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Emerging Construction Materials for Sustainable Infrastructure

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A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Construction and Building Materials".

Deadline for manuscript submissions: closed (10 May 2022) | Viewed by 19464

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Special Issue Editors

Prof. Dr. Jian-Guo Dai

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Guest Editor

Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Interests: fiber-reinforced polymer composites; fiber-reinforced cementitious composites; multiple functional coating; geopolymer concrete; durability and life cycle management of concrete infrastructure
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Dr. Mehran Khan

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Guest Editor

Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 999077, China
Interests: hybrid fiber reinforced concrete; multi-scale fiber reinforced composites; analytical modelling of hybrid fiber concrete; durability of fiber concrete; structural and fire engineering
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Prof. Dr. Majid Ali

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Guest Editor

Department of Civil Engineering, Capital University of Science and Technology, Islamabad, Pakistan
Interests: natural fiber reinforced concrete; fiber reinforced composites; construction material; waste fibers concrete; hybrid fiber reinforced cementitious composites, earthquake resistant buildings

Prof. Dr. Mingli Cao

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Guest Editor

Department of Civil Engineering, Dalian University of Technology, Dalian, China
Interests: high-performance fiber-reinforced cementitious composites; micro/nano-modified cement-based materials; multi-scale fiber reinforced cementitious composites; functional porous cement-based materials
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Special Issue Information

Dear Colleagues,

Concrete infrastructure is the basis of our society and plays an important role in supporting economic development. Sustainability of concrete infrastructure has become a critical issue worldwide due to the ever-increasing deterioration of concrete and steel materials under combined mechanical loading and environmental action. To carry out appropriate maintenance on the existing aging concrete structures for service life extension and to realize highly durable and maintenance-free new concrete structures, the research community has been focusing their continuous efforts on creating novel construction materials with specialized features.

The aim of this Special Issue is to publish up-to-date research contributions related to emerging civil engineering materials for sustainable concrete infrastructures, including advanced reinforcing materials and concrete composites. State-of-the-art material approaches used for the development of sustainable concrete infrastructure are particularly encouraged. Potential topics include but are not limited to the following:

Fiber-reinforced polymer (FRP) composites for construction applications;
Multiscale hybrid fiber-reinforced cementitious composites for sustainable infrastructures;
Geopolymer composites used as low carbon construction materials;
Ultra-high performance cementitious composites (UHPCC) for new structural elements;
High performance fiber-reinforced cementitious composites as repair/strengthening materials;
3D printing concrete materials and applications;
Ecofriendly cementitious composites with the use of waste materials;
Long-term durability of innovative reinforcing materials or cementitious composites;
High-performance cementitious composites under fire/elevated temperatures;
Advanced machine learning techniques for performance design and prediction of innovative construction materials.

Prof. Dr. Jian-Guo Dai
Dr. Mehran Khan
Prof. Dr. Majid Ali
Prof. Dr. Mingli Cao
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. Materials is an international peer-reviewed open access semimonthly 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

sustainable concrete infrastructure
hybrid fiber-reinforced cement-based materials
engineered cementitious composites
fiber-reinforced polymer
geopolymer
ultra-high performance cementitious composites
durability
microstructure
repair/strengthening
fire/elevated temperature
basalt fibers
3D printing
machine learning

Published Papers (7 papers)

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Research

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24 pages, 9134 KiB

Open AccessArticle

Use of Artificial Intelligence for Predicting Parameters of Sustainable Concrete and Raw Ingredient Effects and Interactions

by Muhammad Nasir Amin, Waqas Ahmad, Kaffayatullah Khan, Ayaz Ahmad, Sohaib Nazar and Anas Abdulalim Alabdullah

