Search Results (189)

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

Changes in the environment and in land use interconnect and interact. To ascertain the impact of meteorological factors, namely temperature, precipitation, and sunshine, on land use changes, an analysis was conducted using meteorological data and the China land use dataset spanning from 1990 to 2020. Pearson correlation analysis, grey correlation degree, and vector regression model were employed to assess the influence of meteorological factors on land use alterations and to pinpoint the primary driving forces. The findings reveal the following: (1) The spatial distribution of isohyets and isotherms is shifting towards the north, with the most significant northward movement observed in the 1600 mm isohyets and 15 °C isotherm contours. (2) Overall, the areas of croplands, shrubs, grasslands, and wetlands are decreasing, notably, with a reduction of approximately 100,000 km² in cropland, while forests, water, and impervious surfaces are expanding annually. (3) Temperature and precipitation exhibit notable impacts on various land use types, with temperature exerting the most substantial influence on changes in cropland area, contributing to 8% of the observed variations. This study can provide a scientific basis for the rational optimization and allocation of land resources under changing environmental conditions. Full article

With the acceleration of the oilfield development process during the high water content period, the contradiction between the increase in energy consumption and the decrease in the energy efficiency of the gathering and transportation system has become increasingly obvious. This paper develops a grey relational analysis model using a combination of AHP and EWM. Based on the characteristics of light oil production, a four-level evaluation indicator system is developed. Based on game theory, AHP can provide subjective weights, the EWM can provide objective weights, and subjective and objective combinations are used for a more reasonable assignment. Concurrently, the 0.05 distinguishing coefficient and the ideal reference values are selected as the GRA reference sequence to evaluate the energy consumption of the gathering and transportation system as a whole and each subsystem. The analysis of a light oil block indicates significant room for improvement in the energy efficiency correlation across the system. Taking the central processing station as an example, the grey relational degree of electricity consumption per unit of injected water is measured at 0.12, marking it as the weakest link in the system. This study supports efficiency enhancement by identifying energy consumption bottlenecks within the system. Full article

In the context of global energy scarcity, thermal insulation castables have garnered significant attention from the steel industry to reduce energy consumption. To optimize the performance of calcium hexaaluminate (CA₆)-based insulating castables, a systematic comparative study was conducted on the influence of varying amounts of calcium aluminate cement (CAC) incorporated into the castables. The results indicated that the addition of more CAC could increase the initial flowability of the castables with an air-entraining agent (AEA). Conversely, the flowability of the castables containing alumina bubbles continuously decreased after 30 min and 60 min. The apparent porosity of castables with only added AEA and alumina bubbles after being dried at 110 °C and treated at 1300 °C presented a decreasing trend as CAC content increased. Under the joint action of AEA and alumina bubbles, the amplification in porosity of castables treated at 1300 °C was positively correlated with the amount of CAC. The increase in CAC content could enhance the strength of samples, with a particularly notable improvement observed in castables prepared with the addition of AEA. For castables prepared with AEA and CAC contents of 9 wt.%, the cold modulus of rupture and cold crushing strength after heat treatment at 1300 °C were 17.5 MPa and 80.5 MPa, respectively. The thermal conductivity of castables presented non-monotonic change with the increase in CAC content. The effect of elevated CAC content on the pore fractal dimension of castables depended on the pore-forming methods. Grey correlation analysis (GCA) demonstrated that pore sizes in the range of 500–1000 nm, pore fractal dimensions, and pore sizes less than 500 nm had the highest degrees of correlation with CMOR, CCS, and thermal conductivity, respectively. Full article

This paper focuses on the stability issues of geological and engineering structures and conducts research from two perspectives: the mechanism of slope landslides under micro-seismic action and the cyclic failure behavior of concrete materials. In terms of slope stability, through the combination of model tests and theories, the cumulative effect of circulating micro-seismic waves on the internal damage of slopes was revealed. This research finds that the coupling of micro-vibration stress and static stress significantly intensifies the stress concentration on the slope, promotes the development of potential sliding surfaces and the extension of joints, and provides a scientific basis for the prediction of landslide disasters. This helps protect mountain ecosystems and reduce soil erosion and vegetation destruction. The number of cyclic loads has a power function attenuation relationship with the compressive strength of concrete. After 1200 cycles, the strength drops to 20.5 MPa (loss rate 48.8%), and the number of cracks increases from 2.7 per mm³ to 34.7 per mm³ (an increase of 11.8 times). Damage evolution is divided into three stages: linear growth, accelerated expansion, and critical failure. The influence of load amplitude on the number of cracks shows a threshold effect. A high amplitude (>0.5 g) significantly stimulates the propagation of intergranular cracks in the mortar matrix, and the proportion of intergranular cracks increases from 12% to 65%. Grey correlation analysis shows that the number of cycles dominates the strength attenuation (correlation degree 0.87), and the load amplitude regulates the crack initiation efficiency more significantly (correlation degree 0.91). These research results can optimize the design of concrete structures, enhance the durability of the project, and indirectly reduce the resource consumption and environmental burden caused by structural damage. Both studies are supported by numerical simulation and experimental verification, providing theoretical support for disaster prevention and control and sustainable engineering practices and contributing to ecological environment risk management and the development of green building materials. Full article

