CSA/GFRC is an advanced composite material possessed with great ductility and durability. However, its bending performance and fibre condition, as well as intrinsic microstructural changes, under elevated temperature have not been understood so far. XRD was applied in this study to investigate the hydration mechanism of CSA cement under 50°C, 70°C, and 80°C. Bending performance was carried out to test the toughness of CSA/GFRC. SEM was applied to observe the underlying microstructural changes of CSA/GFRC under different curing regimes. It was found out that there was a gradual degradation of both ultimate tensile strength and ultimate strain of CSA/GFRC with elevated curing temperature and curing age, but glass fibre still shows considerable ability to carry stress alone by bridging cracks. Microstructural studies showed that, at accelerated temperatures of 50°C and 70°C, the space between fibres remained empty in general only with some hydration products adhering to the fibre surface occasionally. At a higher accelerated curing temperature of 80°C, densification of the interfilamentary spaces by larger and clustered hydration products can be observed at longer curing ages, causing the fibres to lose parts of the flexibility. Therefore, it can be concluded that densification of interfilamentary spaces may have a greater role to play in the strength degradation of CSA/GFRC than mechanisms associated with fibre weakening caused by chemical corrosion.

Calcium sulfoaluminate cement (CSA) is a low‐carbon cementitious material that significantly reduces alkalinity and produces calcium hydroxide‐free (CH‐free) matrix environment in comparison to ordinary Portland cement (OPC). It might be, however, less efficient towards the passivation of steel in concrete and further investigation before widespread adoption is required. In this project, scanning electron microscopy/energy dispersive X‐ray spectroscopy (SEM/EDX) on polished samples was employed to provide the interfacial characterization of steel reinforced CSA concrete and study the relationship of interfacial quality and corrosion resistance of the embedded steel. The galvanostatic polarization behavior indicates that the steel embedded in CSA concrete remains passive for 28 days in absence of Cl⁻ ions and carbonation. Microstructure analysis has shown that there is an Al‐enriched layer at interfacial zone in CSA concrete with the main hydration product of AH₃, which is also alkaline and is expected to improve the steel passivity. Furthermore, the interfacial zone has markedly reduced porosity compared to the bulk matrix, which leads to reduced possibility of current flow between anode and cathode and therefore improves the corrosion resistance of the embedded reinforcement.

Unmanned aerial vehicles (UAVs) are increasingly deployed to enhance the operational efficiency of city services. However, finding optimal solutions for the gather–return task pattern under dynamic environments and the energy constraints of UAVs remains a challenge, particularly in dense high-rise building areas. This paper investigates the multi-UAV path planning problem, aiming to optimize solutions and enhance data gathering rates by refining exploration strategies. Initially, for the path planning problem, a reinforcement learning (RL) technique equipped with an environment reset strategy is adopted, and the data gathering problem is modeled as a maximization problem. Subsequently, to address the limitations of stationary distribution in indicating the short-term behavioral patterns of agents, a Time-Adaptive Distribution is proposed, which evaluates and optimizes the policy by combining the behavioral characteristics of agents across different time scales. This approach is particularly suitable for the early stages of learning. Furthermore, the paper describes and defines the “Narrow-Elongated Path” Problem (NEP-Problem), a special spatial configuration in RL environments that hinders agents from finding optimal solutions through random exploration. To address this, a Robust-Optimization Exploration Strategy is introduced, leveraging expert knowledge and robust optimization to ensure UAVs can deterministically reach and thoroughly explore any target areas. Finally, extensive simulation experiments validate the effectiveness of the proposed path planning algorithms and comprehensively analyze the impact of different exploration strategies on data gathering efficiency.