Materials 2022, 15(15), 5207; https://doi.org/10.3390/ma15155207 - 27 Jul 2022

Cited by 20 | Viewed by 2140

Abstract

Incorporating waste material, such as recycled coarse aggregate concrete (RCAC), into construction material can reduce environmental pollution. It is also well-known that the inferior properties of recycled aggregates (RAs), when incorporated into concrete, can impact its mechanical properties, and it is necessary to evaluate the optimal performance. Accordingly, artificial intelligence has been used recently to evaluate the performance of concrete compressive behaviour for different types of construction material. Therefore, supervised machine learning techniques, i.e., DT-XG Boost, DT-Gradient Boosting, SVM-Bagging, and SVM-Adaboost, are executed in the current study to predict RCAC’s compressive strength. Additionally, SHapley Additive exPlanations (SHAP) analysis shows the influence of input parameters on the compressive strength of RCAC and the interactions between them. The correlation coefficient (R²), root mean square error (RMSE), and mean absolute error (MAE) are used to assess the model’s performance. Subsequently, the k-fold cross-validation method is executed to validate the model’s performance. The R² value of 0.98 from DT-Gradient Boosting supersedes those of the other methods, i.e., DT- XG Boost, SVM-Bagging, and SVM-Adaboost. The DT-Gradient Boosting model, with a higher R² value and lower error (i.e., MAE, RMSE) values, had a better performance than the other ensemble techniques. The application of machine learning techniques for the prediction of concrete properties would consume fewer resources and take less time and effort for scholars in the respective engineering field. The forecasting of the proposed DT-Gradient Boosting models is in close agreement with the actual experimental results, as indicated by the assessment output showing the improved estimation of RCAC’s compressive strength. Full article

(This article belongs to the Special Issue Emerging Construction Materials for Sustainable Infrastructure)

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27 pages, 19015 KiB

Open AccessEditor’s ChoiceArticle

Predicting the Ultimate Axial Capacity of Uniaxially Loaded CFST Columns Using Multiphysics Artificial Intelligence

by Sangeen Khan, Mohsin Ali Khan, Adeel Zafar, Muhammad Faisal Javed, Fahid Aslam, Muhammad Ali Musarat and Nikolai Ivanovich Vatin

Materials 2022, 15(1), 39; https://doi.org/10.3390/ma15010039 - 22 Dec 2021

Cited by 30 | Viewed by 4108

Abstract

The object of this research is concrete-filled steel tubes (CFST). The article aimed to develop a prediction Multiphysics model for the circular CFST column by using the Artificial Neural Network (ANN), the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Gene Expression Program (GEP). The database for this study contains 1667 datapoints in which 702 are short CFST columns and 965 are long CFST columns. The input parameters are the geometric dimensions of the structural elements of the column and the mechanical properties of materials. The target parameters are the bearing capacity of columns, which determines their life cycle. A Multiphysics model was developed, and various statistical checks were applied using the three artificial intelligence techniques mentioned above. Parametric and sensitivity analyses were also performed on both short and long GEP models. The overall performance of the GEP model was better than the ANN and ANFIS models, and the prediction values of the GEP model were near actual values. The PI of the predicted N_st by GEP, ANN and ANFIS for training are 0.0416, 0.1423, and 0.1016, respectively, and for N_lg these values are 0.1169, 0.2990 and 0.1542, respectively. Corresponding OF values are 0.2300, 0.1200, and 0.090 for N_st, and 0.1000, 0.2700, and 0.1500 for N_lg. The superiority of the GEP method to the other techniques can be seen from the fact that the GEP technique provides suitable connections based on practical experimental work and does not rely on prior solutions. It is concluded that the GEP model can be used to predict the bearing capacity of circular CFST columns to avoid any laborious and time-consuming experimental work. It is also recommended that further research should be performed on the data to develop a prediction equation using other techniques such as Random Forest Regression and Multi Expression Program. Full article

(This article belongs to the Special Issue Emerging Construction Materials for Sustainable Infrastructure)

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19 pages, 4449 KiB

Open AccessArticle

Effect of Carbon Black and Hybrid Steel-Polypropylene Fiber on the Mechanical and Self-Sensing Characteristics of Concrete Considering Different Coarse Aggregates’ Sizes

by Shakeel Ahmed, Abasal Hussain, Zahoor Hussain, Zhang Pu, Krzysztof Adam Ostrowski and Rafał Walczak

