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Reinforced Concrete Structures, Seismic Reliability and Sustainability

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (25 March 2023) | Viewed by 10619

Special Issue Editors

Department of Engineering, University of Palermo, 90128 Palermo, Italy
Interests: sustainable materials; plastic and plastic waste; strategies of waste disposal by concrete; sustainable constructions; durability of construction for sustainability; structural health monitoring
Faculty for the Built Environment, University of Malta, Msida, Malta
Interests: sustainability and resilience; reinforced concrete structures; durability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to introduce this Special Issue on the reliability and sustainability of reinforced concrete structures.

Traditionally, engineering education aims to establish quantifiable, measurable units, and after that compare those to utilize the unit considered more suitable. When thinking about the sustainability of structures, traditional mentality has to be set aside, as comparing different structural systems becomes a complex task.

Rarely in practical application is it considered that reinforced concrete structures use a large quantity of energy to produce steel rebar and concrete. This has a negative impact on the environment. For example, the cement industry accounts for 8% of the world’s production of carbon dioxide, with a significant effect on climate change.

In spite of reinforced concrete structures not being generally the most sustainable solutions from an environmental point of view, reinforced concrete is preferred for most situations due to the other advantages that it presents. One of this is its optimal behavior in middle–high-risk seismic areas thanks to an obtainable ductility–strength combination.

Considering that reinforced concrete structures are indispensable for society, the aim of obtaining sustainable buildings becomes equivalent to decreasing their negative impact on the environment while still taking full advantage of their seismic capacity. This target can be achieved through judicious choice of the built-in materials.

Therefore, in this Special Issue, each of you is invited to propose solutions which are able to integrate sustainability and seismic reliability in the design of reinforced concrete structures and/or strategies for intervention on existing structures aiming to increase seismic reliability while avoiding the higher impact of reconstruction after demolition. Some of the topics to be addressed are covered in the keywords below, but please note the list is far from exhaustive.

Prof. Dr. Liborio Cavaleri
Prof. Dr. Ruben P. Borg
Prof. Dr. Panagiotis G. Asteris
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. Sustainability 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 2400 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

  • structural sustainability
  • reinforced concrete sustainability
  • economy and sustainability
  • life-cycle analysis
  • material quality impact
  • seismic behavior of sustainable concrete structures
  • case studies
  • improvement or retrofitting of existing structures

Published Papers (5 papers)

