activities, the landscape pattern has changed significantly. This study used 12 Landsat images from 1991 to 2022 and applied three common classification methods (support vector machine, maximum likelihood, and CART decision tree) to extract and classify the landscape information, with the latter having a superior annual accuracy of over 90%. Based on the CART decision tree classification results, the dynamic characteristics of wetland spatial patterns were analyzed through the landscape pattern index, dynamic degree model, and transition matrix model. Redundancy and grey correlation analysis were employed to investigate the driving factors. The results showed increased landscape fragmentation, reduced heterogeneity, and increased complexity from 1991 to 2022. The water and mudflat areas exhibited three distinct stages: gradual decline until 2001 (−3.06 km²/a); sharp decrease until 2014 (−19.44 km²/a); and steady increase (22.93 km²/a). Vegetation conversion, particularly between sedge and reed, dominated the change in landscape pattern. Reed area initially increased (18.88 km²/a), then decreased (−35.89 km²/a), while sedge showed the opposite trend. Woodland area fluctuated, peaking in 2016 and declined by 2022. The construction of the Three Gorges Dam significantly altered landscape dynamics through water level changes, reflected by a 4.03% comprehensive dynamic degree during 2001–2004. Potential evaporation also emerged as a significant natural factor, exhibiting a negative correlation with the landscape index. During 1991–2001 and 2004–2022, the comprehensive explanatory rates of temperature, precipitation, potential evaporation, and water level on landscape pattern dynamics were 88.56% and 52.44%, respectively. Other factors like policies and socio-economic factors played a crucial role in wetland change. These findings offer valuable insights into the dynamic evolution and driving mechanisms of Dongting Lake wetland. Full article

Searching for an urbanization development model that is suitable for the eco-environment can provide important references for regional sustainable development. By comprehensively using models such as system dynamics (SD), distance coordination coupling degree, symbiosis degree, and grey correlation degree, the interaction between urbanization and eco-environment in Hebei Province from 2020 to 2035 was dynamically simulated based on the historical data from 2000 to 2019. In addition, the key bidirectional influence factors of urbanization and eco-environment were identified. The entire process analysis from model construction, scenario simulation, and preferred scenarios to factor identification was achieved. The results showed the following. (1) The constructed SD model was reliable and effective, and could be used to simulate future strategies. (2) Three evaluation methods could effectively reveal the advantages and disadvantages of the phased scenario schemes during the simulation period, and the obtained results had strong consistency. The urbanization priority development scenario was more suitable for short-term and medium-term planning, while the friendly development scenario was more suitable for the entire simulation period. (3) Five indicators of urbanization and seven indicators of the eco-environment were highly relevant to the evaluation levels of the eco-environment and urbanization, respectively. The study extended the application of the symbiosis theory and the evaluation methods of scenario simulation schemes for urbanization and eco-environment systems. Full article

The parallel reservoirs in the upper reach of the Hanjiang River are key projects for watershed management, development, and protection. The optimal operation of parallel reservoirs is a multiple-stage, multiple-objective, and multiple-decision attributes complex decision problem. Taking Jiaoyan–Shimen parallel reservoirs as an example, a method of multi-objective optimal operation decision of parallel reservoirs (MOODPR) was proposed. The multi-objective optimal operation model (MOOM) was constructed. The new algorithm coupling NSGA-II, TOPSIS, and GCA was used to solve the MOODPR problem. The method of MOODPR was formed by coupling problem identification, model construction, an optimization solution, and scheme evaluation. The results show that (1) combining the Euclidean distance with the grey correlation degree to construct a new hybrid closeness degree makes the multi-attribute decision making method more scientific and feasible. (2) The NSGA-II-TOPSIS-GCA algorithm is applied to obtain decision schemes, which provide decision support for management. (3) It can be seen from the Pareto chart that for the Jiaoyan–Shimen parallel reservoirs, the comprehensive water supply was negatively related to ecology. (4) The comprehensive water supply and ecological AAPFD value in the extraordinarily dry year was 4.212 × 10⁸ m³ and 4.953. The number of maximum continuous water shortage periods was 4 and 6. The maximum ten-day water shortage was 4.46 × 10⁷ m³ and 2.3 × 10⁶ m³. The research results provide technical support and reference value to multi-objective optimal operation decisions for parallel reservoirs in the upper reach of the Hanjiang River. Full article

