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Buildings
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Advanced Research on Building Materials Performance
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Advanced Research on Building Materials Performance

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 (31 August 2023) | Viewed by 11834

Special Issue Editors

Dr. Muhammad Faisal Javed

E-Mail Website1 Website2
Guest Editor
Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan
Interests: sustainable construction; machine learning; green concrete; geopolymer concrete; nanomaterials in concrete
Dr. Arslan Akbar

E-Mail Website
Guest Editor
Department of Architecture and Civil Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong
Interests: sustainable cementitious materials; engineered cement composites; alkali-activated materials; environmental impact assessment; machine learning and artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Furqan Farooq

E-Mail Website
Assistant Guest Editor
NUST Institute of Civil Engineering (NICE), School of Civil and Environmental Engineering (SCEE), National University of Sciences and Technology (NUST), Sector H-12, Islamabad 44000, Pakistan
Interests: geopolymer concrete; machine learning in cementitious materials; self-compacting concrete; intrusion of nanomaterials and fibres in concrete; sustainable and green materials in the civil engineering domain

Special Issue Information

Dear Colleagues,

Our civilization is founded on building infrastructure, which plays a vital role in fostering economic growth. Scientific innovations in improving the performance of building materials have gained significant importance. Therefore, academic and industry researchers need to devote their research and development efforts to discovering how the performance of these materials can be fully realized. With the advancement of experimental techniques and analytical methods, the performance of advanced building materials and structures has been thoroughly studied from the microscale to the macroscale. Similarly, the latest advances in machine learning and artificial intelligence have enabled this technology to predict the performance of building materials in a more adaptable, efficient, and effective manner. Thus, this Special Issue aims to promote and disseminate the latest research on the performance of building materials.

This Special Issue is dedicated to advanced research on mechanical, thermal, and environmental performances, including the multifunctional properties of sustainable building materials and structures. Topics of interest include (but are not limited to):

  • Experiments on building materials and structures;
  • Mechanical, thermal, and environmental performance;
  • Numerical simulations of material properties;
  • Application of machine learning and artificial intelligence.

Dr. Muhammad Faisal Javed
Dr. Arslan Akbar
Guest Editors

Furqan Farooq
Assistant 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

  • advanced sustainable materials and structures
  • material characterization
  • mechanical, thermal, and environmental performance
  • experiments
  • microstructural analysis
  • numerical simulations
  • lifecycle assessment
  • circular economy of sustainable building materials and infrastructure
  • machine learning and artificial intelligence

