Paper Title
Predictive Analysis for Construction Site using AI
Article Identifiers
Authors
Sakshi Tayade , Akash Goyal , Pranjal Wani , Sanskruti Behar , Prof. Sweta Wankhade
Keywords
Artificial intelligence (AI), Machine learning algorithms, Neural networks, Predictive modelling, Risk Management, Accuracy, Reliability, Resource optimization.
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.
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How To Cite (APA)
Sakshi Tayade, Akash Goyal, Pranjal Wani, Sanskruti Behar, & Prof. Sweta Wankhade (January-2024). Predictive Analysis for Construction Site using AI. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(1), a168-a171. https://ijnrd.org/papers/IJNRD2401014.pdf
Issue
Volume 9 Issue 1, January-2024
Pages : a168-a171
Other Publication Details
Paper Reg. ID: IJNRD_211580
Published Paper Id: IJNRD2401014
Downloads: 000121980
Research Area: Engineering
Country: Pune, Maharashtra, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2401014.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2401014
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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition
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