Predictive Analysis for Construction Site using AI

Predictive Analysis for Construction Site using AI

Registration ID: IJNRD_211580

Published ID: IJNRD2401014

DOI: Click Here to Get

Sakshi Tayade , Akash Goyal , Pranjal Wani , Sanskruti Behar , Prof. Sweta Wankhade

Artificial intelligence (AI), Machine learning algorithms, Neural networks, Predictive modelling, Risk Management, Accuracy, Reliability, Resource optimization.

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.

Published Paper   E-Certificate

"Predictive Analysis for Construction Site using AI", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 1, page no.a168-a171, January-2024, Available :https://ijnrd.org/papers/IJNRD2401014.pdf

Volume 9 Issue 1, January-2024

Pages : a168-a171

Paper Reg. ID: IJNRD_211580

Published Paper Id: IJNRD2401014

Downloads: 000121137

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

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to explore advances in research pertaining to applied, theoretical and experimental Technological studies. The goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in and around the world. IJNRD will provide an opportunity for practitioners and educators of engineering field to exchange research evidence, models of best practice and innovative ideas.

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