The Smart Supply Chain Revolution: Ai Innovations, Opportunities, And Strategic Challenges
DOI:
https://doi.org/10.63278/mme.vi.1907Keywords:
Machine Learning, Civil Engineering, Material Optimization, Structural Performance, Quantitative Study, Engineering Design, Artificial Intelligence, Reliability Analysis, Regression, Research Methodology.Abstract
Background:
The integration of machine learning (ML) in civil engineering design is an emerging trend aimed at improving efficiency, reducing material waste, and enhancing structural performance. As the construction industry embraces data-driven innovations, it becomes crucial to understand the quantitative impact and perceptions surrounding ML adoption.
Objective:
This study investigates how machine learning contributes to optimizing material usage and improving structural performance in civil engineering projects. It aims to identify key factors influencing successful ML integration and evaluate the relationship between these factors and project outcomes.
Methods:
A quantitative research design was employed using a structured questionnaire distributed to 273 professionals in civil engineering and AI-related fields. The study followed the research onion framework and adopted a deductive approach, grounded in positivist philosophy. Data were analyzed using descriptive statistics, correlation analysis, reliability testing (Cronbach’s Alpha), and multiple regression analysis.
Results:
The findings reveal that while the regression model had limited predictive strength (R² = 0.087), certain variables—such as algorithm type, optimization efficiency, and engineering expertise—significantly influenced structural performance outcomes. Most participants held positive views on ML integration, with a strong skew toward agreement in survey responses. However, the reliability of the questionnaire was weak, indicating a need for improved instrument design.
Conclusion:
Machine learning holds promise in civil engineering for enhancing material efficiency and structural design, but its success depends on quality data, professional expertise, and appropriate algorithm selection. Although the results show limited statistical strength, they highlight important areas for future research and practical application. Better tool design and interdisciplinary collaboration are recommended to fully realize the benefits of ML in this domain.
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Copyright (c) 2025 Muhammad Hashim Zia, Ahsan Ali, Hafiz Muhammad Asad Mustafa, Uroosh Niazi
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