Авторы

  • Л. Гепхалакшами

DOI:

https://doi.org/10.71337/inlibrary.uz.imjrd.113726

Аннотация

Artificial Intelligence (AI) is revolutionizing the engineering industry by enhancing productivity, precision, and innovation. This article explores the growing role of AI in various engineering disciplines, its benefits, challenges, and potential future developments.

 


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INTERNATIONAL MULTIDISCIPLINARY JOURNAL FOR

RESEARCH & DEVELOPMENT

SJIF 2019: 5.222 2020: 5.552 2021: 5.637 2022:5.479 2023:6.563 2024: 7,805

eISSN :2394-6334 https://www.ijmrd.in/index.php/imjrd Volume 12, issue 06 (2025)

22

THE ROLE OF ARTIFICIAL INTELLIGENCE IN MODERN ENGINEERING

Gephalakshami L.

independent researcher, specialist in technical sciences

Abstract:

Artificial Intelligence (AI) is revolutionizing the engineering industry by enhancing

productivity, precision, and innovation. This article explores the growing role of AI in various

engineering disciplines, its benefits, challenges, and potential future developments.

Introduction:

In the past decade, AI has transitioned from a theoretical concept to a practical tool used across

various industries. Engineering, being one of the most dynamic fields, has embraced AI to

improve design, manufacturing, maintenance, and overall efficiency. From predictive analytics in

mechanical systems to autonomous vehicles in transportation engineering, AI is reshaping how

engineers approach complex problems.

Applications of AI in Engineering:

1.

Design Optimization:

AI algorithms are being used to optimize designs by simulating numerous configurations and

selecting the most efficient ones. In civil engineering, for instance, AI tools help in designing

earthquake-resistant structures by predicting stress points.

2.

Predictive Maintenance:

Using sensors and machine learning, engineers can predict equipment failures before they occur.

This approach minimizes downtime and reduces maintenance costs in industries like aerospace

and manufacturing.

3.

Robotics and Automation:

AI powers intelligent robots capable of performing repetitive or hazardous tasks, increasing safety

and precision in industrial settings. These robots learn from their environment and improve their

performance over time.

4.

Smart Infrastructure:

AI is integral in developing smart cities, where traffic systems, energy consumption, and utilities

are managed using data-driven approaches. Structural health monitoring in bridges and buildings

also relies on AI for real-time analysis.

Challenges and Limitations:

Despite its advantages, AI adoption in engineering faces challenges such as data privacy concerns,

high implementation costs, and a lack of skilled personnel. There is also a risk of over-reliance on

AI, potentially leading to reduced human oversight.

Future Outlook:

The integration of AI with other emerging technologies like IoT, blockchain, and quantum

computing will further enhance engineering capabilities. Continuous research and development,

along with ethical guidelines, will ensure the safe and effective use of AI in engineering.

Conclusion:

Artificial Intelligence is becoming an indispensable part of modern engineering. While challenges


background image

INTERNATIONAL MULTIDISCIPLINARY JOURNAL FOR

RESEARCH & DEVELOPMENT

SJIF 2019: 5.222 2020: 5.552 2021: 5.637 2022:5.479 2023:6.563 2024: 7,805

eISSN :2394-6334 https://www.ijmrd.in/index.php/imjrd Volume 12, issue 06 (2025)

23

remain, the potential benefits in terms of efficiency, innovation, and safety are immense.

Engineers of the future must be equipped with both technical skills and AI literacy to fully

harness the power of this transformative technology.

Artificial Intelligence has proven to be a transformative force in modern engineering. It is not

merely a tool but a co-decision maker that helps in managing complexity and uncertainty in

engineering tasks.

The key benefits of AI in engineering include:

Improved efficiency through automation and optimization

Reduced costs via predictive maintenance and smart resource use

Enhanced safety in high-risk environments through robotics

Better decision-making based on real-time data and simulations

However, engineers must be cautious of several limitations. AI systems require large volumes of

high-quality data to function correctly. There is also a risk of algorithmic bias, and over-reliance

on AI can reduce critical human oversight. Additionally, integrating AI systems into existing

workflows demands significant time and financial investment.

Future engineering practices

will depend heavily on interdisciplinary knowledge. Engineers

should be equipped not only with technical knowledge in their core disciplines but also with skills

in data science, AI ethics, and programming. Academic institutions and industry leaders should

collaborate to create training programs that address this growing need.

In summary, AI is a powerful asset for engineers, but its successful implementation depends on

responsible usage, continuous learning, and thoughtful integration into engineering systems.

References:

1.

Russell, S., & Norvig, P. (2021).

Artificial Intelligence: A Modern Approach

(4th ed.).

Pearson.

2.

Goodfellow, I., Bengio, Y., & Courville, A. (2016).

Deep Learning

. MIT Press.

