542
Publications
37.9K
Citations
1.9K
Authors
843
Institutions
Integrated Predictive Intelligence for Industry 4.0
2019 - 2024
The period marks a consolidation of predictive maintenance and reliability analytics as a central AI paradigm across manufacturing, shipping, and industrial systems. It also foregrounds AI-enabled sustainability and energy optimization in urban, port, water, and construction contexts, together with engineering AI-driven design and smart manufacturing integration that spans design exploration, digital manufacturing, and automation. Human-centered AI development and labor governance, along with cross-domain digital twins and metaverse-enabled training, shape responsible deployment and knowledge transfer in complex systems.
• Predictive maintenance and reliability analytics stand out as a unifying AI paradigm across manufacturing, shipping, and industrial systems, integrating sensor data, IoT, and ML to forecast failures and optimize maintenance schedules [2], [9], [14].
• AI-driven sustainability in urban and logistical domains emphasizes environmental impact reduction, energy efficiency, and resource optimization in smart cities, ports, water systems, and construction via AI-enabled decision support [1], [3], [19], [12], [17].
• Engineering AI-enabled design and smart manufacturing integration unify design exploration, digital manufacturing, automation systems, and ML-driven optimization across AEC and industry, emphasizing system-wide ML deployment [4], [10], [11], [16], [6].
• Human-centered AI development and labor governance address the social-technical dimensions of AI, including platform labor, training/verification roles, and future labour demand [8], [7], [5].
• Metaverse, digital twins, and training/maintenance integration illustrate cross-domain AI-enabled capabilities, enabling immersive learning, diagnostics, and knowledge transfer in complex systems [18], [17].