Identifying Emerging Risks in Ground Reinforcement Systems and Sensor Integration Using Text Mining

초록

Ground reinforcement systems (GRS) are vital for stabilizing rock and soil in geotechnical applications such as slopes, tunnels, and mines. Although conventional risk factors, such as mechanical stress, corrosion, and improper installation, have been well studied, the emerging risks associated with environmental conditions, dynamic loads, and sensor-integrated monitoring remain underexplored. Sensor integration facilitates continuous monitoring and proactive interventions; however, it also presents challenges, such as calibration sensitivity, potential sensor malfunctions, and complex data interpretation needs. In this study, a scoping review is conducted following the PRISMA-ScR guidelines and text mining and network analysis are applied to examine the relationships between reinforcement components and risk factors. The findings revealed key emerging risks, including freeze-thaw cycles, non-uniform pretension, and sensor reliability issues. By categorizing risks into four domains, namely, geological and environmental factors, failure modes, engineering and design factors, and installation and operational factors— the study aimed to highlight the interactions that may compromise reinforcement stability. These implications emphasize the need for a comprehensive approach to design, installation, and monitoring in order to mitigate emerging risks, particularly because sensor technology is becoming increasingly more integral to infrastructure safety. By addressing poorly assessed risks associated with ground reinforcement, this study contributes to the existing literature and provides insights that can contribute to guiding future geotechnical engineering strategies, emphasizing adaptive design and robust monitoring systems to enhance long-term stability. Future research should examine adaptive sensor calibration and material resilience to further advance safety practices.

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