Construction Delays Due to Weather in Cold Regions: A Two-Stage Structural Equation Modeling and Artificial Neural Network Approach
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....
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/15/11/1916 |
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| author | Atul Kumar Singh Faizan Anjum Pshtiwan Shakor Varadhiyagounder Ranganathan Prasath Kumar Sathvik Sharath Chandra Saeed Reza Mohandes Bankole Awuzie |
| author_facet | Atul Kumar Singh Faizan Anjum Pshtiwan Shakor Varadhiyagounder Ranganathan Prasath Kumar Sathvik Sharath Chandra Saeed Reza Mohandes Bankole Awuzie |
| author_sort | Atul Kumar Singh |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-61514d4ea72143b58e491a113e4b17dd |
| institution | OA Journals |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-61514d4ea72143b58e491a113e4b17dd2025-08-20T02:33:06ZengMDPI AGBuildings2075-53092025-06-011511191610.3390/buildings15111916Construction Delays Due to Weather in Cold Regions: A Two-Stage Structural Equation Modeling and Artificial Neural Network ApproachAtul Kumar Singh0Faizan Anjum1Pshtiwan Shakor2Varadhiyagounder Ranganathan Prasath Kumar3Sathvik Sharath Chandra4Saeed Reza Mohandes5Bankole Awuzie6Department of Civil Engineering, Chandigarh University, Mohali 140413, IndiaDepartment of Civil Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur 603203, IndiaTechnical College of Engineering, Sulaimani Polytechnic University, Sulaymaniyah 46001, IraqDepartment of Civil Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur 603203, IndiaDepartment of Civil Engineering, Dayananda Sagar College of Engineering, Bengaluru 560111, IndiaDepartment of Civil Engineering and Management, School of Engineering, The University of Manchester, Manchester M13 9PL, UKDepartment of Construction Management and Quantity Surveying, University of Johannesburg-Doornfontein Campus, Johannesburg 2028, South AfricaSignificant 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.https://www.mdpi.com/2075-5309/15/11/1916construction managementdelaysweatherexploratory factor analysisstructural equation modelartificial neural network |
| spellingShingle | Atul Kumar Singh Faizan Anjum Pshtiwan Shakor Varadhiyagounder Ranganathan Prasath Kumar Sathvik Sharath Chandra Saeed Reza Mohandes Bankole Awuzie Construction Delays Due to Weather in Cold Regions: A Two-Stage Structural Equation Modeling and Artificial Neural Network Approach Buildings construction management delays weather exploratory factor analysis structural equation model artificial neural network |
| title | Construction Delays Due to Weather in Cold Regions: A Two-Stage Structural Equation Modeling and Artificial Neural Network Approach |
| title_full | Construction Delays Due to Weather in Cold Regions: A Two-Stage Structural Equation Modeling and Artificial Neural Network Approach |
| title_fullStr | Construction Delays Due to Weather in Cold Regions: A Two-Stage Structural Equation Modeling and Artificial Neural Network Approach |
| title_full_unstemmed | Construction Delays Due to Weather in Cold Regions: A Two-Stage Structural Equation Modeling and Artificial Neural Network Approach |
| title_short | Construction Delays Due to Weather in Cold Regions: A Two-Stage Structural Equation Modeling and Artificial Neural Network Approach |
| title_sort | construction delays due to weather in cold regions a two stage structural equation modeling and artificial neural network approach |
| topic | construction management delays weather exploratory factor analysis structural equation model artificial neural network |
| url | https://www.mdpi.com/2075-5309/15/11/1916 |
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