Latest Advances in Optimized Concrete Mix Design: Assessing Physical Properties, Mechanical Performance and Long-Term Durability

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Materials, and Repair & Renovation".

Deadline for manuscript submissions: closed (20 October 2024) | Viewed by 1734

Special Issue Editors


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Guest Editor
Associate Professor, Department of Physical Sciences & Applications, Hellenic Army Academy, RRV8+VQ Vari, Attica, Greece
Interests: clinker; cement; concrete; green cements; concrete mix design; secondary raw materials; cement mortars; aggregates; building materials; low CO2 technologies

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Guest Editor
Department of Civil Engineering, School of Engineering, University of West Attica, 122 43 Egaleo, Greece
Interests: reinforced concrete; concrete; materials; structural design; building structures; reinforced concrete structures; earthquake engineering; computer modelling and analysis of structures; seismic design and assessment of reinforced concrete and masonry structures

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Guest Editor
Assistant Professor, Infrastructures Engineering Specialization, Department of Aeronautical Sciences, Hellenic Air Force Academy, Acharnes, 136 72 Athens, Greece
Interests: concrete structures; steel structures; stuctural analysis; non linear analysis; stability; buckling

Special Issue Information

Dear Colleagues,

Concrete, being the predominant cementitious composite material in construction, faces escalating demands on its performance within an increasingly complex application environment. The mechanical characteristics of concrete are predominantly influenced by its constituents, compounded by several factors including mix design ratio, the percentage weight content of each component, curing conditions, age of the concrete, and the incorporation of mineral additives and chemical admixtures. The relationship between these factors and mechanical properties, particularly compressive strength, exhibits a highly nonlinear nature, making their prediction based on mix design a subject of very practical importance. In recent years, the utilization of artificial intelligence methods has emerged as a contemporary research focal point, facilitating optimized mix design across various concrete types and the prediction of physical and mechanical properties.

Despite these advancements, challenges persist in developing novel concrete formulations that showcase innovative performance and enhanced durability against aging, external loads, and environmental factors. This Special Issue is designed to showcase the latest advances and developments in mechanical performance, durability, service life prediction, and modeling of optimized concrete mix designs, with a particular focus on meeting the demand for reduced carbon impact. Emphasis will also be placed on areas enhancing sustainability and fortifying the resilience of civil infrastructure.

Contributions to this Special Issue are welcomed in the form of original research articles and reviews, spanning a range of research areas, including, but not limited to,

  • Optimal design and development of new concrete formulations.
  • Algorithmic methods for predicting concrete properties and correlating them with mix design.
  • Modeling of concrete properties and their correlation with mix design.
  • Mix design strategies for low-carbon concretes, with predictions of mechanical and durability performance.
  • Advanced multiscale computational modeling and experimental characterization of innovative concrete types.
  • Development and application of novel nondestructive evaluation methods for concrete material characterization and performance prediction.
  • Composite structures and steel structure design.
  • Analysis methods for structures.
  • Dynamic behaviour of structural elements under blast/explosive loads.
  • Buckling strength estimation of steel structural elements.

We look forward to receiving your contributions.

Dr. Konstantinos G. Kolovos
Dr. Constantinos Repapis
Dr. Georgios Mageirou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Buildings is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • concrete
  • building materials
  • mix design
  • mechanical properties
  • low CO2 concretes
  • algorithmic methods
  • prediction of concrete properties
  • building
  • houses

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Published Papers (2 papers)

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22 pages, 4400 KiB  
Article
Optimized bp Neural Network Based on Improved Dung Beetle Optimization Algorithm to Predict High-Performance Concrete Compressive Strength
by Zhipeng Wang, Jie Cai, Xiaoxiao Liu and Zikang Zou
Buildings 2024, 14(11), 3465; https://doi.org/10.3390/buildings14113465 - 30 Oct 2024
Viewed by 141
Abstract
In modern architecture, the structural safety of buildings largely depends on the compressive strength of high-performance concrete (HPC), which is determined by the complex nonlinear relationships between its components. In order to more accurately forecast HPC’s compressive strength, this paper proposes a prediction [...] Read more.
In modern architecture, the structural safety of buildings largely depends on the compressive strength of high-performance concrete (HPC), which is determined by the complex nonlinear relationships between its components. In order to more accurately forecast HPC’s compressive strength, this paper proposes a prediction model based on an improved dung beetle optimization algorithm (OTDBO)-optimized backpropagation neural network (BPNN). Extreme Gradient Boosting (XGBoost) is employed to determine the inputs for the BPNN, enhancing the computational efficiency under high-dimensional data feature conditions. To address the issues of local optima entrapment and slow convergence in the dung beetle optimization algorithm (DBO), four improvements were made to enhance its performance. In the initial population generation stage, the optimal Latin hypercube method was used to increase the population diversity. In the rolling stage, the osprey optimization algorithm’s global exploration strategy was introduced to improve the global search capability. The variable spiral search strategy was employed in the reproduction stage, and an adaptive t-distribution perturbation strategy was combined in the foraging stage to enhance the algorithm’s adaptability and search efficiency. The improved dung beetle optimization algorithm (OTDBO) outperformed other algorithms in performance tests on the CEC2017 benchmark functions. In terms of predicting the compressive strength of HPC, the XG-OTDBO-BP model developed in this study outperformed models optimized by other algorithms in terms of fitting outcomes and prediction accuracy. These findings support the XG-OTDBO-BP model’s superiority in the compressive strength of HPC prediction. Full article
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21 pages, 6816 KiB  
Article
Study on the Effect of Interfacial Modification on the Properties of Super Standard Mica Sand Cement-Based Materials
by Huanqiang Liu, Xueqing Yang, Linhua Jiang, Keliang Li, Limei Wang and Weizhun Jin
Buildings 2024, 14(6), 1665; https://doi.org/10.3390/buildings14061665 - 5 Jun 2024
Viewed by 938
Abstract
Mica is a harmful substance in sand and occurs frequently. The application of super standard mica sand is a difficult problem in large-scale engineering. In this work, the effects of an interface modifier, mineral admixture, and a curing system on the properties of [...] Read more.
Mica is a harmful substance in sand and occurs frequently. The application of super standard mica sand is a difficult problem in large-scale engineering. In this work, the effects of an interface modifier, mineral admixture, and a curing system on the properties of cement-based materials with super standard mica sand were studied. The strength of cement-based materials linearly decreases with the mica content in sand. When the mica content in sand exceeds 6%, the compressive strength of mortar and concrete at 28 d decreases by more than 22.3% and 33.5%, respectively. By adding the silane coupling agent (SCA) of 50% mica mass and curing in natural conditions, the compressive strength of mortar increases by 10.9%. The cement-based materials with the SCA are more suitable for curing in natural conditions, and the performance of the SCA will not be affected by adding appropriate amounts of mineral admixture. The drying shrinkage strain of the concrete, with the sand containing high mica content modified by SCA, is reduced by 10.5%, and the diffusion of chloride ions in concrete is reduced. The XRD results show that the addition of the interfacial agent does not change the hydration products. The MIP and SEM results show that the SCA can form a bridge structure between the hydration products and the mica, improve the bonding strength of the interface zone, and reduce the number of harmful pores. Full article
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