Application of Artificial Intelligence in Civil Engineering

Abstract

This study explores the application of Artificial Intelligence (AI) in movable dividers in civil engineering. By integrating AI with movable dividers, we can create smart, adaptable spaces that optimize space usage, acoustic performance, and energy efficiency. AI-powered movable dividers can automatically adjust to changing environmental conditions, predict maintenance needs, and provide insights on space usage and occupant behavior. This technology has the potential to revolutionize the design and operation of buildings, events, and exhibitions, enabling more efficient, safe, and flexible use of space. Our research demonstrates the benefits of AI-powered movable dividers, including improved space efficiency, enhanced safety, and data-driven decision making.

Introduction

Overview:
The integration of Artificial Intelligence (AI) into movable dividers is revolutionizing space management in civil engineering. These smart dividers can adapt dynamically to occupancy and environmental conditions, enhancing efficiency, comfort, and safety in buildings.

Key Objectives:

1. Optimize Space Usage: Analyze occupancy patterns to reconfigure spaces efficiently.

2. Improve Acoustic Performance: Adjust dividers based on noise levels.

3. Enable Predictive Maintenance: Forecast maintenance needs, reducing downtime.

Methodology:

• Data Collection: Sensors gather real-time data on occupancy, temperature, and humidity.

• Machine Learning: Models are trained to predict patterns and control divider behavior.

• System Integration: AI is linked with actuators for real-time movement and adjustments.

• Performance Tracking: Continuous learning improves energy use, user satisfaction, and space utilization.

Materials and Components:

• Smart Materials: Respond to environmental changes.

• Sensors & Actuators: Detect conditions and control divider movement.

• AI Software: Processes data and drives automation.

• Maxon Motors: High-quality motors enabling precise movement.

Advantages:

• Better space and energy efficiency

• Enhanced user experience and flexibility

• Improved safety and data-driven management

Disadvantages:

• High initial implementation cost

• Dependency on accurate, quality data

• Risks of AI bias and cybersecurity vulnerabilities

• Ongoing need for updates and maintenance

Results and Applications:

AI-powered movable dividers show:

• Increased operational efficiency

• Higher occupant comfort and productivity

• Greater adaptability to changing needs

• Long-term cost savings through reduced energy and maintenance costs

Conclusion

The integration of Artificial Intelligence (AI) in movable dividers is revolutionizing the field of civil engineering. By leveraging AI, movable dividers can optimize space usage, reduce energy consumption, and enhance occupant experience. These smart dividers can adapt to changing environmental conditions, detect occupant preferences, and adjust accordingly. With AI-powered movable dividers, buildings can become more efficient, sustainable, and responsive to user needs. This technology has the potential to transform the built environment, creating more liable, efficient, and sustainable spaces for occupants. As AI continues to evolve, we can expect to see even more innovative applications of this technology in civil engineering.

References

[1] https://www.ijcrt.org/papers/IJCRT2410471.pdf [2] https://www.researchgate.net/publication/353731405_Influence_of_Artificial_Intelligence_in_Civil_Engineering_toward_Sustainable_Development-A_Systematic_Literature_Review [3] https://www.ijert.org/smart-movable-road-divider [4] https://iarjset.com/wp-content/uploads/2021/06/IARJSET.2021.8554.pdf [5] https://www.jetir.org/view?paper=JETIR1905419

Copyright

Copyright © 2025 Pratik More, Amit Kadam , Samadhan Dongre, Amar Navne, Revansidh Bukka. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.