The water-conducting fracture zone (WCFZ) is a critical geological structure formed by the destruction of overburden during coal mining operations. Accurately predicting the height of the water-conducting fractured zone (HWCFZ) is essential for ensuring safe coal production. Based on more than 150 measured heights of fractured water-conducting zone samples from various mining areas in China, this study investigates the influence of five primary factors on the height: mining thickness, mining depth, length of the panel, coal seam dip, and the proportion coefficient of hard rock. The correlation degrees and relative weights of each factor are determined through grey relational analysis and principal component analysis. All five factors exhibit strong correlations with the height of the fractured water-conducting zone, with correlation degrees exceeding 0.79. Mining thickness is found to have the highest weight (0.256). A multiple nonlinear coordinated regression equation was constructed through regression analysis of the influencing factors. The prediction accuracy was compared with three other predictive models: the multiple nonlinear additive regression model, the BP neural network model, and the GA-BP neural network model. Among these models, the multiple nonlinear coordinated regression model was found to achieve the lowest error rate (7.23%) and the highest coefficient of determination (R² = 87.42%), indicating superior accuracy and reliability. The model’s performance is further validated using drill hole data and numerical simulations at the B-1 drill hole in the Fuda Coal Mine. Predictive results for the entire Fuda Coal Mine area indicate that as the No. 15 coal seam extends northwestward, the height of the fractured water-conducting zone increases from 52.1 m to 73.9 m. These findings have significant implications for improving mine safety and preventing geological hazards in coal mining operations. 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

As a key region in China’s “dual carbon” strategy, the Yangtze River Delta region faces the dual challenge of sustaining tourism-driven economic growth and achieving significant emission reductions. Based on panel data of the Yangtze River Delta region from 2000 to 2022, this paper adopts the “bottom-up” method to measure the carbon emissions of tourism transportation. It systematically analyzes its spatiotemporal evolution, decoupling effect, and driving mechanism. The results showed that (1) regional carbon emissions showed a trend of “first rising and then decreasing”. The spatial distribution changed from “high in the east and low in the west” to central agglomeration, and the hot spots of high emissions continued to concentrate in Shanghai and its surrounding cities, reaching a peak in 2019. (2) The decoupling state is mainly weak decoupling. The environmental Kuznets curve verified that carbon emissions and the tourism economy showed an inverted U-shaped relationship, and the decoupling levels of cities were significantly different. (3) Gross Domestic Product and the scale of tourist flow of cultural facilities (grey correlation degree 0.925) are the core positive drivers. In contrast, the travel ratio (contribution value −215.9) and the scale of passenger flow in A-class scenic spots (correlation degree 0.876) are the key inhibiting factors. This paper proposes a three-pronged policy framework of “energy structure optimization—cross-city carbon compensation—cultural and tourism integration” to provide theoretical and empirical support for the low-carbon transformation of urban agglomerations. 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

In order to solve problems of poor stability and large trajectory-tracking errors when intelligent vehicles are travelling at different speeds, when working conditions that require obstacle avoidance are not taken into account in the trajectory-tracking process, an obstacle avoidance re-planner adaptive trajectory-tracking controller is proposed. For this obstacle avoidance trajectory re-planner, the prediction model is calculated based on the vehicle point mass model, an objective function utilizing the obstacle avoidance function is designed, and finally the obstacle avoidance trajectory is output using a fifth-degree polynomial fitting. For the trajectory-tracking controller, 138 sets of valid data are screened from 300 sets of offline simulation experiments, and the optimal combinations of different vehicle travel speeds and predicted time domains are obtained using grey correlation analysis, and each set of speeds and predicted time domains are fitted using Fourier approximation to design the adaptive parameter model. Using CarSim/Simulink co-simulation, simulation results comparing obstacle avoidance performance and trajectory-tracking performance between the fixed time-domain controller and the controller designed in this paper show that the control accuracy of the controller designed in this paper is improved by 19.9% and the solution speed is increased by 15% at 50 km/h speed; at 100 km/h speed, the maximum traverse angle deviation and maximum lateral deviation are reduced by 0.5% and 26.9%, respectively. In the multi-obstacle environment, the controller is able to achieve obstacle avoidance, and the lateral deviation, traverse angular velocity, and centre-of-mass lateral deviation are all better than those of the fixed time-domain controller. It can be seen that the controller designed in this paper is more stable and has better tracking performance when considering obstacle avoidance. 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

