Towards Zero-Emission, Hazard-Resilient Buildings and Infrastructure: Advanced Material, Construction, and Energy Systems

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Materials, and Repair & Renovation".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 2480

Special Issue Editor


E-Mail Website
Guest Editor
College of Design, Construction and Planning, University of Florida, Gainesville, FL 32611, USA
Interests: AI in built environment; computational mechanics; uncertainty and risk quantification; construction management of the infrastructure system

Special Issue Information

Dear Colleagues,

In this era of rapid climate change and increasing environmental awareness, the construction and civil engineering sectors face unprecedented challenges. This Special Issue, "Towards Zero-Emission, Hazard-Resilient Buildings and Infrastructure: Advanced Material, Construction, and Energy Systems", delves deep into the forefront of sustainable and resilient design practices. As cities and societies grapple with the dual threats of climate change-induced hazards and the imperative to reduce carbon footprints, our built environment must evolve accordingly.

This issue explores, but is not limited to, the following central topics:

Zero-Emission Technologies: Investigating the latest breakthroughs in energy-efficient systems and renewable energy integrations for both buildings and broader infrastructure. We aim to showcase innovations that not only reduce operational emissions but also consider the embodied carbon throughout the lifecycle of structures.

Hazard-Resilience: In a world where extreme weather events are becoming more frequent, the designs of our buildings and infrastructure need a paradigm shift. This section highlights advanced modeling and simulation methods for accurate quantification of natural hazard impacts; cutting-edge design and construction methodologies that stand strong against natural disasters, ensuring longevity and safety.

Advanced Materials: A deep dive into novel construction materials that promise both sustainability and durability. Through rigorous experiments, simulations and data analyses, discover how the very fabric of our built environment is transforming.

Integrated Energy Systems: Exploring holistic approaches to energy use, from smart grids to efficient building management systems, that not only reduce emissions but also optimize consumption patterns in real time.

Advanced Construction Technologies: Unveiling the next frontier in the construction industry, this section delves into the integration of automation, robotics, and digital twins in the building process. From 3D-printed structures to AI-driven project management tools, learn how technological advancements are revolutionizing construction methodologies, increasing precision, reducing waste, and accelerating timelines.

We invite experts, researchers and practitioners from around the world to contribute their insights, research findings and visionary ideas. Together, we can pave the way for a future where our built environment is both a protector and a reflection of our commitment to the planet.

Dr. Chaofeng Wang
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 monthly 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

  • urban buildings
  • zero emission
  • hazard mitigation
  • building resilience
  • climate change
  • sustainable materials
  • building performance
  • AI
  • digital twin
  • IoT
  • zero energy
  • urban infrastructure
  • low-carbon buildings

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

18 pages, 37485 KiB  
Article
Toward Nearly Zero-Waste Architecture: Innovation, Application, and Practice of Construction Methods Using Natural Materials
by Shu-Chen Tsai, Xue-Fang Zhang and Yao-Tan Chang
Buildings 2024, 14(6), 1584; https://doi.org/10.3390/buildings14061584 - 30 May 2024
Viewed by 1028
Abstract
The goals of this study are to propose a method to minimize the waste of buildings’ exterior walls and to respond to practical technical reports on disaster resistance and reductions in resource extraction. This study’s scientific value is its testing of new nearly [...] Read more.
The goals of this study are to propose a method to minimize the waste of buildings’ exterior walls and to respond to practical technical reports on disaster resistance and reductions in resource extraction. This study’s scientific value is its testing of new nearly zero-waste materials and their construction methods for external walls. Four cases using a bamboo and pozzolana wall construction method between 2016 and 2021 in Southern Taiwan were examined. The results show that the materials can be decomposed on site to achieve the goal of nearly zero waste. Steel structures and exterior walls can provide toughness and breathability to resist earthquakes and hot and humid climates. Traditional construction techniques contain elements of technological change and can transcend outdated regulations. The exterior wall materials in this study can replace the local high-carbon-emitting mining industry and are a feasible way to actively respond to net-zero emissions. Full article
Show Figures

Figure 1

17 pages, 5678 KiB  
Article
Assessing Climate Disaster Vulnerability in Peru and Colombia Using Street View Imagery: A Pilot Study
by Chaofeng Wang, Sarah E. Antos, Jessica G. Gosling-Goldsmith, Luis M. Triveno, Chunwu Zhu, Jason von Meding and Xinyue Ye
Buildings 2024, 14(1), 14; https://doi.org/10.3390/buildings14010014 - 20 Dec 2023
Cited by 4 | Viewed by 1119
Abstract
Community and household vulnerability to natural hazards, e.g., earthquakes, hurricanes, and floods, is a concern that transcends geographic and economic boundaries. Despite the abundance of research in this field, most existing methods remain inefficient and face the challenge of data scarcity. By formulating [...] Read more.
Community and household vulnerability to natural hazards, e.g., earthquakes, hurricanes, and floods, is a concern that transcends geographic and economic boundaries. Despite the abundance of research in this field, most existing methods remain inefficient and face the challenge of data scarcity. By formulating and investigating the correlation between the household vulnerability and street view images of buildings, this research seeks to bridge the knowledge gap to enable an efficient assessment. Especially in developing countries, the widespread prevalence of outdated or inadequately enforced building codes poses a significant challenge. Consequently, a considerable portion of the housing stock in these regions fails to meet acceptable standards, rendering it highly vulnerable to natural hazards and climate-related events. Evaluating housing quality is crucial for informing public policies and private investments. However, current assessment methods are often time-consuming and costly. To address this issue, we propose the development of a rapid and reliable evaluation framework that is also cost-efficient. The framework employs a low-cost street view imagery procedure combined with deep learning to automatically extract building information to assist in identifying housing characteristics. We then test its potential for scalability and higher-level reliability. More importantly, we aim to quantify household vulnerability based on street view imagery. Household vulnerability is typically assessed through traditional means like surveys or census data; however, these sources can be costly and may not reflect the most current information. We have developed an index that effectively captures the most detailed data available at both the housing unit and household level. This index serves as a comprehensive representation, enabling us to evaluate the feasibility of utilizing our model’s predictions to estimate vulnerability conditions in specific areas while optimizing costs. Through latent class clustering and ANOVA analysis, we have discovered a strong correlation between the predictions derived from the images and the household vulnerability index. This correlation will potentially enable large-scale, cost-effective evaluation of household vulnerability using only street view images. Full article
Show Figures

Figure 1

Back to TopTop