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Research

20 pages, 4178 KiB  
Article
Investigating the Effect of Parameters on Confinement Coefficient of Reinforced Concrete Using Development of Learning Machine Models
by Gege Cheng, Sai Hin Lai, Ahmad Safuan A. Rashid, Dmitrii Vladimirovich Ulrikh and Bin Wang
Sustainability 2023, 15(1), 199; https://0-doi-org.brum.beds.ac.uk/10.3390/su15010199 - 22 Dec 2022
Viewed by 1792
Abstract
The current research aims to investigate the parameters’ effect on the confinement coefficient, Ks, forecast using machine learning. Because various parameters affect the Ks, a new computational model has been developed to investigate this issue. Six parameters are among [...] Read more.
The current research aims to investigate the parameters’ effect on the confinement coefficient, Ks, forecast using machine learning. Because various parameters affect the Ks, a new computational model has been developed to investigate this issue. Six parameters are among the effective parameters based on previous research. Therefore, according to the dimensions of the variables in the problem, a supply–demand-based optimization (SDO) model was developed. The performance of this model is directly dependent on its main parameters, such as market size and iteration. Then, to compare the performance of the SDO model, classical models, including particle swarm size (PSO), imperialism competitive algorithm (ICA), and genetic algorithm (GA), were used. Finally, the best-developed model used different parameters to check the uncertainty obtained. For the test results, the new SDO-ANFIS model was able to obtain values of 0.9449 and 0.134 for the coefficient of determination (R2), and root mean square error (RMSE), which performed better than other models. Due to the different relationships between the parameters, different designed conditions were considered and developed based on the hybrid model and, finally, the number of longitudinal bars and diameter of lateral ties were obtained as the strongest and weakest parameters based on the developed model for this study. Full article
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21 pages, 5117 KiB  
Article
An Optimized Clustering Approach to Investigate the Main Features in Predicting the Punching Shear Capacity of Steel Fiber-Reinforced Concrete
by Shaojie Zhang, Mahdi Hasanipanah, Biao He, Ahmad Safuan A. Rashid, Dmitrii Vladimirovich Ulrikh and Qiancheng Fang
Sustainability 2022, 14(19), 12950; https://0-doi-org.brum.beds.ac.uk/10.3390/su141912950 - 10 Oct 2022
Cited by 2 | Viewed by 1611
Abstract
We developed an optimized system for solving engineering problems according to the characteristics of data. Because data analysis includes different variations, the use of common features can increase the performance and accuracy of models. Therefore, this study, using a combination of optimization techniques [...] Read more.
We developed an optimized system for solving engineering problems according to the characteristics of data. Because data analysis includes different variations, the use of common features can increase the performance and accuracy of models. Therefore, this study, using a combination of optimization techniques (K-means algorithm) and prediction techniques, offers a new system and procedure that can identify and analyze data with similarity and close grouping. The system developed using the new sparrow search algorithm (SSA) has been updated as a new hybrid solution to optimize development engineering problems. The data for proposing the mentioned techniques were collected from a series of laboratory works on samples of steel fiber-reinforced concrete (SFRC). To investigate the issue, the data were first divided into different clusters, taking into account common features. After introducing the top clusters, each cluster was developed using three predictive models, i.e., multi-layer perceptron (MLP), support vector regression (SVR), and tree-based techniques. This process continues until the criteria are met. Accordingly, the K-means–artificial neural network 3 structure shows the best performance in terms of accuracy and error. The results also showed that the structure of hybrid models with cluster numbers 2, 3, and 4 is higher than the baseline models in terms of accuracy for assessing the punching shear capacity (PSC) of SFRC. The K-means–ANN3-SSA generated a new methodology for optimizing PSC. The new proposed model/procedure can be used for a similar situation by combining clustering and prediction methods. Full article
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15 pages, 5006 KiB  
Article
Hybrid Wavelet Scattering Network-Based Model for Failure Identification of Reinforced Concrete Members
by Mohammad Sadegh Barkhordari, Mohammad Mahdi Barkhordari, Danial Jahed Armaghani, Ahmad Safuan A. Rashid and Dmitrii Vladimirovich Ulrikh
Sustainability 2022, 14(19), 12041; https://0-doi-org.brum.beds.ac.uk/10.3390/su141912041 - 23 Sep 2022
Cited by 7 | Viewed by 1384
Abstract
After earthquakes, qualified inspectors typically conduct a semisystematic information gathering, physical inspection, and visual examination of the nation’s public facilities, buildings, and structures. Manual examinations, however, take a lot of time and frequently demand too much work. In addition, there are not enough [...] Read more.
