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Application of Emerging Techniques and Sustainable Quality Management in the Architectural, Engineering, Construction (AEC) and Facilities Management Industries

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Green Building".

Deadline for manuscript submissions: closed (31 March 2026) | Viewed by 5984

Special Issue Editor

Dr. Natalija Lepkova

E-Mail Website
Guest Editor
Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Vilnius, Lithuania
Interests: facilities management; BIM applications in facilities management; quality management systems; real estate management; new technologies in facilities management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The numerous benefits offered by green buildings have led to an increase in their construction. In parallel, various artificial intelligence (AI) and emerging techniques have been recognized as relevant for these types of buildings to rationalize, optimize, and innovate their operation (Rodríguez-Gracia et al., 2023). Technology is the key to successful projects that advance green building. Various green building technologies with temperature reduction, wastewater systems, energy efficiency, photovoltaic-powered cooling systems, etc., can be adopted in green building projects (Patil et al., 2022).

This Special Issue aims to collate new ideas of research into green buildings and the application of emerging techniques and sustainable quality management in the architectural, engineering, construction (AEC), and facilities management industries. Also, this Special Issue aims to provide an advanced forum for studies related to green buildings and emerging techniques.

In this Special Issue, original research articles and reviews are welcome.

Research areas may include (but are not limited to) the following:

  • Green buildings and application of emerging techniques;
  • Sustainable quality management;
  • Emerging techniques and sustainable quality management in the architectural, engineering, construction (AEC), and facilities management industries;
  • Sustainability in architectural, engineering, construction (AEC), and facilities management industries;
  • Green construction;
  • Green buildings and facilities management.

I look forward to receiving your contributions.

Dr. Natalija Lepkova
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 250 words) can be sent to the Editorial Office for assessment.

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

  • green buildings
  • sustainable quality management
  • architecture
  • engineering
  • construction
  • facilities management
  • emerging techniques
  • BIM
  • artificial intelligence

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

Published Papers (3 papers)

