Abstract

The adoption of Intelligent Construction (IC) technologies, particularly AI-driven systems and autonomous robots, is pivotal for the architecture, engineering, and construction (AEC) industry’s transformation, yet their performance often varies significantly across projects - a challenge known as context dependency. Existing evaluation methods, focused on single-case studies, fail to capture this variance, hindering reliable investment decisions. To address this gap, we propose a novel “Expectation-Robustness” framework. The framework first utilizes a multi-criteria benefit evaluation system to quantify a technology’s performance in a single project into a standardized Overall Benefit Score (OBS). Then, by treating the OBS scores from multiple projects as a statistical sample, we define two key metrics: (1) Expected Benefit, measured by the mean of OBS, representing the technology’s average performance; and (2) Performance Robustness, measured by the inverse of the Coefficient of Variation (CV) of OBS, representing its stability across diverse contexts. We validated the framework using a large-scale dataset of 678 technology application cases from national IC pilot projects in China. An analysis of 10 typical technologies, with a special focus on construction robots (e.g., for floor-finishing and tile-laying), revealed distinct performance profiles, highlighting the critical role of performance robustness in real-world robotic deployment. Our framework moves beyond simplistic single-point estimates, offering a more scientific basis for stakeholders to make informed decisions on technology investment, research, development, and deployment, thereby promoting the sustainable development of intelligent construction.

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