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High Performance Concrete and Concrete Structure
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High Performance Concrete and Concrete Structure

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

Deadline for manuscript submissions: closed (20 September 2022) | Viewed by 6936

Special Issue Editor

Prof. Maciej Niedostatkiewicz

E-Mail Website
Guest Editor
Department of Concrete Structure, Faculty of Civil and Environmental Engineering, Gdansk University of Technology, 80-233 Gdańsk, Poland
Interests: concrete-properties and design; structural health monitoring (SHM); non-destructive testing (NDT); diagnostics and strengthening of concrete and masonery structures; strengthening and securing the structures of historic buildings; use of machine learning technique (MLT) in structural diagnostics and concrete design

Special Issue Information

Dear Colleagues

This special edition of Materials aims to present the latest trends in broadly understood concrete construction testing.

Particular emphasis is placed on experimental research and numerical calculations, the results of which will allow for the target optimization, both in terms of financial and environmental aspects, of designing real structural systems.

The aim of the planned issue of the journal is to present the latest results of research conducted both in academic centers and in research centers of construction corporations related to the subject of concrete structures.

The thematic scope, however, is not limited to experimental research only, publications containing the results of advanced numerical analyzes on the safety of concrete structures and modern methods of their design are also expected.

The main topic of the issue are:

  • concrete: properties and design,
  • monitoring the technical condition of building structures (SHM), in particular reinforced concrete,
  • non-destructive testing of structural systems (NDT), with particular emphasis on reinforced concrete elements,
  • diagnostics and strengthening of concrete, reinforced concrete, prestressed and masonry structures,
  • application of the machine learning technique (MLT) in diagnostics of building structures and concrete design.

Prof. Maciej Niedostatkiewicz
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. 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

  • concrete
  • reinforced concrete
  • prestressed construction
  • masonry construction
  • diagnostic tests
  • numerical simulations
  • nondestructive testing (NDT)
  • machine learning (ML)

Published Papers (4 papers)

