Sustainability in construction economics can be a significant barrier to the adoption of cloud computing in small-scale building projects. The construction industry has the potential to revolutionize with the application of intelligent computing, enhancing decision-making and project outcomes. Intelligent computing involves advanced computational techniques like artificial intelligence, machine learning, and automation to optimize processes and solve complex problems.
However, small-scale construction projects face challenges due to their specific nature, such as limited resources, budgets, and project durations. These challenges make it difficult to manage the upfront costs associated with adopting intelligent computing technologies. Data management is crucial for intelligent computing systems, but small projects often struggle with inconsistent data collection methods and insufficient data sources.
Technical expertise is another barrier for small-scale projects, as specialized skills in machine learning and artificial intelligence are often lacking. Cultural resistance to change and the complexity of integrating various software tools further complicate the adoption of intelligent computing. Overcoming these barriers requires tailored solutions that consider the unique constraints of small-scale projects.
This study aims to identify and address the challenges of implementing intelligent computing in small-scale construction projects. By using a mixed-methods approach that includes quantitative analysis, structural equation modeling, and a comprehensive literature review, the study highlights key problems such as complexity, data quality, economic and cultural factors, ethical concerns, limited data availability, and scalability issues.
The findings emphasize the importance of addressing these barriers to fully utilize intelligent computing in the construction sector
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