Publication2026
AI-powered delay risk prediction in construction projects: leveraging machine learning, deep learning and hybrid models
The record
- Type
- Publication
- Date
- 1 January 2026
- Level
- National
- Journal
- International Journal of Construction Management
- Volume
- 26
- Issue
- 12
- Pages
- 2837-2858
- Published
- 1 January 2026
- DOI
- 10.1080/15623599.2026.2619678
- Scopus ID
- 105029602759
- ISSN
- 15623599
- Authors
- 5
- Open access
- No
- Citations
- 1
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
9.2 Promote inclusive and sustainable industrialization and raise industry’s share of employment and GDP
CNN-BiLSTM hybrid neural network for construction delay prediction from 68 projects achieves 95.56% accuracy, enabling proactive project risk management.
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Deep learning algorithm development combining CNN and LSTM for temporal-spatial pattern recognition advances AI methodology innovation.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-2.4 Infrastructure and connectivity
Construction project scheduling optimization and risk prediction support infrastructure development and cost management.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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- 1
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Citing this record
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KIIT Deemed to be University. “AI-powered delay risk prediction in construction projects: leveraging machine learning, deep learning and hybrid models”. Kortex.