Evidence map›Paper›PMID 42014780›Full record

ArticleScientific reports2026

Artificial intelligence assisted structural realignment of high-rise buildings through lifting, grouting and reinforcement.

Xuedong Cui

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Xuedong CuiBeijing Hengxiang Hongye Foundation Reinforcement Technology Co. Ltd, Beijing, 100097, China. hengxianghongye@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AI-based reinforcementArtificial intelligenceHigh-rise building correctionPRISMA-ScRSmart groutingStructural stabilityUrban resilience

Identifiers

PMID42014780
PMCPMC13269489

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.