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Innovative Concrete Composite: Materials and Structures with Sustainability-Centred Design
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Innovative Concrete Composite: Materials and Structures with Sustainability-Centred Design

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 (30 November 2022) | Viewed by 13910

Special Issue Editor

Dr. Vanissorn Vimonsatit

E-Mail Website
Guest Editor
School of Engineering, Faculty of Science and Engineering, Macquarie University, Macquarie Park, NSW 2109, Australia
Interests: AI for building design; fibre-reinforced composite materials; BIM; buildability; construction materials; modular structures; multiscaling and sustainability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The global use of concrete is continually increasing, with demand for more innovative materials that satisfactorily deliver economic, environmental and social benefits. This demand presents engineering challenges in developing sustainable materials and environmentally focussed assets. Topics of interest presenting emerging trends in cement and concrete technology include the development of low-carbon cementitious binders, high-performance concrete, multiscaling characterisation, sustainability-centred design, the incorporation of recycled materials, logistics, life-cycle cost, performance enhancements, design guidelines, AI-based design, and other related fields.

While concrete research has been fast-paced and intensive, benefits can only be seen when there is a wide spread of industry applications. Transferring research to practice requires the collective efforts of researchers, practitioners and regulators in bringing ideas from concepts to implementation. One pressing issue is for all to work towards getting regulatory provisions for standards of practice.

This Special Issue provides a platform to promote the widespread implementation of innovative concrete materials and structures. Authors can showcase their novel concepts, proofs of ideas, and recommendations for design and construction that will lead to the global goal of sustainability outcomes. The theme is vast and open, covering all aspects of science and engineering technology for the whole life cycle of innovative concrete materials and structures, including characterisation, design, construction, performance, durability, safety, maintenance, demolition and waste recycling.

Dr. Vanissorn Vimonsatit
Guest Editor

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

  • cementitious materials
  • recycled materials
  • low-carbon concrete
  • geopolymer
  • fibre reinforcement
  • multiscale modelling
  • upscaling approach
  • design guideline
  • chemical and mechanical properties
  • time-dependent properties
  • sustainability
  • life cycle analysis
  • AI-based design
  • multicriterial optimisation

Published Papers (6 papers)

