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Buildings
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Application of Eco-Efficient Composites in Construction Engineering
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Application of Eco-Efficient Composites in Construction Engineering

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 4679

Special Issue Editor

Dr. Afshin Marani

E-Mail Website
Guest Editor
Department of Civil Engineering, McMaster University, Hamilton, ON, Canada
Interests: thermal energy storage; phase change material; ultra-low-carbon cement-based composites; machine learning; synthetic data generation; net-zero operational and embodied carbon; circular economy

Special Issue Information

Dear Colleagues,

Buildings are a major source of operational and embodied anthropogenic global CO2 emissions while the construction industry is responsible for extensive natural resources depletion and waste production. Therefore, performing comprehensive research to promote energy-efficient buildings, eco-friendly construction materials, and recycling material and energy within a circular economy approach is of great significance. This Special Issue invites robust and novel research studies on carbon-neutral building materials, building materials with high recycled content, utilization of by-products and waste materials in construction, eco-efficient technologies in building engineering, additive manufacturing in building engineering, artificial intelligence, IoT technologies for sustainable and resilient buildings, and related innovative research centered on the sustainability of building materials. Experimental, analytical, and numerical models with clear novelty and contribution to the state of the art will be considered. Redundant studies that report on issues already covered in the open literature will not be considered.

Dr. Afshin Marani
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 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

  • resilient buildings
  • net-zero buildings
  • circular economy
  • recycled materials
  • waste valorization
  • energy-efficient buildings
  • artificial intelligence

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

Published Papers (2 papers)

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Research

17 pages, 4637 KiB  
Article
Machine Learning Method Based on Symbiotic Organism Search Algorithm for Thermal Load Prediction in Buildings
by Fatemeh Nejati, Wahidullah Omer Zoy, Nayer Tahoori, Pardayev Abdunabi Xalikovich, Mohammad Amin Sharifian and Moncef L. Nehdi
Buildings 2023, 13(3), 727; https://doi.org/10.3390/buildings13030727 - 9 Mar 2023
Cited by 8 | Viewed by 1968
Abstract
This research investigates the efficacy of a proposed novel machine learning tool for the optimal simulation of building thermal load. By applying a symbiotic organism search (SOS) metaheuristic algorithm to a well-known model, namely an artificial neural network (ANN), a sophisticated optimizable methodology [...] Read more.
This research investigates the efficacy of a proposed novel machine learning tool for the optimal simulation of building thermal load. By applying a symbiotic organism search (SOS) metaheuristic algorithm to a well-known model, namely an artificial neural network (ANN), a sophisticated optimizable methodology is developed for estimating heating load (HL) in residential buildings. Moreover, the SOS is comparatively assessed with several identical optimizers, namely political optimizer, heap-based optimizer, Henry gas solubility optimization, atom search optimization, stochastic fractal search, and cuttlefish optimization algorithm. The dataset used for this study lists the HL versus the corresponding building conditions and the model tries to disclose the nonlinear relationship between them. For each mode, an extensive trial and error effort revealed the most suitable configuration. Examining the accuracy of prediction showed that the SOS–ANN hybrid is a strong predictor as its results are in great harmony with expectations. Moreover, to verify the results of the SOS–ANN, it was compared with several benchmark models employed in this study, as well as in the earlier literature. This comparison revealed the superior accuracy of the suggested model. Hence, utilizing the SOS–ANN is highly recommended to energy-building experts for attaining an early estimation of the HL from a designed building’s characteristics. Full article
(This article belongs to the Special Issue Application of Eco-Efficient Composites in Construction Engineering)
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13 pages, 9361 KiB  
Article
New Fuzzy-Heuristic Methodology for Analyzing Compression Load Capacity of Composite Columns
by Bizhan Karimi Sharafshadeh, Mohammad Javad Ketabdari, Farhood Azarsina, Mohammad Amiri and Moncef L. Nehdi
Buildings 2023, 13(1), 125; https://doi.org/10.3390/buildings13010125 - 3 Jan 2023
Cited by 3 | Viewed by 2193
Abstract
Predicting the mechanical strength of structural elements is a crucial task for the efficient design of buildings. Considering the shortcomings of experimental and empirical approaches, there is growing interest in using artificial intelligence techniques to develop data-driven tools for this purpose. In this [...] Read more.
Predicting the mechanical strength of structural elements is a crucial task for the efficient design of buildings. Considering the shortcomings of experimental and empirical approaches, there is growing interest in using artificial intelligence techniques to develop data-driven tools for this purpose. In this research, empowered machine learning was employed to analyze the axial compression capacity (CC) of circular concrete-filled steel tube (CCFST) composite columns. Accordingly, the adaptive neuro-fuzzy inference system (ANFIS) was trained using four metaheuristic techniques, namely earthworm algorithm (EWA), particle swarm optimization (PSO), salp swarm algorithm (SSA), and teaching learning-based optimization (TLBO). The models were first applied to capture the relationship between the CC and column characteristics. Subsequently, they were requested to predict the CC for new column conditions. According to the results of both phases, all four models could achieve dependable accuracy. However, the PSO-ANFIS was tangibly more efficient than the other models in terms of computational time and accuracy and could attain more accurate predictions for extreme conditions. This model could predict the CC with a relative error below 2% and a correlation exceeding 99%. The PSO-ANFIS is therefore recommended as an effective tool for practical applications in analyzing the behavior of the CCFST columns. Full article
(This article belongs to the Special Issue Application of Eco-Efficient Composites in Construction Engineering)
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