3.

Mohammadi, M., Al-Fuqaha, A., Sorour, S., & Guizani, M. (2018). Deep learning for IoT

big data and streaming analytics: A survey.

IEEE Communications Surveys & Tutorials

, 20(4),

2923–2960.

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Юллиев, Н. Ж. (2022). Определение физической подготовленности спасателей в

условиях среднегорья. In

ТРУДЫ ХIII ЕВРАЗИЙСКОГО НАУЧНОГО ФОРУМА

(pp. 259-

262).

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УМУМИЙ

ЎРТА

ТАЪЛИМ

МАКТАБЛАРИДА

ЎРГАНИШДА

ЁШЛАРНИ

ВАТАПАРВАРЛИК РУҲИДА ТАРБИЯЛАШНИНГ АҲАМИЯТИ.

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background image

INTERNATIONAL MULTIDISCIPLINARY JOURNAL FOR

RESEARCH & DEVELOPMENT

SJIF 2019: 5.222 2020: 5.552 2021: 5.637 2022:5.479 2023:6.563 2024: 7,805

eISSN :2394-6334 https://www.ijmrd.in/index.php/imjrd Volume 12, issue 06 (2025)

24

PRESCHOOL EDUCATION.

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П84

Профилактическая медицина-2020: сборник научных трудов Все-российской научно-

практической конференции с международным участи-ем. 18–19 ноября 2020 года/под ред.

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Automation in Construction

, 112, 103083.

Библиографические ссылки

Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

Mohammadi, M., Al-Fuqaha, A., Sorour, S., & Guizani, M. (2018). Deep learning for IoT big data and streaming analytics: A survey. IEEE Communications Surveys & Tutorials, 20(4), 2923–2960.

Юллиев, Н. Ж. (2022). Определение физической подготовленности спасателей в условиях среднегорья. In ТРУДЫ ХIII ЕВРАЗИЙСКОГО НАУЧНОГО ФОРУМА (pp. 259-262).

Turdaliyeva, N. (2025). DIFFERENT TYPES OF MANUAL LABOR FOR CHILDREN AND THEIR IMPACT ON CREATIVE DEVELOPMENT. Journal of Multidisciplinary Sciences and Innovations, 1(1), 563-568.

Файзуллаев, Т., & Хужамбердиева, Ш. (2020). ЭРКИН ВОҲИДОВ ИЖОДИНИ УМУМИЙ ЎРТА ТАЪЛИМ МАКТАБЛАРИДА ЎРГАНИШДА ЁШЛАРНИ ВАТАПАРВАРЛИК РУҲИДА ТАРБИЯЛАШНИНГ АҲАМИЯТИ. Scientific Bulletin of Namangan State University, 2(4), 543-546.

Boymirzayeva, S. (2025). DIDACTIC FORMS AND METHODS OF PEDAGOGICAL SUPPORT AND TARGETED DEVELOPMENT OF CHILDREN IN THE PROCESS OF PRESCHOOL EDUCATION. Journal of Multidisciplinary Sciences and Innovations, 1(1), 557-562.

Lee, J., Bagheri, B., & Kao, H. A. (2015). A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18–23.

Bini, S. A. (2018). Artificial intelligence, machine learning, deep learning, and cognitive computing: What do these terms mean and how will they impact health care? The Journal of Arthroplasty, 33(8), 2358–2361.

Sobirjonovich, S. I. (2023). Systemic Organization of Professional Competence, Creativity and Innovative Activity of A Future Kindergartener. Journal of Pedagogical Inventions and Practices, 19, 108-112.

Abdurashidov, A., & Turdaliyeva, N. (2023). DEVELOPMENT OF MANUAL WORK IN PRE-SCHOOL EDUCATION. Science and innovation, 2(B2), 282-286.

Мухамедова, М. Г., Куртиева, Ш. А., & Назарова, Ж. А. (2020). СИНДРОМ ФУНКЦИОНАЛЬНОЙ КАРДИОПАТИИ У СОВРЕМЕННЫХ ПОДРОСТКОВ. In П84 Профилактическая медицина-2020: сборник научных трудов Все-российской научно-практической конференции с международным участи-ем. 18–19 ноября 2020 года/под ред. АВ Мельцера, ИШ Якубовой. Ч. 2.—СПб.: Изд-во СЗГМУ им. ИИ Мечникова, 2020.—304 с. (p. 105).

qizi Turdaliyeva, N. A. (2024). MAKTABGACHA YOSHDAGI BOLALAR IJODIY QOBILIYATLARNI RIVOJLANTIRISHNING NAZARIY ASOSLARI. GOLDEN BRAIN, 2(7), 48-52.

Zhang, Y., & Wang, J. (2020). Applications of Artificial Intelligence in Civil Engineering. Automation in Construction, 112, 103083.