About this Research Topic
Computational Intelligence (CI) has emerged as a transformative force in the field of Infrastructure Materials Engineering, revolutionizing the way we design, analyze, and optimize construction materials and processes. This interdisciplinary approach leverages advanced computational techniques, machine learning, and artificial intelligence to address the complex challenges faced by civil engineers and material scientists.
The aim of this Research Topic is to apply CI algorithms in order to facilitate data-driven decision-making in infrastructure-based projects. By analyzing vast datasets from construction sites, CI algorithms can identify patterns and trends that help optimize construction schedules and reduce costs and energy consumption. Additionally, real-time monitoring of infrastructure health using sensors and CI algorithms can predict maintenance needs, ensuring the longevity of critical structures. Enhanced safety in infrastructures through advanced predictive maintenance using CI algorithms is another key objective, proactively detecting and mitigating potential hazards. This collaborative effort between CI and infrastructure development promises to revolutionize the way we construct and maintain essential facilities.
The scope of this Research Topic encompasses the application of advanced CI techniques to address critical challenges in the field of materials engineering for infrastructure design, construction, and maintenance. This interdisciplinary domain seeks to leverage artificial intelligence, machine learning, data analytics, and modeling to optimize material selection, design, and performance prediction. Specific areas of interest include the development of predictive models for material behavior under varying conditions, the utilization of data-driven insights to enhance material sustainability, and the integration of computational intelligence to improve material testing, quality control, durability assessment and forecasting of essential variables. This scope fosters innovation and knowledge exchange at the intersection of materials engineering and CI, advancing the sustainability, efficiency, and resilience of infrastructure materials.
The aim of this Research Topic is to apply CI algorithms in order to facilitate data-driven decision-making in infrastructure-based projects. By analyzing vast datasets from construction sites, CI algorithms can identify patterns and trends that help optimize construction schedules and reduce costs and energy consumption. Additionally, real-time monitoring of infrastructure health using sensors and CI algorithms can predict maintenance needs, ensuring the longevity of critical structures. Enhanced safety in infrastructures through advanced predictive maintenance using CI algorithms is another key objective, proactively detecting and mitigating potential hazards. This collaborative effort between CI and infrastructure development promises to revolutionize the way we construct and maintain essential facilities.
The scope of this Research Topic encompasses the application of advanced CI techniques to address critical challenges in the field of materials engineering for infrastructure design, construction, and maintenance. This interdisciplinary domain seeks to leverage artificial intelligence, machine learning, data analytics, and modeling to optimize material selection, design, and performance prediction. Specific areas of interest include the development of predictive models for material behavior under varying conditions, the utilization of data-driven insights to enhance material sustainability, and the integration of computational intelligence to improve material testing, quality control, durability assessment and forecasting of essential variables. This scope fosters innovation and knowledge exchange at the intersection of materials engineering and CI, advancing the sustainability, efficiency, and resilience of infrastructure materials.
Keywords: Sustainability, Durability, Theory-guided ML/AI, Probabilistic and reliability methods, Risk-based approach in design and construction, Resilience, Material Selection, Infrastructure Development, Material Performance, Hybrid-based intelligence techniques
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