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

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Research

29 pages, 7576 KiB  
Article
Investigating the Integrity and Failure Characteristics of Deteriorated Polymeric 3D-Printed Components as Candidates for Structural and Construction Applications
by Waleed Ahmed, Essam Zaneldin and Amged El Hassan
Buildings 2023, 13(10), 2402; https://doi.org/10.3390/buildings13102402 - 22 Sep 2023
Cited by 1 | Viewed by 1987
Abstract
This investigation aimed to comprehensively investigate the integrity and failure characteristics of deteriorated polymeric components produced through Fused Filament Fabrication (FFF) technology. The primary focus was to examine the performance of flawed 3D-printed samples, which were purposely designed and 3D-printed to incorporate a [...] Read more.
This investigation aimed to comprehensively investigate the integrity and failure characteristics of deteriorated polymeric components produced through Fused Filament Fabrication (FFF) technology. The primary focus was to examine the performance of flawed 3D-printed samples, which were purposely designed and 3D-printed to incorporate a range of crack types and geometric features that were initially designed through CAD. This study adopted two main approaches to deal with the cracks by producing the flaws through design and laser processes. These specimens were subjected to destructive testing to gain valuable insights into the FFF-printed components’ performance and failure characteristics under the tensile mode, a significant concern in engineering applications. A Finite Element Analysis (FEA) was employed on the flawed and intact specimens to compare and correlate the experimental results with the simulation results. This study reveals the tested samples’ structural response and failure mechanisms under tensile loading conditions. Exceptionally, it was found that the faulty 3D-printed parts made by the laser process demonstrated less resistance to failure due to disturbing the 3D-printed extruded filament streams. In contrast, the flaws initially produced solely by the 3D printing process showed better resistance to mechanical failure due to the crack-bridging effect. It was observed that there were reductions of 11% and 32% in the failure load of the 3D-printed cracked sample and the laser-cracked samples, respectively, in comparison with the intact one. Additionally, the stress intensity factor showed a decrease of 20% in the laser-cracked sample compared to the 3D-printed one. Full article
(This article belongs to the Special Issue Advanced Research on Building Materials Performance)
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17 pages, 7235 KiB  
Article
Compressive Strength Prediction of BFRC Based on a Novel Hybrid Machine Learning Model
by Jiayan Zheng, Tianchen Yao, Jianhong Yue, Minghui Wang and Shuangchen Xia
Buildings 2023, 13(8), 1934; https://doi.org/10.3390/buildings13081934 - 29 Jul 2023
Cited by 1 | Viewed by 1492
Abstract
Basalt fiber-reinforced concrete (BFRC) represents a form of high-performance concrete. In structural design, a 28-day resting period is required to achieve compressive strength. This study extended an extreme gradient boosting tree (XGBoost) hybrid model by incorporating genetic algorithm (GA) optimization, named GA-XGBoost, for [...] Read more.
Basalt fiber-reinforced concrete (BFRC) represents a form of high-performance concrete. In structural design, a 28-day resting period is required to achieve compressive strength. This study extended an extreme gradient boosting tree (XGBoost) hybrid model by incorporating genetic algorithm (GA) optimization, named GA-XGBoost, for the projection of compressive strength (CS) on BFRC. GA optimization may reduce many debugging efforts and provide optimal parameter combinations for machine learning (ML) algorithms. The XGBoost is a powerful integrated learning algorithm with efficient, accurate, and scalable features. First, we created and provided a common dataset using test data on BFRC strength from the literature. We segmented and scaled this dataset to enhance the robustness of the ML model. Second, to better predict and evaluate the CS of BFRC, we simultaneously used five other regression models: XGBoost, random forest (RF), gradient-boosted decision tree (GBDT) regressor, AdaBoost, and support vector regression (SVR). The analysis results of test sets indicated that the correlation coefficient and mean absolute error were 0.9483 and 2.0564, respectively, when using the GA-XGBoost model. The GA-XGBoost model demonstrated superior performance, while the AdaBoost model exhibited the poorest performance. In addition, we verified the accuracy and feasibility of the GA-XGBoost model through SHAP analysis. The findings indicated that the water–binder ratio (W/B), fine aggregate (FA), and water–cement ratio (W/C) in BFRC were the variables that had the greatest effect on CS, while silica fume (SF) had the least effect on CS. The results demonstrated that GA-XGBoost exhibits exceptional accuracy in predicting the CS of BFRC, which offers a valuable reference for the engineering domain. Full article
(This article belongs to the Special Issue Advanced Research on Building Materials Performance)
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10 pages, 1963 KiB  
Article
Influence of Defoamer on the Properties and Pore Structure of Cementitious Grout for Rebar Sleeve Splicing
by Chunhua Huang, Bo Ding, Zhihua Ou and Ruiping Feng
Buildings 2023, 13(1), 170; https://doi.org/10.3390/buildings13010170 - 9 Jan 2023
Viewed by 1696
Abstract
Grout sleeve splicing of rebar is a major technology in prefabricated buildings, and cementitious grout for rebar sleeve splicing (hereinafter called grout) is an essential material for this technology. Grout, with its excellent mechanical properties, improves the stability of rebar sleeve splicing. In [...] Read more.
Grout sleeve splicing of rebar is a major technology in prefabricated buildings, and cementitious grout for rebar sleeve splicing (hereinafter called grout) is an essential material for this technology. Grout, with its excellent mechanical properties, improves the stability of rebar sleeve splicing. In this study, the mechanical properties of grout were improved by introducing an admixture in the form of a defoamer, and the influence of the defoamer on the fluidity, loss rate of fluidity, wet apparent density and strength of the grout was investigated. The action of the defoamer in regulating the pore structure of the grout was further analyzed using the mercury intrusion porosimetry (MIP) method. The results showed that when the dosage of defoamer was increased from 0 to 0.025%, the fluidity of the grout initially increased and then decreased, but there was no change in the loss rate at 30 min. The wet apparent density increased continuously, whereas the flexural and compressive strength generally increased initially and then tended to stabilize. The MIP test results showed that the defoamer increased the pore volume of the grout in the range of 6 nm to 30 nm and decreased the pore volume in the range of 30 nm to 60 μm. However, in the 60 μm to 300 μm pore size range, the pore volume increased when the dosage of the defoamer was 0.0025% and 0.0075%, and decreased when the dosage was 0.005% and 0.001%. The porosity of the grout initially decreased and then increased slightly as the dosage of the defoamer increased from 0 to 0.01%. The introduction of defoamer can optimize the pore structure of grout and then improve its mechanical properties. The influence of defoamer on grout properties and pore structure were systemically studied with a view to providing technical and theoretical guidance for rebar sleeve-splicing technology in prefabricated construction. Full article