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Research

22 pages, 7954 KiB  
Article
Mechanical Properties and Absorption of High-Strength Fiber-Reinforced Concrete (HSFRC) with Sustainable Natural Fibers
by Muttaqin Hasan, Taufiq Saidi, Muhammad Jamil, Zahra Amalia and Azzaki Mubarak
Buildings 2022, 12(12), 2262; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings12122262 - 19 Dec 2022
Cited by 5 | Viewed by 2622
Abstract
This study aimed to determine the mechanical properties and absorption of high-strength fiber-reinforced concrete (HSFRC), using sustainable natural fibers. In this analysis, two types of fibers were used, namely, ramie and abaca. Two different HSFRC mixtures were also designed, where one composition [...] Read more.
This study aimed to determine the mechanical properties and absorption of high-strength fiber-reinforced concrete (HSFRC), using sustainable natural fibers. In this analysis, two types of fibers were used, namely, ramie and abaca. Two different HSFRC mixtures were also designed, where one composition emphasized ordinary Portland cement (OPC) as a binder, and the other prioritizing calcined diatomaceous earth (CDE) as a mineral additive to replace 10% weight of OPC. Furthermore, ramie and abaca fibers were separately added to the mixtures at three different volumetric contents. Based on the results, the addition of these fibers in the concrete mixtures improved the mechanical properties of HSFRC. The improvements of compressive strength, splitting tensile strength, and flexural strength, due to the addition of ramie fiber were 18%, 17.3%, and 31.8%, respectively, while those for the addition of abaca fiber were 11.8%, 17.2%, and 38.1%, respectively. This indicated that the fibers were capable of being used as alternative materials for sustainable concrete production. The effects of ramie and abaca fibers on the absorption of HSFRC were also not significant, and their presence for the same amount of superplasticizer reduced the flow speed of fresh reinforced concrete mixtures. Full article
(This article belongs to the Special Issue Innovative Concrete Composite: Materials and Structures with Sustainability-Centred Design)
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26 pages, 8452 KiB  
Article
Combined Gaussian Local Search and Enhanced Comprehensive Learning PSO Algorithm for Size and Shape Optimization of Truss Structures
by Thu Huynh Van, Sawekchai Tangaramvong, Soviphou Muong and Phuc Tran Van
Buildings 2022, 12(11), 1976; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings12111976 - 14 Nov 2022
Cited by 4 | Viewed by 1652
Abstract
This paper proposes the use of enhanced comprehensive learning particle swarm optimization (ECLPSO), combined with a Gaussian local search (GLS) technique, for the simultaneous optimal size and shape design of truss structures under applied forces and design constraints. The ECLPSO approach presents two [...] Read more.
This paper proposes the use of enhanced comprehensive learning particle swarm optimization (ECLPSO), combined with a Gaussian local search (GLS) technique, for the simultaneous optimal size and shape design of truss structures under applied forces and design constraints. The ECLPSO approach presents two novel enhancing techniques, namely perturbation-based exploitation and adaptive learning probability, in addition to its distinctive diversity of particles. This prevents the premature convergence of local optimal solutions. In essence, the perturbation enables the robust exploitation in the updating velocity of particles, whilst the learning probabilities are dynamically adjusted by ranking information on the personal best particles. Based on the results given by ECLPSO, the GLS technique takes data from the global best particle and personal best particles in the last iteration to generate samples from a Gaussian distribution to improve convergence precision. A combination of these techniques results in the fast convergence and likelihood to obtain the optimal solution. Applications of the combined GLS-ECLPSO method are illustrated through several successfully solved truss examples in two- and three-dimensional spaces. The robustness and accuracy of the proposed scheme are illustrated through comparisons with available benchmarks processed by other meta-heuristic algorithms. All examples show simultaneous optimal size and shape distributions of truss structures complying with limit state design specifications. Full article
(This article belongs to the Special Issue Innovative Concrete Composite: Materials and Structures with Sustainability-Centred Design)
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19 pages, 3804 KiB  
Article
Nonlinear Response of RC Columns Subjected to Equal Energy-Double Impact Loads
by Warakorn Tantrapongsaton, Chayanon Hansapinyo, Suchart Limkatanyu, Hexin Zhang and Vanissorn Vimonsatit
Buildings 2022, 12(9), 1420; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings12091420 - 10 Sep 2022
Cited by 2 | Viewed by 1263
Abstract
Defining the damage and deflection from impact by using only the impact energy could be misleading due to the effect of impact momentum. In addition, reinforced concrete columns might be subjected to repeated impact loading. Hence, this study presents the numerical simulation of [...] Read more.
Defining the damage and deflection from impact by using only the impact energy could be misleading due to the effect of impact momentum. In addition, reinforced concrete columns might be subjected to repeated impact loading. Hence, this study presents the numerical simulation of 16 RC columns with identical sizing and reinforcement details, subjected to equal energy-double impact loadings using a free-falling mass at midspan. The impact energy was kept constant for both impacts. For each analysis, the impact momentum was varied by varying the velocity and mass of the impactor. The axial load ratios of the columns are between 0.0 to 0.3 of the compressive strength of the concrete cross-section. The results clearly addressed the momentum effect on the impact responses. The momentum level affected the specimens’ damage behavior under the same input impact energy. A high momentum impact yielded more global flexural damage with large deflection, and a low momentum impact produced more local damage with a slight deflection. The axial load helps maintain the impact resistance capacity. However, the failure determined by the flexural damage pattern under the first impact was changed when subjected to the second impact to the shear mode with the presence of axial load. Further, the colliding index considering the momentum was used in the deflection prediction equation. The proposed equation improved the deflection calculation accuracy of reinforced concrete beams under equal energy but different momentum impact. Full article
(This article belongs to the Special Issue Innovative Concrete Composite: Materials and Structures with Sustainability-Centred Design)
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28 pages, 9840 KiB  
Article
Development of an Ultra-High-Performance Fibre-Reinforced Concrete (UHPFRC) Manufacturable at Ambient Temperature