In response to the limited available information during the initial stages of coal spontaneous combustion and the influence of decision makers’ risk preferences on decision-making, this paper proposes an emergency decision-making method for coal spontaneous combustion that integrates grey correlation degree and TOPSIS with an enhanced prospect theory. Firstly, a normalized weighted evaluation matrix is established for the emergency response plan of coal spontaneous combustion, and the entropy method is utilized to determine the weights of various indexes. Then, considering the imperfect rationality of decision makers and their diverse individual risk preferences, they are categorized into three types: risk-seeking type, risk-neutral type, and risk-averse type. The corresponding risk coefficients are determined based on these different types. Positive and negative ideal solutions are taken as reference points, and matrices representing gains and losses are constructed. The grey correlation degree is introduced to calculate both positive and negative prospect values based on these matrices. Moreover, the prospect value for each emergency response plan is calculated, respectively, based on different types of decision makers, and the entropy method is used to assign weights to decision makers according to their respective risk preferences. Consequently, based on these prospect values and the weights, comprehensive prospect values for each emergency response plan are obtained and ranked to identify the optimal one. Finally, in order to validate the effectiveness of our proposed approach, a case study is conducted, and the results obtained from this case study are discussed and compared with those from other methods. Full article

The physical properties and nutritional quality of H. marmoreus by-products (HMB) dried by different methods were comprehensively evaluated by a rigorous statistical method of grey correlation analysis. The results indicated that different drying methods had significant impacts on the characteristics of HMB. Heat pump drying (HPD) was conducive to the preservation of protein and reducing sugar, and hot air drying (HAD) maintained a high content of total flavonoids. The highest fat, polysaccharide, and total phenolic contents were obtained by heated vacuum freeze-drying (H-VFD) treatment. The unheated vacuum freeze-drying (UH-VFD) treatment achieved bright colour, lacunose texture profile, and looser organization structure. The grey correlation analysis showed that UH-VFD and H-VFD had higher-weighted correlation degrees than HPD and HAD. HMB had many higher nutritional components than commodity specifications, especially protein, fat, polyphenols, and amino acids, and had potential applications in the food industry as functional foods and nutraceutical agents. Full article

The change grassland plant communities demonstrate with elevation has been one of the hot issues in ecological research, and there remain many unsolved problems. In order to further elucidate the rules of grassland plant community change with elevation, this study took the Burqin forest area as a research object, using field survey, redundancy analysis and grey correlation analysis to comprehensively assess the characteristics of change in grassland plant communities with elevation and the relationship of this evolution with environmental factors. The results showed that (1) the numbers of species, community biomass, community cover and community densities of grassland plant communities showed an “M” pattern with the increase in elevation. There were significant changes in the importance values and dominance of plants at different elevations; with increasing elevation, grassland plants became primarily dominated by cold-tolerant and well-adapted perennials. (2) The similarity coefficients of grassland plant communities at different elevations ranged from 0.06 to 0.62, i.e., from very dissimilar to moderately similar. (3) As the elevation increased, the Margalef species richness index, Shannon–Wiener diversity index, Simpson dominance index and Alatalo evenness index all showed an “M” pattern trend. (4) The degrees of correlation between temperature and precipitation and community biomass and species diversity were at a high level, and these were the most important environmental factors affecting the biomass and species diversity of grassland plant communities in the Burqin forest area. The results of this study can provide a theoretical basis for the rational utilization of grassland resources and for the sustainable development of grassland ecosystems in the Burqin forest area. Full article