This research paper is like a crash-test lab for AI in construction: it doesn’t sell you a tool, it runs experiments to see whether using AI actually helps engineers and project teams make better decisions or quietly makes things worse.
Leaders in construction are flooded with AI hype but lack hard evidence on when AI improves outcomes (cost, schedule, safety, design quality) versus when it introduces new risks (errors, bias, over‑reliance). This work provides empirical evidence to guide whether, where, and how AI should be adopted in engineering and construction workflows.
Empirical results and domain-specific methodology for evaluating AI in construction/engineering contexts; this type of validated experimental evidence is harder to replicate than generic AI tooling and can underpin standards, consulting methodologies, or internal governance frameworks.
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Medium (Integration logic)
The main constraint is not infrastructure but the external validity of the empirical studies: results may depend on specific tasks, datasets, and participant expertise, so scaling findings across companies, project types, and geographies requires careful replication and adaptation.
Early Adopters
Unlike vendor marketing material or generic AI case studies, this source is a peer‑reviewed empirical study in the construction/engineering domain, focusing on whether AI is genuinely beneficial or harmful in practice. Its differentiation is methodological rigor and neutrality rather than offering a specific product; it informs how to govern and deploy AI, not just how to build it.