Materials 2021, 14(23), 7455; https://doi.org/10.3390/ma14237455 - 04 Dec 2021

Cited by 14 | Viewed by 2683

Abstract

The effect of combining filler (carbon black) and fibrous materials (steel fiber and polypropylene fiber) with various sizes of coarse particles on the post-cracking behavior of conductive concrete was investigated in this study. Steel fibers (SF) and carbon black (CB) were added as monophasic, diphasic, and triphasic materials in the concrete to enhance the conductive properties of reinforced concrete. Polypropylene fiber (PP) was also added to steel fiber and carbon to improve the post-cracking behavior of concrete beams. This research mainly focused on the effects of macro fibers on toughness parameters and energy absorption capacity, as well as enhancing the self-sensing of multiple cracks and post-cracking behavior. Fractional changes in resistance and crack opening displacement (COD-FCR) and the relationship of load-deflection-FCR with different coarse aggregates of (5–10 mm and 15–20 mm) sizes were investigated, and the law of resistance signal changes with single and multiple cracking through load-time-FCR curves was explored. Results indicated that the smaller size coarse aggregates (5–10 mm) showed higher compressive strength: up to 8.3% and 14.83% with diphasic (SF + CB), respectively. The flexural strength of PC-10 increased 22.60 and 51.2%, respectively, with and without fibers, compared to PC-20. The diphasic and triphasic conductive material with the smaller size of aggregates (5–10 mm) increased the FCR values up to 38.95% and 42.21%, respectively, as compared to those of greater size coarse aggregates (15–20 mm). The hybrid uses of fibrous and filler materials improved post-cracking behavior as well as the self-sensing ability of reinforced concrete. Full article

(This article belongs to the Special Issue Emerging Construction Materials for Sustainable Infrastructure)

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16 pages, 8525 KiB

Open AccessArticle

Computation of High-Performance Concrete Compressive Strength Using Standalone and Ensembled Machine Learning Techniques

by Yue Xu, Waqas Ahmad, Ayaz Ahmad, Krzysztof Adam Ostrowski, Marta Dudek, Fahid Aslam and Panuwat Joyklad

Materials 2021, 14(22), 7034; https://doi.org/10.3390/ma14227034 - 19 Nov 2021

Cited by 44 | Viewed by 2109

Abstract

The current trend in modern research revolves around novel techniques that can predict the characteristics of materials without consuming time, effort, and experimental costs. The adaptation of machine learning techniques to compute the various properties of materials is gaining more attention. This study aims to use both standalone and ensemble machine learning techniques to forecast the 28-day compressive strength of high-performance concrete. One standalone technique (support vector regression (SVR)) and two ensemble techniques (AdaBoost and random forest) were applied for this purpose. To validate the performance of each technique, coefficient of determination (R²), statistical, and k-fold cross-validation checks were used. Additionally, the contribution of input parameters towards the prediction of results was determined by applying sensitivity analysis. It was proven that all the techniques employed showed improved performance in predicting the outcomes. The random forest model was the most accurate, with an R² value of 0.93, compared to the support vector regression and AdaBoost models, with R² values of 0.83 and 0.90, respectively. In addition, statistical and k-fold cross-validation checks validated the random forest model as the best performer based on lower error values. However, the prediction performance of the support vector regression and AdaBoost models was also within an acceptable range. This shows that novel machine learning techniques can be used to predict the mechanical properties of high-performance concrete. Full article

(This article belongs to the Special Issue Emerging Construction Materials for Sustainable Infrastructure)

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12 pages, 10384 KiB

Open AccessArticle

Coatings Based on Phosphate Cements for Fire Protection of Steel Structures

by Cristina Andreea Vijan, Alina Badanoiu, Georgeta Voicu and Adrian Ionut Nicoara