(This article belongs to the Special Issue Advanced Research on Building Materials Performance)
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16 pages, 2313 KiB  
Article
Development of the New Prediction Models for the Compressive Strength of Nanomodified Concrete Using Novel Machine Learning Techniques
by Sohaib Nazar, Jian Yang, Waqas Ahmad, Muhammad Faisal Javed, Hisham Alabduljabbar and Ahmed Farouk Deifalla
Buildings 2022, 12(12), 2160; https://doi.org/10.3390/buildings12122160 - 7 Dec 2022
Cited by 27 | Viewed by 2638
Abstract
Concrete is a heterogeneous material that is extensively used as a construction material. However, to improve the toughness and mechanical properties of concrete, various ingredients (fillers) have been added in the past. The addition of nanomaterials for the improvement of the aforementioned properties [...] Read more.
Concrete is a heterogeneous material that is extensively used as a construction material. However, to improve the toughness and mechanical properties of concrete, various ingredients (fillers) have been added in the past. The addition of nanomaterials for the improvement of the aforementioned properties has attracted many researchers worldwide. The high surface area, high reactivity, and finer size of various nanomaterials have made them preferable for the enhancement of durability, as well as compressive and flexural strength. The aim of the current research is focused on the estimation of compressive strength for the concrete modified with various nanomaterials using two machine learning techniques, namely decision tree technique (DTT) and random forest technique (RFT), and comparison with existing models. The database is collected for different percentages of four major widely used nanomaterials in concrete, i.e., carbon nanotubes, nano silica, nano clay, and nano alumina. The other four input variables used for the calibration of the models are: cement content (CC); water–cement ratio (W/C); fine aggregate, i.e., sand (FA); and coarse aggregate (CA). Both DTT and RFT models were developed for 94 collected experimental datasets from the published literature. The predicted results are further validated through K-fold cross-validation using correlation coefficient (R2), mean absolute error (MAE), root mean square error (RMSE), relative root mean square error, relative square error (RRMSE), and performance index factor (PiF). The RFT model was found to have the lowermost MAE 3.253, RMSE 4.387, RRMSE 0.0803, and performance index factor (PiF) 0.0061. In comparison, predicted results overall revealed better performance and accuracy for the RFT-developed models than for DTT and gene expression programming (GEP) models, as illustrated by their high R2 value, equal to 0.96, while the R2 value for DTT and GEP was found 0.94 and 0.86, respectively. Full article
(This article belongs to the Special Issue Advanced Research on Building Materials Performance)
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16 pages, 12350 KiB  
Article
Fracture Behavior of Headed Studs: Ductile Fracture of Cold Heading Steel ML15
by Yixing Ding and Yanmin Jia
Buildings 2022, 12(12), 2128; https://doi.org/10.3390/buildings12122128 - 4 Dec 2022
Cited by 1 | Viewed by 1636
Abstract
In this paper, the fracture behavior of ML15 cold heading steel was investigated based on the ductile fracture theory. Smooth and notched bar, flat grooved and tensile–shear mixed flat plate specimens were designed, and tensile tests were carried out to examine the fracture [...] Read more.
In this paper, the fracture behavior of ML15 cold heading steel was investigated based on the ductile fracture theory. Smooth and notched bar, flat grooved and tensile–shear mixed flat plate specimens were designed, and tensile tests were carried out to examine the fracture mechanism of ML15. The micromechanical characteristics of the fracture surfaces of different specimens were studied by performing scanning electron microscopy. The results showed that the specimens under different stress states showed different micro fracture morphologies. The Rice–Tracey model and Bai–Wierzbicki model were calibrated using test results. Based on the calibrated fracture locus, a finite element model is developed and compared to the test results, which confirms the feasibility of the calibrated fracture locus for metal failure analysis. Full article
(This article belongs to the Special Issue Advanced Research on Building Materials Performance)
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17 pages, 1781 KiB  
Article
Comparison Study of the Heating Energy Demand for a Multi-Storey Residential Building in Romania Using Steady-State and Dynamic Methods
by Simon Pescari, Valeriu Augustin Stoian, Mircea Merea and Alexandru Pitroaca
Buildings 2022, 12(8), 1246; https://doi.org/10.3390/buildings12081246 - 15 Aug 2022
Cited by 4 | Viewed by 1577
Abstract
The purpose of this study is to determine the differences between the steady-state energetic method and the dynamic energetic method in a multi-storey residential building in Romania. For both methods, there are two values for the heating energy demand, one obtained with the [...] Read more.
The purpose of this study is to determine the differences between the steady-state energetic method and the dynamic energetic method in a multi-storey residential building in Romania. For both methods, there are two values for the heating energy demand, one obtained with the theoretical U value and g value according to Romanian Methodology Mc 001/1-2006 and one with the real U value and g value obtained from in situ measurements. The results of our study revealed a difference between the steady-state method and the dynamic method in both cases of approximately 20%. Because the heating energy demand needs to decrease in value according to European legislation and the classical energy demand determination is shallow, as it does not take into account some important factors, it is important to use a method that produces accurate values so the economic factor does not become overwhelming. Full article
(This article belongs to the Special Issue Advanced Research on Building Materials Performance)
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