By Nidhi DhullReviewed by Susha Cheriyedath, M.Sc.May 2 2024
A recent editorial published in Applied Sciences discussed the latest advances in concrete structures and their impact on the sustainable development of society, economy, and technology. The papers included in this special issue were classified into groups covering distinct aspects of sustainable infrastructures.
Background
Structural concrete, which is made up of cement, water, and fine and coarse aggregates, is a fundamental material in civil engineering infrastructure. However, despite its many benefits, the cement in concrete is a major source of anthropogenic carbon dioxide emissions. Consequently, the United Nations Sustainable Development Goals pose a challenge to the concrete industry to ensure responsible and rational use of cement and steel.
Current technological advancements, including machine learning (ML) algorithms, artificial intelligence (AI), and life cycle assessment tools for buildings, are enhancing the structural concrete sector. These technologies enable better control of environmental impacts and expand opportunities for recycling and recirculation, thus steering the industry toward greater sustainability.
However, the scientific community often overlooks certain aspects of concrete, failing to explore alternative ideas or evaluate their impact on the sustainable development of society, economy, and technology. This serves as the foundation for this special issue, which concentrates on advancements in concrete structures aimed at fostering a sustainable future.
Published Studies
The 14 contributions to this special issue were categorized into four groups. Papers 1, 9, 10, and 11 discussed the advances in comprehending cement and concrete chemistry. They focused on improving techniques that accurately predict the functional life of infrastructures and incorporating recycled materials such as mineral wool waste and cow dung ash. They demonstrated the significant potential and synergy of cement used with such recycled materials for sustainable development.
The management and maintenance of reinforced concrete structures are essential for ensuring their durability and safety. For example, de-icing salts are used in bridge decks to deal with corrosion of steel rebars. Contribution 11 investigated the use of de-icing salts on a bridge repaired in 2020 and suggested that additional high-performance waterproof treatments during the construction phases are essential to ensure the safety of such critical structures.
Papers 2, 3, 4, 7, and 14 presented experimental characterizations of the mechanical behavior of structural concrete and their quantitative results. The experiments on soil-foundation interaction explored the use of fiber-reinforced polymers and cementitious matrices for structural retrofitting of existing structures. These studies employed appropriate analyses and non-destructive testing methods to validate the safety indexes of existing buildings.
Studies 5 and 6 evaluated the structural behavior of concrete through experimental and numerical tests, focusing on the effect of seismic action. Their objective was to propose methodologies for achieving resilient building designs while prioritizing the safety of occupants. Moreover, these studies tackled the challenge of maintaining aesthetic standards in new structures without compromising seismic safety.
This editorial highlights the power of the current numerical analysis tools and innovative retrofit solutions for seismic assessment of existing reinforced concrete buildings. Seismically vulnerable structures can undergo reinforcement using auxiliary reinforced concrete structures and infills. Alternatively, advanced numerical methods like Monte Carlo simulations can aid in designing resilient structures without sacrificing aesthetic considerations.
Studies 8, 12, and 13 used the latest technological advances, such as ML and AI, to analyze large databases to improve experiment designs, optimize concrete dosage parameters, and estimate concrete’s mechanical properties. ML methods are applied to classical problems in concrete technology, such as predicting the properties of fresh and hardened conventional and self-compacting concrete mixes along with the effects of adding waste materials.
AI algorithms, such as Procedural Binary Particle Swarm Optimization, can be trained to accurately estimate concrete mixture properties like density and compressive strength values. These proposed AI methodologies prove instrumental in developing sustainable concrete due to their rapidity, precision, and cost-effectiveness.
Conclusion
In summary, despite cement's long-standing environmental challenges, research efforts have persisted, focusing on innovative and eco-friendly infrastructure solutions while also prioritizing the preservation of historical structures.
Emerging approaches in construction materials, such as utilizing recycled materials and supplementary cementitious materials, alongside effective preservation techniques, aim to address environmental concerns while ensuring infrastructure longevity and safety.
A wide array of disciplines and continually evolving numerical analysis methods are deployed to assess the structural integrity of concrete buildings and infrastructure against seismic forces. However, the variability in seismic standards across regions poses challenges to drawing universal conclusions on resilient building design.
Recently, AI has emerged as a key player in developing advanced, sustainable cement-based products. Nevertheless, its widespread adoption in the construction industry necessitates rigorous field testing, experimental validation, and comprehensive mechanical property datasets.
In conclusion, the methodologies outlined in this editorial offer a promising framework for future endeavors aimed at reducing production costs, mitigating environmental impacts, and enhancing the technical performance of mortars and concrete.
Journal Reference
Diaferio, M., & Varona, F. B. (2024). Concrete Structures: Latest Advances and Prospects for a Sustainable Future. Applied Sciences, 14(9), 3803–3803. https://doi.org/10.3390/app14093803, https://www.mdpi.com/2076-3417/14/9/3803
Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.