Materials 2021, 14(20), 6213; https://doi.org/10.3390/ma14206213 - 19 Oct 2021

Cited by 5 | Viewed by 1722

Abstract

Fire events in buildings can cause losses to human life and important material damage, therefore a great deal of attention is paid nowadays to fire prevention. Buildings based on steel structures are especially affected in the event of a fire, due to the important loss of load-bearing capability when steel is heated at temperatures higher than 500 °C. Therefore, one possible method to mitigate the deleterious effect of fire is to protect steel structures from direct heating by applying protective coatings. In this paper, the ability of magnesium phosphate cement (MPC), based on dead burned magnesite and calcium magnesium phosphate cement (CMPC) coatings, to protect a steel substrate was assessed. CMPCs were obtained by mixing partially calcined dolomite with a KH₂PO₄ (MKP) solution, and in some cases, with a setting retarder (borax). The main mineralogical compounds assessed by X-ray diffraction and electronic microscopy (SEM-EDS) in CMPC are MgO, CaCO₃, and K-struvite (KMgPO₄·6H₂O). The coatings based on MPC and CMPC, applied to steel plates, were tested in direct contact with a flame; the coatings of MPC and CMPC without the borax addition prevented the temperature increase of a metal substrate above 500 °C. No exfoliation of coatings (MPC and CMPC without borax addition) was noticed during the entire period of the test (45 min). Full article

(This article belongs to the Special Issue Emerging Construction Materials for Sustainable Infrastructure)

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17 pages, 5487 KiB

Open AccessEditor’s ChoiceArticle

Application of Advanced Machine Learning Approaches to Predict the Compressive Strength of Concrete Containing Supplementary Cementitious Materials

by Waqas Ahmad, Ayaz Ahmad, Krzysztof Adam Ostrowski, Fahid Aslam, Panuwat Joyklad and Paulina Zajdel

Materials 2021, 14(19), 5762; https://doi.org/10.3390/ma14195762 - 02 Oct 2021

Cited by 67 | Viewed by 2777

Abstract

The casting and testing specimens for determining the mechanical properties of concrete is a time-consuming activity. This study employed supervised machine learning techniques, bagging, AdaBoost, gene expression programming, and decision tree to estimate the compressive strength of concrete containing supplementary cementitious materials (fly ash and blast furnace slag). The performance of the models was compared and assessed using the coefficient of determination (R²), mean absolute error, mean square error, and root mean square error. The performance of the model was further validated using the k-fold cross-validation approach. Compared to the other employed approaches, the bagging model was more effective in predicting results, with an R² value of 0.92. A sensitivity analysis was also prepared to determine the level of contribution of each parameter utilized to run the models. The use of machine learning (ML) techniques to predict the mechanical properties of concrete will be beneficial to the field of civil engineering because it will save time, effort, and resources. The proposed techniques are efficient to forecast the strength properties of concrete containing supplementary cementitious materials (SCM) and pave the way towards the intelligent design of concrete elements and structures. Full article

(This article belongs to the Special Issue Emerging Construction Materials for Sustainable Infrastructure)

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Review

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28 pages, 4512 KiB

Open AccessReview

Experimental Investigation on the Strengthening of Reinforced Concrete Beams Using Externally Bonded and Near-Surface Mounted Natural Fibre Reinforced Polymer Composites—A Review

by John Uduak Effiong and Anthony Nkem Ede

Materials 2022, 15(17), 5848; https://doi.org/10.3390/ma15175848 - 25 Aug 2022

Cited by 11 | Viewed by 2524

Abstract

Developing more resilient and sustainable physical infrastructure increases the demand for sustainable materials and strengthening approaches. Many investigations into strengthening RC beam structures have used either externally bonded (EB) or near-surface mounted (NSM) systems with synthetic fibre reinforced polymer composites. These synthetic fibres are unsustainable since they involve the use of nonrenewable resources and a large amount of energy. Research shows that natural fibre reinforced polymer (NFRP) composites may be an alternative to synthetic FRP composites in the strengthening of concrete beams. However, there is limited literature that validates their performance in various structural applications. Hence, the purpose of this paper is to explore the advances, prospects, and gaps of using EB/NSM NFRP techniques in strengthening concrete beams to provide areas for future research directions. The NSM FRP technique provides improved strengthening effects and mitigates the concerns associated with the EB system, based on a wider range of applications using synthetic FRPs. However, the NSM NFRP strengthening technique has been underutilized, though the EB NFRP system has been more commonly explored in reviewed studies. The knowledge gaps and areas for proposed future research directions are essential in developing work in emerging NFRPs and strengthening techniques for sustainable infrastructure. Full article

(This article belongs to the Special Issue Emerging Construction Materials for Sustainable Infrastructure)

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