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Resilient Infrastructure Planning: Enhancing Sustainability in Construction

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

Deadline for manuscript submissions: 15 September 2025 | Viewed by 920

Special Issue Editor


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Guest Editor
Department of Civil Engineering and Architecture, School of Engineering, Tallinn University of Technology, Ehitajate tee 5, 19086 Tallinn, Estonia
Interests: construction economics; construction management; maintenance of built environment; maintenance of construction entities; housing management; facilities management

Special Issue Information

Dear Colleagues,

When thinking about construction, we generally consider houses and/or buildings. Thus, when preparing textbooks and manuals, the introduction to construction begins with foundations. Then, there is the superstructure, and finally, the variety of finishing works that describe all the extremely specific aspects of technology and quality.

At the same time, when people buy and use homes or buildings, the most important thing for them is that they have access to amenities. Is water and sewage available, how are the electricity and heating systems arranged, and is accessibility to the building important? These everyday conveniences must be planned and built; the construction industry is responsible for that.

Infrastructure has become more of an umbrella term to describe the entire environment (often the support environment) needed to create a living, working, and recreational environment. Different authors of academic papers may have different interpretations of the term “infrastructure”, but the most general definition may be “the basic systems and services, such as transport and power supplies, that a country or organization uses in order to work effectively” (Cambridge Dictionary). The infrastructure does not draw a clear outline of either one or the other network or system, and currently, when highlighting the importance of the indoor climate of the buildings as a complex concept, it is necessary to balance both heating, electricity, water supply, and the functioning of the building's main structures. When the infrastructure that services the building is not resilient enough, dependable, and of poor quality, the building connected to this infrastructure is considered low quality, though the bearing structures may be solid.

The journal encourages submission of papers related to:

  • Sustainable and healthy infrastructure for the society;
  • Developing contemporary infrastructure;
  • Reconstruction of derelict infrastructure;
  • Contemporary methods of infrastructure management and maintenance;
  • Digital tools when managing infrastructure;
  • Influence of natural hazards on the normal functioning of infrastructure;
  • Green deal strategies for infrastructure.

Prof. Dr. Roode Liias
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. 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

  • urban and rural infrastructure
  • professional competence for infrastructure management
  • developing infrastructure networks
  • standards for regeneration of infrastructure
  • professionalism of servicing staff

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Published Papers (2 papers)

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Research

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24 pages, 12352 KiB  
Article
Predictive Models and GIS for Road Safety: Application to a Segment of the Chone–Flavio Alfaro Road
by Luis Alfonso Moreno-Ponce, Ana María Pérez-Zuriaga and Alfredo García
Sustainability 2025, 17(11), 5032; https://doi.org/10.3390/su17115032 - 30 May 2025
Viewed by 405
Abstract
The analysis of traffic crashes facilitates the identification of trends that can inform strategies to enhance road safety. This study aimed to detect high-risk zones and forecast collision patterns by integrating spatial analysis and predictive modeling. Traffic incidents along the Chone–Flavio Alfaro road [...] Read more.
The analysis of traffic crashes facilitates the identification of trends that can inform strategies to enhance road safety. This study aimed to detect high-risk zones and forecast collision patterns by integrating spatial analysis and predictive modeling. Traffic incidents along the Chone–Flavio Alfaro road segment in Manabí, Ecuador, were examined using Geographic Information Systems (GIS) and Kernel Density Estimation (KDE), based on official data from the National Traffic Agency (ANT) covering the period 2017–2023. Additionally, ARIMA, Prophet, and Long Short-Term Memory (LSTM) models were applied to predict crash occurrences. The most influential contributing factors were driver distraction, excessive speed, and adverse weather. Four main crash hotspots were identified: near Chone (PS 0–2.31), PS 2.31–7.10, PS 13.39–21.31, and PS 31.27–33.92, close to Flavio Alfaro. A total of 55 crashes were recorded, with side impacts (27.3%), pedestrian-related collisions (14.5%), and rear-end crashes (12.7%) being the most frequent types. The predictive models performed well, with Prophet achieving the highest estimated accuracy (90.8%), followed by LSTM (88.2%) and ARIMA (87.6%), based on MAE evaluations. These findings underscore the potential of intelligent transportation systems (ITSs) and predictive analytics to support proactive traffic management and resilient infrastructure development in rural regions. Full article
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Review

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17 pages, 583 KiB  
Review
Analyzing Patterns and Predictive Models of Energy and Water Consumption in Schools
by Hana Begić Juričić and Hrvoje Krstić
Sustainability 2025, 17(12), 5514; https://doi.org/10.3390/su17125514 - 15 Jun 2025
Viewed by 195
Abstract
Schools are major consumers of energy and water, significantly influencing environmental sustainability and operational budgets. This study presents a comprehensive review of global trends in energy and water consumption in school buildings, identifying key factors that shape usage patterns, such as the geographic [...] Read more.
Schools are major consumers of energy and water, significantly influencing environmental sustainability and operational budgets. This study presents a comprehensive review of global trends in energy and water consumption in school buildings, identifying key factors that shape usage patterns, such as the geographic location, climate, building characteristics, and occupancy levels. A particular focus is placed on the role of predictive models in enhancing resource efficiency. The review found that energy consumption in schools varies widely, with heating, lighting, and cooling systems being the primary contributors. In contrast, research on water consumption—especially predictive modeling—is notably scarce, with no studies found that focused specifically on school buildings. This highlights a critical gap in the literature. This study evaluated the existing predictive approaches, including regression analyses, machine learning algorithms, and statistical models, which offer valuable tools for forecasting consumption and guiding targeted efficiency interventions. The findings underscore the urgent need for data-driven strategies to support sustainable resource management in educational facilities. Full article
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