Pipelines play a critical role in transporting water, oil, and gas and are indispensable for urban development. However, monitoring underground pipelines is challenging due to the complex environment in which they are situated. Traditional sensors have limitations in all-round and real-time monitoring, while fiber optic sensors offer several advantages, including large coverage, high sensitivity, long sensing distance, and corrosion resistance. As such, fiber optic sensing technology (FOST) has emerged as a promising tool for underground pipeline monitoring. This review article provides a comprehensive overview of FOST, including its basic principles, classification, and research progress in underground pipeline monitoring. Specifically, this article focuses on the technology’s application in monitoring pipeline leakage, deformation, corrosion, and geological natural disasters. In addition, the article highlights the need for future research and development in FOST. For example, combining FOST with artificial intelligence can enhance the accuracy and efficiency of pipeline monitoring. Overall, this review article offers valuable insights into the potential of FOST for underground pipeline monitoring and provides essential guidance for future research in this field.

The rheological properties of mortar are of vital importance to ensure the quality and durability of engineering structures, improving construction efficiency and adapting to different construction environments. This research focused on examining the rheological properties of geopolymer mortar (GM) with the incorporation of metakaolin (MK), nano-SiO₂ (NS) and polyvinyl alcohol (PVA) fibers. The research focused on varying concentrations of PVA fiber ranging from 0 to 1.2% (interval of 0.2%) and NS ranging from 0 to 2.5% (interval of 0.5%). As the mix proportion optimization of GM is normally carried out experimentally, a significant amount of labor and material resources was consumed. Based on large amounts of authentic operation data, a prediction model of rheological properties for NS- and PVA-fiber-reinforced GM was developed using a back propagation (BP) neural network. Subsequently, the parameters were refined using a genetic algorithm (GA) to predict the rheological properties of GM reinforced with different dosages of NS and PVA fiber. Three rheological parameters, including static yield stress, plastic viscosity and dynamic yield stress, were used to evaluate the rheological properties of GM. Moreover, parameters of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE) were applied to assess the capability of the algorithms. When the GA–BP neural network was used, compared with the BP neural network, the coefficient of determination (R²) of the static yield stress, plastic viscosity, and dynamic yield stress increased by 4.40%, 2.11% and 15.28%, respectively, and the GA–BP neural network provided a superior fitting effect, higher prediction accuracy and faster convergence. Based on the outputs of the developed model, the GA–BP neural network can be adopted as a precise method to forecast the rheological properties of GM reinforced with NS and PVA fibers.

Fracture behavior is one of the key properties to study concrete cracking under sodium chloride attack exposed to the freeze–thaw cycles environment, which is frequently neglected. In this paper, 24 single edge notch beam specimens and 24 cubes were poured. The corresponding freeze–thaw cycles test in sodium chloride solution, standard cube compressive strength of concrete test, and three-point-bending tests were carried out. The research revealed that the fracture toughness, fracture energy, relative dynamic modulus of elasticity, and standard cube compressive strength were decreased by increasing freeze–thaw cycles under sodium chloride attack, and the damage degree of concrete caused by sodium chloride solution was deeper than that of pure water. In particular, there existed good linear correlation between the fracture behavior and imposed freeze–thaw damage for various solution. Accordingly, a more reliable damage model using fracture control parameters as damage factors was proposed.

With the increasing demand for automation in agriculture, more and more researchers are exploring the application of digital twin in agricultural production. However, existing studies have predominantly focused on enhancing resource utilization efficiency and improving irrigation control systems in agricultural production through the implementation of digital twins. Unfortunately, there is a noticeable research gap when it comes to applying digital twins specifically to buried water conveyance pipelines within an agricultural irrigation infrastructure. Focusing on the long-term performance requirements of buried pipelines in agricultural irrigation and drainage, this study established a digital twin system for the industry of axial hollow-wall pipes with an outer diameter of 200 mm, specifically designed for this field of operation. The system was used to optimize the end-forming process of axial hollow-wall pipes, resulting in improved leak tightness under internal pressure and angular deflection of the pipes. The study suggests that the most effective method for the end-forming process of axial hollow-wall pipes is to heat the pipe for 60 s at the ambient temperature of 15 °C, with heating temperatures of 225 °C on both the inner and outer sides. Additionally, preheating the stamping equipment to 70 °C and controlling the cooling temperature, during pipe detachment, to between 35 °C and 45 °C is recommended. In terms of the leak tightness under internal pressure and angular deviation, the study found that increasing the thickness of the protruding end of the sealing ring to 16 mm, and shortening the chamfer length to 20 mm, while maintaining the same slope, can enhance the sealing effectiveness of the pipeline interface. The implementation of the digital twin system improves the production efficiency of the hollow pipe production line during the end-forming process, resulting in a yield rate of the pipe of up to 95% for qualified products. Moreover, the system provides intelligent closed-loop feedback which ensures the long-term operation and maintenance of the pipelines, making it easier to identify problems and implement design improvements. By doing so, it contributes to ensuring the long-term stability of related agricultural production.