by Koji Tamataki, Tomoaki Ito, Yutaka Fujino and Isamu Yoshitake
Buildings 2022, 12(6), 740; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings12060740 - 30 May 2022
Cited by 4 | Viewed by 1468
Abstract
Ultra-high-performance fibre-reinforced concrete (UHPFRC) manufacturing typically requires heat curing. UHPFRC production at a ready-mixed concrete (RMC) plant is often difficult because specific equipment is required for heat curing. Concerns associated with ultra-high-performance concrete (UHPC) construction include the energy costs and environmental impacts of [...] Read more.
Ultra-high-performance fibre-reinforced concrete (UHPFRC) manufacturing typically requires heat curing. UHPFRC production at a ready-mixed concrete (RMC) plant is often difficult because specific equipment is required for heat curing. Concerns associated with ultra-high-performance concrete (UHPC) construction include the energy costs and environmental impacts of the heat curing and transportation from the factory to the construction site. Few studies have been conducted on the manufacturing of UHPFRC under standard curing conditions. The strength properties of UHPFRC manufactured under standard curing are typically poorer than those of UHPFRC manufactured under heat curing. The materials and mixture proportions required for the UHPFRC manufacturable under ambient temperature conditions were investigated. Five types of cement and four types of powder materials were tested, as well as the fine aggregate needed to achieve proper fluidity. This paper reports that the cement having low C3A and high C3S is suitable for the UHPFRC manufacturable at ambient temperatures; the allowable volume of fine aggregate was 600 kg/m3 for the UHPFRC having a proper dispersion of steel fibres; the highest water-binder ratio (W/B) of 21% was found for the UHPFRC cured under ambient temperature. Full article
(This article belongs to the Special Issue Innovative Concrete Composite: Materials and Structures with Sustainability-Centred Design)
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23 pages, 2980 KiB  
Article
Shear Strength Prediction of Slender Steel Fiber Reinforced Concrete Beams Using a Gradient Boosting Regression Tree Method
by Amjed Shatnawi, Hana Mahmood Alkassar, Nadia Moneem Al-Abdaly, Emadaldeen A. Al-Hamdany, Luís Filipe Almeida Bernardo and Hamza Imran
Buildings 2022, 12(5), 550; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings12050550 - 25 Apr 2022
Cited by 13 | Viewed by 2062
Abstract
For the design or assessment of concrete structures that incorporate steel fiber in their elements, the accurate prediction of the shear strength of steel fiber reinforced concrete (SFRC) beams is critical. Unfortunately, traditional empirical methods are based on a small and limited dataset, [...] Read more.
For the design or assessment of concrete structures that incorporate steel fiber in their elements, the accurate prediction of the shear strength of steel fiber reinforced concrete (SFRC) beams is critical. Unfortunately, traditional empirical methods are based on a small and limited dataset, and their abilities to accurately estimate the shear strength of SFRC beams are arguable. This drawback can be reduced by developing an accurate machine learning based model. The problem with using a high accuracy machine learning (ML) model is its interpretation since it works as a black-box model that is highly sophisticated for humans to comprehend directly. For this reason, Shapley additive explanations (SHAP), one of the methods used to open a black-box machine learning model, is combined with highly accurate machine learning techniques to build an explainable ML model to predict the shear strength of SFRC slender beams. For this, a database of 330 beams with varying design attributes and geometries was developed. The new gradient boosting regression tree (GBRT) machine learning model was compared statistically to experimental data and current shear design models to evaluate its performance. The proposed GBRT model gives predictions that are very similar to the experimentally observed shear strength and has a better and unbiased predictive performance in comparison to other existing developed models. The SHAP approach shows that the beam width and effective depth are the most important factors, followed by the concrete strength and the longitudinal reinforcement ratio. In addition, the outputs are also affected by the steel fiber factor and the shear-span to effective depth ratio. The fiber tensile strength and the aggregate size have the lowest effect, with only about 1% on average to change the predicted value of the shear strength. By building an accurate ML model and by opening its black-box, future researchers can focus on some attributes rather than others. Full article
(This article belongs to the Special Issue Innovative Concrete Composite: Materials and Structures with Sustainability-Centred Design)
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12 pages, 4911 KiB  
Article
Compressive Strength Prediction of High-Strength Concrete Using Long Short-Term Memory and Machine Learning Algorithms
by Honggen Chen, Xin Li, Yanqi Wu, Le Zuo, Mengjie Lu and Yisong Zhou
Buildings 2022, 12(3), 302; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings12030302 - 4 Mar 2022
Cited by 43 | Viewed by 4063
Abstract
Compressive strength is an important mechanical property of high-strength concrete (HSC), but testing methods are usually uneconomical, time-consuming, and labor-intensive. To this end, in this paper, a long short-term memory (LSTM) model was proposed to predict the HSC compressive strength using 324 data [...] Read more.
Compressive strength is an important mechanical property of high-strength concrete (HSC), but testing methods are usually uneconomical, time-consuming, and labor-intensive. To this end, in this paper, a long short-term memory (LSTM) model was proposed to predict the HSC compressive strength using 324 data sets with five input independent variables, namely water, cement, fine aggregate, coarse aggregate, and superplasticizer. The prediction results were compared with those of the conventional support vector regression (SVR) model using four metrics, root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and correlation coefficient (R2). The results showed that the prediction accuracy and reliability of LSTM were higher with R2 = 0.997, RMSE = 0.508, MAE = 0.08, and MAPE = 0.653 compared to the evaluation metrics R2 = 0.973, RMSE = 1.595, MAE = 0.312, MAPE = 2.469 of the SVR model. The LSTM model is recommended for the pre-estimation of HSC compressive strength under a given mix ratio before the laboratory compression test. Additionally, the Shapley additive explanations (SHAP)-based approach was performed to analyze the relative importance and contribution of the input variables to the output compressive strength. Full article
(This article belongs to the Special Issue Innovative Concrete Composite: Materials and Structures with Sustainability-Centred Design)
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