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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM61833
Volume:
10 |
4 |
April 2026
Pages:
1-8
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Artificial intelligence and data science
Ajeenkya DY patil school of engineering
Department of Artificial intelligence and data science
Ajeenkya DY patil school of engineering
Department of Artificial intelligence and data science
Ajeenkya DY patil school of engineering
Department of Artificial intelligence and data science
Ajeenkya DY patil school of engineering
Department of Artificial intelligence and data science
Ajeenkya DY patil school of engineering
Department of Artificial intelligence and data science
Ajeenkya DY patil school of engineering
The rapid advancement of Artificial Intelligence (AI) has significantly transformed the way construction projects are planned and managed. Traditional construction cost estimation methods are often time-consuming, error-prone, and lack adaptability to changing market conditions. This research presents the design and implementation of an AI-based Construction Estimator System that automates cost prediction and material estimation by leveraging machine learning, data analytics, and intelligent optimization techniques. The proposed system analyzes key project parameters such as built-up area, number of floors, material specifications, labor costs, and location-based variations to generate accurate construction cost estimates. It integrates multiple data sources, including real-time material prices, regional labor rates, and historical project data, to provide intelligent predictions for overall budget and resource allocation. The system employs regression models and rule-based calculations to ensure reliable estimation of structural components such as cement, steel, bricks, and labor requirements. Additionally, the integration of AI-driven assistance enables users to interact with the system for insights, cost optimization suggestions, and decision support. The model continuously improves through user inputs and historical data, enhancing prediction accuracy and adaptability over time. The results demonstrate that the AI Construction Estimator significantly reduces manual effort while improving estimation accuracy and project planning efficiency. This research highlights the potential of AI in revolutionizing the construction industry by providing smart, scalable, and data-driven solutions for cost estimation and resource management.
KEYWORDS
The AI Construction Cost Estimator project demonstrates how Artificial Intelligence can simplify construction planning by generating accurate cost estimates based on user inputs such as area, materials, labor, and number of floors. The system provides detailed cost breakdowns and material requirements, making the estimation process faster, more efficient, and data-driven. Overall, it offers a smart and reliable solution for builders, contractors, and users involved in construction projects.
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Publication Date
4/30/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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