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Infrastructure projects have major effects on implementing the principles of sustainable development. Infrastructure projects will continue to be developed in the coming years, particularly in developing countries such as China and India; therefore, it is important to find methods and solutions for improving the sustainability of them. Although existing studies have suggested various methods for practicing sustainable development principles in the process of implementing infrastructure projects, effective assessment indicators are unavailable, which presents a barrier to the effective assessment of infrastructure project sustainability. This study introduces key assessment indicators (KAIs) for assessing the sustainability performance of an infrastructure project. The research data used for analysis were collected from a questionnaire survey given to three groups of experts, including government officials, professionals, and clients in the Chinese construction industry. The fuzzy set theory was used to establish KAIs. A procedure for using the KAIs is demonstrated by a case study. These research findings provide an alternative solution to appraise the sustainability of infrastructure projects.

Human-in-the-loop (HITL) AI may enable an ideal symbiosis of human experts and AI models, harnessing the advantages of both while at the same time overcoming their respective limitations. The purpose of this study was to investigate a novel collective intelligence technology designed to amplify the diagnostic accuracy of networked human groups by forming real-time systems modeled on biological swarms. Using small groups of radiologists, the swarm-based technology was applied to the diagnosis of pneumonia on chest radiographs and compared against human experts alone, as well as two state-of-the-art deep learning AI models. Our work demonstrates that both the swarm-based technology and deep-learning technology achieved superior diagnostic accuracy than the human experts alone. Our work further demonstrates that when used in combination, the swarm-based technology and deep-learning technology outperformed either method alone. The superior diagnostic accuracy of the combined HITL AI solution compared to radiologists and AI alone has broad implications for the surging clinical AI deployment and implementation strategies in future practice.

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