The integration of scientific and technological innovation with industrial innovation has emerged as a pivotal driver of sustainable economic growth. Utilizing the theory of system coupling, this study conceptualizes the integration process as complex and dynamic. It explores the interaction mechanisms between these innovations and establishes a systematic coupling evaluation index system. Additionally, methods such as the coupling coordination model and grey relational analysis are employed to quantitatively assess the integration levels across China and its 30 provincial regions from 2012 to 2022 in this study. Key factors influencing the enhancement of this integration are also identified. The findings indicate that, from 2012 to 2022, the coupling relationship between these two innovation systems has attained an advanced coupling state; however, the degree of coupling coordination remains at a primary level. Regionally, the coupling coordination degree exhibits features of unbalanced development: the eastern region reaches the highest level, albeit still at a barely coordinated stage, followed by the central region, which surpasses the northeastern region, while the western region exhibits the lowest degree. At the provincial level, notable discrepancies exist in the coupling coordination between these two innovation systems. Grey relational analysis reveals that scientific and technological input, along with industry–university–research collaboration, play particularly critical roles in enhancing the degree of integration. Full article

The development of park cities is an important exploration for better satisfying people’s aspirations for a better life, promoting sustainable social development, and advancing the transformation of green ecological values. As a basic industry for sustainable development, the combination of agriculture and urban development is an important way to build an ecological civilization. Clarifying the relationship between a park city and green development of agriculture is of great significance to the construction of a green base and ecological system of the city, sustainable development of agriculture, and integrated development of urban and rural areas. Chengdu is a mega-city in western China, and the Chengdu-led park city development program is unique in Chinese urban development. Chengdu’s park city development is a pioneering example of urban ecological civilization construction. Taking Chengdu as an example and combining the data of other prefecture-level cities in Sichuan, this study explored the correlation and interaction between agricultural green development (AGD) and park city development (PCD) in Chengdu and other prefecture-level cities in Sichuan from 2011 to 2022 based on the coupling coordination degree, gray correlation degree, and spatial autocorrelation analysis. The results showed the following: (1) Based on the entropy method, the level of AGD in Chengdu rises from 0.353 in 2011 to 0.537 in 2022, and the level of PCD rises from 0.368 to 0.826. The level of AGD and the level of PCD as a whole show an upward trend. (2) The degree of coupling and coordination between the PCD and AGD levels rises from 0.600 to 0.816, realizing the leap from coordination to good coordination, and the degree of coupling has been at a high level. (3) Based on the grey correlation degree, in the process of the influence of AGD on the PCD, the correlation degree of the influencing factors of each indicator is basically above 0.5, and each influencing factor has a strong contribution to the level of the PCD. (4) Spatial self-analysis shows that the coupling coordination degree of AGD and PCD in a region is affected by the neighboring region. Therefore, we believe that AGD plays a more obvious role in driving and radiating PCD and that it can effectively promote the economic, social, and ecological upgrading in the process of PCD. Full article

Moisture is a critical factor leading to the deterioration of concrete structures. Hydrophobic cement-based materials, with their excellent waterproof performance, hold significant application value in humid, coastal, and cold environments. This study employed stearic acid (STA, CH₃(CH₂)₁₆COOH) as a hydrophobic agent dissolved in anhydrous ethanol using ultrasonication to create an STA–ethanol solution. In addition, the ball-milling method was used to mix STA with tuff powder (TP) to prepare hydrophobic modified tuff powder (MTP). This study investigated the effects of the STA content, water–cement (w/c) ratio, cement–sand (c/s) ratio, the replacement rate, and addition method of TP and MTP on the wettability (contact angle and sorptivity) and compressive strength of the mortar. The effects of the STA on the cement hydration were explored by microanalysis techniques, such as SEM, XRD, and FTIR, and the modification method with the best effect was recommended based on a gray correlation degree analysis. The results indicate that the STA could be successfully grafted into the mortar without affecting the types of cement hydration products. When using the STA–ethanol solution for hydrophobic modification, adding 0.9% STA by weight increased the mortar contact angle to 69.5° and reduced the sorptivity by 22%, while the 28-day compressive strength was decreased. When the w/c ratio was 0.5, the contact angle rose with the increase in the replacement rate of MTP, while the sorptivity and compressive strength decreased. The grey relational analysis showed that at a w/c ratio of 0.4, the STA–ethanol solution was a more effective modification method in terms of reducing the mortar sorptivity. Full article

The development of a city needs the accumulation of culture, and historical buildings are the most direct witness of the rise and fall of a city. Like the human body, historical buildings have a certain life cycle, but the acceleration of urbanization and unreasonable use cause an irreversible reduction in the remaining life of historical buildings. There is a notable lack of quantitative analysis regarding the residual life of historical buildings. Therefore, identifying the factors that influence their residual life is crucial for both preserving these buildings and sustaining urban culture. In order to obtain a more accurate correlation degree of influencing factors, a systematic-analysis model of influencing factors on the residual life of historical buildings based on the entropy weight method (EWM) and the grey relation analysis method (GRA) was established, so as to excavate the mechanism of the influencing factors on the residual life of historical buildings, accurately identify the main influencing factors on the residual life of historical buildings, and propose preventive measures. The results show that the structural system has the greatest influence on the residual life of historical buildings, followed by the enclosure system, and the equipment system. The research findings offer valuable insights for extending the residual life of historical buildings in the future. Full article