Construction Delays Due to Weather in Cold Regions: A Two-Stage Structural Equation Modeling and Artificial Neural Network Approach

Singh, AK; Anjum, F; Shakor, P; Kumar, VRP; Sharath Chandra, S; Mohandes, SR; Awuzie, B

Construction Delays Due to Weather in Cold Regions: A Two-Stage Structural Equation Modeling and Artificial Neural Network Approach

Singh, AK Anjum, F Shakor, P

Kumar, VRP Sharath Chandra, S Mohandes, SR Awuzie, B

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Buildings, 2025, 15, (11)
Issue Date:: 2025-06-01

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Field	Value	Language
dc.contributor.author	Singh, AK
dc.contributor.author	Anjum, F
dc.contributor.author	Shakor, P https://orcid.org/0000-0002-6617-261X
dc.contributor.author	Kumar, VRP
dc.contributor.author	Sharath Chandra, S
dc.contributor.author	Mohandes, SR
dc.contributor.author	Awuzie, B
dc.date.accessioned	2026-01-14T03:07:19Z
dc.date.available	2026-01-14T03:07:19Z
dc.date.issued	2025-06-01
dc.identifier.citation	Buildings, 2025, 15, (11)
dc.identifier.issn	2075-5309
dc.identifier.issn	2075-5309
dc.identifier.uri	http://hdl.handle.net/10453/191793
dc.description.abstract	Significant weather-induced delays often plague construction projects in India’s extremely cold regions, yet comprehensive studies addressing this issue remain scarce. This study aims to fill this gap by identifying key delay factors and proposing mitigation strategies for the construction industry. Through an extensive literature review, 42 delay factors were identified and categorized into four groups. A survey of 83 experts from cold regions was conducted to evaluate these factors’ significance to contractors and subcontractors. Employing exploratory factor analysis (EFA), structural equation modeling (SEM), and artificial neural networks (ANN), the study analyzed the relationships between these factors and ranked their impact. The findings reveal that snowfall, rainfall, and low temperatures are the most significant contributors to delays, with snowfall being the most influential (significance: 1.000), followed by rainfall (0.890) and low temperatures (0.790). This research establishes a risk hierarchy and develops a predictive model to facilitate the proactive scheduling of challenging tasks during favorable seasons. This study advances the understanding of weather-induced delays in India’s cold regions and offers valuable insights for project management in such climates. However, it underscores the importance of clearly articulating its novel contributions to differentiate it within the existing literature on weather-related construction delays.
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	Buildings
dc.relation.isbasedon	10.3390/buildings15111916
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	1201 Architecture, 1202 Building, 1203 Design Practice and Management
dc.subject.classification	3301 Architecture
dc.subject.classification	3302 Building
dc.subject.classification	4005 Civil engineering
dc.title	Construction Delays Due to Weather in Cold Regions: A Two-Stage Structural Equation Modeling and Artificial Neural Network Approach
dc.type	Journal Article
utslib.citation.volume	15
utslib.for	1201 Architecture
utslib.for	1202 Building
utslib.for	1203 Design Practice and Management
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2026-01-14T03:07:17Z
pubs.issue	11
pubs.publication-status	Published
pubs.volume	15
utslib.citation.issue	11

Abstract:

Significant weather-induced delays often plague construction projects in India’s extremely cold regions, yet comprehensive studies addressing this issue remain scarce. This study aims to fill this gap by identifying key delay factors and proposing mitigation strategies for the construction industry. Through an extensive literature review, 42 delay factors were identified and categorized into four groups. A survey of 83 experts from cold regions was conducted to evaluate these factors’ significance to contractors and subcontractors. Employing exploratory factor analysis (EFA), structural equation modeling (SEM), and artificial neural networks (ANN), the study analyzed the relationships between these factors and ranked their impact. The findings reveal that snowfall, rainfall, and low temperatures are the most significant contributors to delays, with snowfall being the most influential (significance: 1.000), followed by rainfall (0.890) and low temperatures (0.790). This research establishes a risk hierarchy and develops a predictive model to facilitate the proactive scheduling of challenging tasks during favorable seasons. This study advances the understanding of weather-induced delays in India’s cold regions and offers valuable insights for project management in such climates. However, it underscores the importance of clearly articulating its novel contributions to differentiate it within the existing literature on weather-related construction delays.

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http://hdl.handle.net/10453/191793