Tiantian Jiang

This study aimed to enhance the slurry performance and durability of coal gangue-based geopolymers (CGGP) by incorporating four types of nanomaterials: nano-SiO₂ (NS), graphene oxide (GO) nanosheets, nano-CaCo₃ (NC), and nano-Al₂O₃ (NA). The microstructure and underlying mechanisms were thoroughly investigated using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS). The results indicate that the type and dosage of nanomaterials significantly influence the rheological properties, strength development, setting time, porosity, and water absorption of CGGP. Specifically, the addition of GO nanosheets drastically reduced fluidity, with a 73.33% decrease in flowability compared to the control group at a 2.0 wt.% dosage. Nano-SiO₂ exhibited the most pronounced effect in improving compressive strength and shortening the setting time, with the optimal accelerating effect observed at a 1.5 wt.% dosage. Nano-CaCO₃ primarily acts as a filler. Though its reactivity is relatively low, at an appropriate dosage (1.5 wt.%), it can effectively reduce porosity and water absorption. Moreover, at a dosage of 1 wt.%, it exhibits the optimal 28-day compressive strength, which is 54.18% higher than that of the blank group. Nano-Al₂O₃ demonstrated a relatively stable accelerating effect on setting and yielded the best pore structure and strength at a 1.5 wt.% dosage. SEM analysis revealed that the incorporation of an appropriate amount of NC particles significantly improved the microstructural densification of the polymer. Concurrently, EDS results confirmed the positive influence of the nano-Al₂O₃ material on the distribution of hydration products and the interfacial structure. This research provides an important theoretical basis and technical support for the high-performance design and widespread engineering application of coal gangue-based geopolymers.

This study aims to refine the ratio of alkali-activated steel slag (SS) to granulated blast furnace slag (GBFS)–cement-based grouting materials, with the dual objectives of cost reduction and performance enhancement. By employing single-factor experiments and response surface methodology (RSM), we have pinpointed the critical factors that influence the slurry’s performance and developed a regression model to assess the impact of these factors and their interplay. Our findings indicate that the compressive strength initially increases with higher SS content but subsequently declines. Additionally, an increase in alkali content and activator modulus is beneficial for strength improvement. However, beyond an alkali content of 8%, the 28-day strength is observed to decrease. Through meticulous model analysis, we have determined the optimal ratio to be 7.07% SS content, 7.82% alkali content, and an activator modulus of 1.8. The material’s performance at this ratio satisfies construction specifications. This research not only offers a cost-effective and high-performance grouting solution for geotechnical applications but also pioneers a novel approach to the resourceful utilization of solid waste materials, such as SS.

With the acceleration of urbanization, the importance of risk management in underground construction projects has become increasingly prominent. In the process of risk assessment for underground construction projects, the uncertainty of subjective factors from experts poses a significant challenge to the accuracy of assessment outcomes. This paper takes a section of the Nanchang Metro Line 2 as the research object, aiming to address the subjectivity issues in the risk assessment of underground construction projects and to enhance the scientific rigor and accuracy of the assessment. The study initially conducts a comprehensive identification and analysis of risk factors in underground engineering through a literature review and expert consultation method. Based on this, this paper introduces the theory of Pythagorean fuzzy sets to improve the Delphi method in order to reduce the impact of subjectivity in expert assessments. Furthermore, this paper constructs a Bayesian network model, incorporating risk factors into the network, and quantifies the construction risks through a probabilistic inference mechanism. The research findings indicate a total of 12 key risk factors that have been identified across four dimensions: geological and groundwater conditions, tunnel construction technical risks, construction management measures, and the surrounding environment. The Bayesian network assessment results indicate that the effectiveness of engineering quality management and the state of safety management at the construction site are the two most influential factors. Based on the assessment results, this paper further conducts a risk control analysis and proposes targeted risk management measures.