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Research

15 pages, 5938 KiB  
Article
Study on the Performance and Mechanisms of High-Performance Foamed Concrete
by Guodong Xian, Zhe Liu, Zhen Wang and Xuejun Zhou
Materials 2022, 15(22), 7894; https://doi.org/10.3390/ma15227894 - 08 Nov 2022
Cited by 8 | Viewed by 1139
Abstract
As a common building insulation material, foamed concrete has been widely used in engineering practice. However, the contradiction between compressive strength and thermal conductivity has become the main problem limiting the development and application of foamed concrete. Therefore, high-performance foam concrete (HPFC) with [...] Read more.
As a common building insulation material, foamed concrete has been widely used in engineering practice. However, the contradiction between compressive strength and thermal conductivity has become the main problem limiting the development and application of foamed concrete. Therefore, high-performance foam concrete (HPFC) with high compressive strength and low thermal conductivity was prepared by using graphene oxide (GO), fly ash, and polypropylene (PP) fiber as the main admixtures, and taking compressive strength, thermal conductivity, and microstructure as the main indices. Scanning electron microscopy, X-ray diffraction (XRD), and thermogravimetry–differential scanning calorimetry (TG-DSC) were employed to examine the mechanisms of HPFC. The results showed that when the content of fly ash was 25–35 wt%, PP fiber was 0.2–0.4 wt%, and GO was 0.02–0.03 wt%, the FC’s compressive strength increased by up to 38%, and its thermal conductivity reduced by up to 3.4%. Fly ash improved the FC’s performance mainly through filling, pozzolanic activity, and slurry fluidity. PP fiber enhanced the performance of FC mainly through bridging cracks and skeletal effects. The addition of GO had no significant impact on the type, quantity, or hydration reaction rate of the hydration products in these cement-based materials, and mainly improved the FC’s microstructural compactness through template action and crack resistance, thereby improving its performance. Full article
(This article belongs to the Special Issue High Performance Concrete and Concrete Structure)
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24 pages, 3229 KiB  
Article
Fatigue Performance Prediction of RC Beams Based on Optimized Machine Learning Technology
by Li Song, Lian Wang, Hongshuo Sun, Chenxing Cui and Zhiwu Yu
Materials 2022, 15(18), 6349; https://doi.org/10.3390/ma15186349 - 13 Sep 2022
Cited by 2 | Viewed by 1112
Abstract
The development of fatigue damage in reinforced concrete (RC) beams is affected by various factors such as repetitive loads and material properties, and there exists a complex nonlinear mapping relationship between their fatigue performance and each factor. To this end, a fatigue performance [...] Read more.
The development of fatigue damage in reinforced concrete (RC) beams is affected by various factors such as repetitive loads and material properties, and there exists a complex nonlinear mapping relationship between their fatigue performance and each factor. To this end, a fatigue performance prediction model for RC beams was proposed based on the deep belief network (DBN) optimized by particle swarm optimization (PSO). The original database of fatigue loading tests was established by conducting fatigue loading tests on RC beams. The mid-span deflection, reinforcement strain, and concrete strain during fatigue loading of RC beams were predicted and evaluated. The fatigue performance prediction results of the RC beam based on the PSO-DBN model were compared with those of the single DBN model and the BP model. The models were evaluated using the R2 coefficient, mean absolute percentage error, mean absolute error, and root mean square error. The results showed that the fatigue performance prediction model of RC beams based on PSO-DBN is more accurate and efficient. Full article
(This article belongs to the Special Issue High Performance Concrete and Concrete Structure)
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20 pages, 5262 KiB  
Article
A Comparison of Machine Learning Tools That Model the Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete
by Jesús de-Prado-Gil, Covadonga Palencia, P. Jagadesh and Rebeca Martínez-García
Materials 2022, 15(12), 4164; https://doi.org/10.3390/ma15124164 - 12 Jun 2022
Cited by 18 | Viewed by 1809
Abstract
Several types of research currently use machine learning (ML) methods to estimate the mechanical characteristics of concrete. This study aimed to compare the capacities of four ML methods: eXtreme gradient boosting (XG Boost), gradient boosting (GB), Cat boosting (CB), and extra trees regressor [...] Read more.
Several types of research currently use machine learning (ML) methods to estimate the mechanical characteristics of concrete. This study aimed to compare the capacities of four ML methods: eXtreme gradient boosting (XG Boost), gradient boosting (GB), Cat boosting (CB), and extra trees regressor (ETR), to predict the splitting tensile strength of 28-day-old self-compacting concrete (SCC) made from recycled aggregates (RA), using data obtained from the literature. A database of 381 samples from literature published in scientific journals was used to develop the models. The samples were randomly divided into three sets: training, validation, and test, with each having 267 (70%), 57 (15%), and 57 (15%) samples, respectively. The coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) metrics were used to evaluate the models. For the training data set, the results showed that all four models could predict the splitting tensile strength of SCC made with RA because the R2 values for each model had significance higher than 0.75. XG Boost was the model with the best performance, showing the highest R2 value of R2 = 0.8423, as well as the lowest values of RMSE (=0.0581) and MAE (=0.0443), when compared with the GB, CB, and ETR models. Therefore, XG Boost was considered the best model for predicting the splitting tensile strength of 28-day-old SCC made with RA. Sensitivity analysis revealed that the variable contributing the most to the split tensile strength of this material after 28 days was cement. Full article
(This article belongs to the Special Issue High Performance Concrete and Concrete Structure)
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Graphical abstract

17 pages, 5254 KiB  
Article
Properties of Old Concrete Built in the Former Leipziger Palace
by Andrzej Ambroziak and Elżbieta Haustein
Materials 2022, 15(2), 673; https://doi.org/10.3390/ma15020673 - 17 Jan 2022
Cited by 7 | Viewed by 1941
Abstract
This research aims to determine the mechanical, chemical, and physical properties of old concrete used in the former Leipziger Palace in Wrocław, Poland. The cylindrical specimens were taken from the basement concrete walls using a concrete core borehole diamond drill machine. The determination [...] Read more.
This research aims to determine the mechanical, chemical, and physical properties of old concrete used in the former Leipziger Palace in Wrocław, Poland. The cylindrical specimens were taken from the basement concrete walls using a concrete core borehole diamond drill machine. The determination of the durability and strength of old concrete was based on specified chosen properties of the old concrete obtained through the following set of tests: measurements of dry density, tests of water absorption, specification of concrete compressive strength and frost resistance, determination of the modulus of elasticity, measurement of the pH value, determination of water-soluble chloride salts and sulphate ions, and X-ray diffraction analyses. Large dispersions of the compressive strength (10.4 MPa to 34.2 MPa), density (2049 kg/m3 to 2205 kg/m3), water absorption (4.72% to 6.55%), and stabilized secant modulus of elasticity (15.25 Gpa to 19.96 GPa) were observed. The paper is intended to provide scientists, civil engineers, and designers with guidelines for examining and assessing the long-term durability of old concrete, and also extending knowledge in the field of archaeological restoration and the protection of old concrete structures. Full article
(This article belongs to the Special Issue High Performance Concrete and Concrete Structure)
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