Special Issue: Emerging Approaches for the Performance Assessment and Prediction of Cement-Based Materials
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Department of Civil, Geological, and Environmental Engineering, College of Engineering and Mines, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
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Department of Computer and Electrical Engineering, California State University, Bakersfield, CA 93311, USA
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Authors to whom correspondence should be addressed.
Materials 2023, 16(21), 6974; https://doi.org/10.3390/ma16216974
Submission received: 27 September 2023
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Accepted: 7 October 2023
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Published: 31 October 2023
(This article belongs to the Special Issue Emerging Approaches for Performance Assessment and Prediction of Cement-Based Materials)
The current Special Issue, entitled “Emerging Approaches for Performance Assessment and Prediction of Cement-Based Materials”, aims to showcase cutting-edge research into the technologies, smart sensing systems, and tools for assessing and predicting the performance of cement-based materials. Special emphasis is placed on understanding innovative strategies for characterizing the microstructure and durability of these materials, as well as harnessing smart sensing systems and data-driven approaches, all of which hold the potential to redefine our modern infrastructure.
Cement-based materials have always been the backbone of our construction industry, providing strength, durability, and resilience to our built environment. Over the years, researchers have delved deep into understanding the intricate physicochemical processes that govern the behavior of these materials, studying hydration processes, the formation of various crystalline and amorphous phases, and interactions with admixtures as well as deterioration mechanisms [1,2,3]. The advent of smart sensing systems has added a new dimension to this research, allowing for the real-time monitoring of stress, strain, and environmental impacts on concrete structures, thereby enabling timely interventions [4]. Furthermore, data-driven approaches, bolstered by the rise of machine learning and artificial intelligence, are beginning to play a pivotal role in predicting the long-term performance of concrete structures. These methodologies not only rely on historical data but also incorporate real-time feedback from sensors, leading to highly accurate and dynamic predictive models [5].
The research scope of this Special Issue encompasses, but is not limited to, advanced imaging techniques for cement microstructure; novel experimental and computational methodologies for durability assessments; predictive modeling using data analytics; and the integration and utility of smart sensing systems in cement-based constructs. Additionally, leveraging the power of big data to develop high-performance composites with better properties and enhanced performance, remains the focal point of this Special Issue.
Conflicts of Interest
The authors declare no conflict of interest.
References
- Scrivener, K.; Ouzia, A.; Juilland, P.; Mohamed, A.K. Advances in understanding cement hydration mechanisms. Cem. Concr. Res. 2019, 124, 105823. [Google Scholar] [CrossRef]
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- Marchon, D.; Flatt, R.J. Impact of chemical admixtures on cement hydration. In Science and Technology of Concrete Admixtures; Woodhead Publishing: Sawston, UK, 2016; pp. 279–304. [Google Scholar]
- Sony, S.; Laventure, S.; Sadhu, A. A literature review of next-generation smart sensing technology in structural health monitoring. Struct. Control. Health Monit. 2019, 26, e2321. [Google Scholar] [CrossRef]
- Azimi, M.; Eslamlou, A.D.; Pekcan, G. Data-driven structural health monitoring and damage detection through deep learning: State-of-the-art review. Sensors 2020, 20, 2778. [Google Scholar] [CrossRef] [PubMed]
Short Biography of Authors
Farzadnia is a concrete scientist with expertise in designing, modeling, characterizing, and optimizing cementitious composites, as well as sustainable alternative binders, smart materials, and robotic construction. Farzadnia is an Assistant Professor at the University of Alaska Fairbanks, USA. He contributes to the editorial landscape as a member of the editorial team for the Crystals journal and has also taken on the role of guest editor for the Special Issue “Cement-based Crystals.” Additionally, he has participated as a technical or organizing committee member in several conferences and co-chaired the Gordon Research Seminar on Advanced Materials for Sustainable Infrastructure Development in 2020. He has published over 60 journal papers in high-impact-factor journals in the field.
Amin Malek is an experienced researcher with a specific discipline in Electronics, Design, Fabrication, and Data Analysis. He is a Fellow of the UK Higher Education Academy (FHEA), a Senior Member of IEEE, and a member of the Engineering Council (CEng), IET. Malek is an Associate Professor at California State University, Bakersfield, California, USA. He has been awarded over USD 1.5 million in grant monies and endowments. He has published over 100 scientific research papers and a postgraduate textbook and delivered a few keynote speeches at international scientific conferences around the globe. Thus far, he has four patents for digital communication systems.
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MDPI and ACS Style
Farzadnia, N.; Malek, A. Special Issue: Emerging Approaches for the Performance Assessment and Prediction of Cement-Based Materials. Materials 2023, 16, 6974. https://doi.org/10.3390/ma16216974
AMA Style
Farzadnia N, Malek A. Special Issue: Emerging Approaches for the Performance Assessment and Prediction of Cement-Based Materials. Materials. 2023; 16(21):6974. https://doi.org/10.3390/ma16216974
Chicago/Turabian StyleFarzadnia, Nima, and Amin Malek. 2023. "Special Issue: Emerging Approaches for the Performance Assessment and Prediction of Cement-Based Materials" Materials 16, no. 21: 6974. https://doi.org/10.3390/ma16216974
APA StyleFarzadnia, N., & Malek, A. (2023). Special Issue: Emerging Approaches for the Performance Assessment and Prediction of Cement-Based Materials. Materials, 16(21), 6974. https://doi.org/10.3390/ma16216974
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