After earthquakes, qualified inspectors typically conduct a semisystematic information gathering, physical inspection, and visual examination of the nation’s public facilities, buildings, and structures. Manual examinations, however, take a lot of time and frequently demand too much work. In addition, there are not enough professionals qualified to assess such structural damage. As a result, in this paper, the efficiency of computer-vision hybrid models was investigated for automatically detecting damage to reinforced concrete elements. Data-driven hybrid models are generated by combining wavelet scattering network (WSN) with bagged trees (BT), random subspace ensembles (RSE), artificial neural networks (ANN), and quadratic support vector machines (SVM), named “BT-WSN”, “RSE-WSN”, “ANN-WSN”, and “SVM-WSN”. The hybrid models were trained on an image database containing 4585 images. In total, 15% of images with different sorts of damage were used to test the trained models’ robustness and adaptability; these images were not utilized in the training or validation phase. The WSN-SVM algorithm performed best in classifying the damage. It had the highest accuracy of the hybrid models, with a value of 99.1% in the testing phase. Full article
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20 pages, 8182 KiB  
Article
Flexural Strength of Concrete Beams Made of Recycled Aggregates: An Experimental and Soft Computing-Based Study
by Ehsan Momeni, Fereydoon Omidinasab, Ahmad Dalvand, Vahid Goodarzimehr and Abas Eskandari
Sustainability 2022, 14(18), 11769; https://0-doi-org.brum.beds.ac.uk/10.3390/su141811769 - 19 Sep 2022
Cited by 9 | Viewed by 1846
Abstract
The implementation of recycled concrete aggregates (RCAs) in the construction industry has been highlighted in the literature recently. This study aimed to propose an intelligent model for predicting the ultimate flexural strength of recycled reinforced concrete (RRC) beams. For this reason, a database [...] Read more.
The implementation of recycled concrete aggregates (RCAs) in the construction industry has been highlighted in the literature recently. This study aimed to propose an intelligent model for predicting the ultimate flexural strength of recycled reinforced concrete (RRC) beams. For this reason, a database comprising experimental tests on concrete beams was compiled from the literature. Additionally, two experimental tests were performed in the laboratory to enhance the aforementioned database. The flexural test results showed a 10% reduction in flexural strength when the RRC beam was tested instead of a conventional beam (constructed with natural aggregates). Nevertheless, an artificial neural network (ANN) improved by particle swarm optimization (PSO), as well as an imperialist competitive algorithm (ICA), were utilized for developing the predictive model. The inputs of the hybrid predictive models of flexural strength were the beam geometrical properties, reinforcement ratio, RCA percentage, compressive strength of concrete, and the yield strength of steel. The overall findings (e.g., correlation coefficient values of 0.997 and 0.994 for the testing data) showed the feasibility of the PSO-based ANN predictive model, as well as the ICA-based ANN predictive model in the flexural assessment of RRC beams. Furthermore, comparing the prediction performances of PSO-based ANN with ICA-based ANN and the conventional ANN showed that the PSO-based ANN model outperformed the predictive model built with the conventional ANN and the ICA-ANN. Full article
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15 pages, 3302 KiB  
Article
Strategies for Waste Recycling: The Mechanical Performance of Concrete Based on Limestone and Plastic Waste
by Marco Filippo Ferrotto, Panagiotis G. Asteris, Ruben Paul Borg and Liborio Cavaleri
Sustainability 2022, 14(3), 1706; https://0-doi-org.brum.beds.ac.uk/10.3390/su14031706 - 01 Feb 2022
Cited by 13 | Viewed by 2490
Abstract
Recycling is among the best management strategies to avoid dispersion of several types of wastes in the environment. Research in recycling strategies is gaining increased importance in view of Circular Economy principles. The exploitation of waste, or byproducts, as alternative aggregate in concrete, [...] Read more.
Recycling is among the best management strategies to avoid dispersion of several types of wastes in the environment. Research in recycling strategies is gaining increased importance in view of Circular Economy principles. The exploitation of waste, or byproducts, as alternative aggregate in concrete, results in a reduction in the exploitation of scarce natural resources. On the other hand, a productive use of waste leads to a reduction in the landfilling of waste material through the transformation of waste into a resource. In this frame of reference, the paper discusses how to use concrete as a container of waste focusing on the waste produced in limestone quarries and taking the challenge of introducing plastic waste into ordinary concrete mixes. To prove the possibility of reaching this objective with acceptable loss of performance, the mechanical characteristics of concrete mixed with additional alternative aggregates classified as waste are investigated and discussed in this paper through the presentation of two experimental campaigns. The first experimental investigation refers to concrete made with fine limestone waste used as a replacement for fine aggregate (sand), while the second experimental program refers to the inclusion of three types of plastic wastes in the concrete. Different mixes with different percentages of wastes are investigated to identify possible fields of application. The experimental results indicate that use of limestone quarry waste and use of plastic waste are possible within significant percentage ranges, having recognized a limited reduction of concrete strength that makes concrete itself appropriate for different practical applications. Full article
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