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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Predictive Analysis for Construction Site using AI |
| Authors Name: | Sakshi Tayade , Akash Goyal , Pranjal Wani , Sanskruti Behar , Prof. Sweta Wankhade |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_211580
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| Published Paper Id: | IJNRD2401014 |
| Published In: | Volume 9 Issue 1, January-2024 |
| DOI: | |
| Abstract: | The construction industry, characterized by its complex and dynamic nature, demands efficient project management and accurate performance predictions to ensure successful project delivery. This research paper explores the application of artificial intelligence (AI) techniques in predicting and analyzing construction project outcomes. The study leverages machine learning algorithms, neural networks, and predictive modeling to enhance the accuracy of project performance forecasts. The methodology involves collecting and analyzing historical project data, including project schedules, budgetary allocations, resource utilization, and external factors affecting construction projects. Utilizing this data, the AI models are trained to recognize patterns and relationships, enabling them to make predictions on various project parameters such as completion time, cost overruns, and resource optimization. The research aims to contribute to the field by addressing the challenges of uncertainty and risk inherent in construction projects. The AI models developed in this study offer a proactive approach to project management, allowing for real-time adjustments and resource allocations based on predictive insights. This approach empowers project stakeholders to make informed decisions, mitigate potential risks, and optimize overall project performance. The paper discusses the results of the prediction analysis, highlighting the accuracy and reliability of the AI models in comparison to traditional methods. Additionally, it explores the potential impact of AI-driven prediction on project planning, risk management, and resource allocation strategies within the construction industry. |
| Keywords: | Artificial intelligence (AI), Machine learning algorithms, Neural networks, Predictive modelling, Risk Management, Accuracy, Reliability, Resource optimization. |
| Cite Article: | "Predictive Analysis for Construction Site using AI", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 1, page no.a168-a171, January-2024, Available :http://www.ijnrd.org/papers/IJNRD2401014.pdf |
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| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2401014 Registration ID: 211580 Published In: Volume 9 Issue 1, January-2024 DOI (Digital Object Identifier): Page No: a168-a171 Country: Pune, Maharashtra, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2401014 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2401014 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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