Abstract
The use of artificial intelligence (AI) in high-rise building rectification is emerging as an essential way to address structural issues. This study explores how AI technologies are used in lifting, grouting, and reinforcement to enhance safety, efficiency, and sustainability in structural realignment practices. The PRISMA-ScR framework was utilized for conducting a scoping study published over the last seven years on AI-driven advancements in grouting, lifting reinforcement, and structural health monitoring (SHM) technologies. Furthermore, thematic analysis was employed to determine the recent developments, barriers encountered along the way, and future directions using data from major academic databases. The results indicate that AI helps with structural realignment by using predictive analysis, digital twin models, IoT monitoring systems, and automated defect detection. AI-driven reinforcement optimizes load distribution and enhances structural durability. In addition, AI-assisted grouting improves material selection, injection precision, and quality assessment, while AI-supported lifting techniques ensure real-time load balancing and effective risk management. However, challenges such as data reliability, high implementation costs, and interpretation capabilities hinder broader implementation. AI may improve structural realignment for high-rise buildings, but it also needs advances in explaining AI (XAI) and frameworks for data sharing, interdisciplinary collaboration, cybersecurity risk mitigation, and regulation compliance to ensure safe AI integration into everyday processes. Finally, this work provided future directions on grouting and lifting strengthening methods.
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Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
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Cui, X. Artificial intelligence assisted structural realignment of high-rise buildings through lifting, grouting and reinforcement. Sci Rep (2026). https://doi.org/10.1038/s41598-026-48789-5
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DOI: https://doi.org/10.1038/s41598-026-48789-5