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Research

22 pages, 3205 KB  
Article
Context-Responsive Building Footprint Generation via Conditional Inpainting Using Latent Diffusion Models
by Eunseok Jang and Kyunghwan Kim
Sustainability 2026, 18(8), 3987; https://doi.org/10.3390/su18083987 - 17 Apr 2026
Viewed by 242
Abstract
Generative AI has advanced rapidly in architectural design; however, existing building footprint generation models tend to emphasize stylistic exploration while insufficiently integrating site context as a fundamental physical constraint that facilitates alignment with the surrounding urban fabric. To address this limitation, this study [...] Read more.
Generative AI has advanced rapidly in architectural design; however, existing building footprint generation models tend to emphasize stylistic exploration while insufficiently integrating site context as a fundamental physical constraint that facilitates alignment with the surrounding urban fabric. To address this limitation, this study proposes a context-responsive methodology for generating building footprints using a multi-layered four-channel representation of site conditions—including roads, sidewalks, adjacent buildings, and site boundaries—within a Latent Diffusion Model framework. The proposed approach encodes these physical conditions into a structured tensor and concatenates them directly to the U-Net input, enabling site context to function as an explicit spatial control variable during generation. An ablation study evaluated the effectiveness of the proposed contextual configuration. Compared with a single-channel model, the four-channel model achieved an 18.08% reduction in average pixel-wise information entropy, indicating a measurable decrease in generative uncertainty. Qualitative analyses further demonstrated that the enriched contextual input promotes geometrically coherent footprint configurations, such as context-responsive setbacks and spatial alignment with surrounding built forms. These findings suggest that structured multi-channel site information enhances contextual grounding in generative design processes and may contribute to more environmentally integrated and spatially coherent architectural outcomes. Full article
(This article belongs to the Special Issue Application of Emerging Techniques and Sustainable Quality Management in the Architectural, Engineering, Construction (AEC) and Facilities Management Industries)
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38 pages, 2437 KB  
Article
A Stochastic Multi-Objective Model for Optimal Design of Electronic Waste Reverse Supply Chain
by Abbas Al-Refaie, Aya Shabaneh and Natalija Lepkova
Sustainability 2025, 17(23), 10693; https://doi.org/10.3390/su172310693 - 28 Nov 2025
Viewed by 1075
Abstract
The consumption of electronic products is growing rapidly, resulting in considerable amounts of electronic waste (e-waste). In addition, economic, environmental, and social perspectives increased the need to develop an effective reverse supply chain (RSC). This study, therefore, formulates a stochastic model for a [...] Read more.
The consumption of electronic products is growing rapidly, resulting in considerable amounts of electronic waste (e-waste). In addition, economic, environmental, and social perspectives increased the need to develop an effective reverse supply chain (RSC). This study, therefore, formulates a stochastic model for a multi-objective, multi-product, multi-period RSC for electronic waste (e-waste) under uncertainty in returns’ quantity, quality, and availability to repair. Three objective functions are considered: maximizing profit, maximizing social impact, and minimizing CO2 emissions. The end-of-life (EOL) household appliance firm was considered for illustration. Results showed that selling products’ parts and generating 123.025 tons of raw materials are expected to generate profit and revenue averages of USD 547,750 and USD 220,207, respectively. The multiple-product RSC is expected to increase profit by 2.3 times that of a single-product RSC. Finally, the effects of uncertainty in model parameters on the objective functions are examined. In conclusion, the proposed RSC of e-waste can effectively enhance sustainability. Full article
(This article belongs to the Special Issue Application of Emerging Techniques and Sustainable Quality Management in the Architectural, Engineering, Construction (AEC) and Facilities Management Industries)
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19 pages, 3317 KB  
Article
Development of a Cost Prediction Model for Design Changes: Case of Korean Apartment Housing Projects
by Ie-Sle Ahn, Jae-Jun Kim and Joo-Sung Lee
Sustainability 2024, 16(11), 4322; https://doi.org/10.3390/su16114322 - 21 May 2024
Cited by 5 | Viewed by 3762
Abstract
Apartment buildings are significantly popular among South Korean construction companies. However, design changes present a common yet challenging aspect, often leading to cost overruns. Traditional cost prediction methods, which primarily rely on numerical data, have a gap in fully capitalizing on the rich [...] Read more.
Apartment buildings are significantly popular among South Korean construction companies. However, design changes present a common yet challenging aspect, often leading to cost overruns. Traditional cost prediction methods, which primarily rely on numerical data, have a gap in fully capitalizing on the rich insights that textual descriptions of design changes offer. Addressing this gap, this research employs machine learning (ML) and natural language processing (NLP) techniques, analyzing a dataset of 35,194 instances of design changes from 517 projects by a major public real estate developer. The proposed models demonstrate acceptable performance, with R-square values ranging from 0.930 to 0.985, underscoring the potential of integrating structured and unstructured data for enhanced predictive analytics in construction project management. The predictor using Extreme Gradient Boosting (XGB) shows better predictive ability (R2 = 0.930; MAE = 16.05; RMSE = 75.09) compared to the traditional Multilinear Regression (MLR) model (R2 = 0.585; MAE = 43.85; RMSE = 101.41). For whole project cost changes predictions, the proposed models exhibit good predictive ability, both including price fluctuations (R2 = 0.985; MAE = 605.1; RMSE = 1009.5) and excluding price fluctuations (R2 = 0.982; MAE = 302.1; RMSE = 548.5). Additionally, a stacked model combining CatBoost and Support Vector Machine (SVM) algorithms was developed, showcasing the effective prediction of cost changes, with or without price fluctuations. Full article
(This article belongs to the Special Issue Application of Emerging Techniques and Sustainable Quality Management in the Architectural, Engineering, Construction (AEC) and Facilities Management Industries)
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