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When Digital Meets Traditional: How Does the Built Environment Head toward Sustainability?

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 5536

Special Issue Editors

Dr. Xiang Xie

E-Mail Website
Guest Editor
School of Engineering, Newcastle University, Newcastle upon Tyne, UK
Interests: digital twin; building information modelling; smart city
Special Issues, Collections and Topics in MDPI journals
Dr. Long Chen

E-Mail Website
Guest Editor
School of Architecture, Building and Civil Engineering, Loughborough University, Epinal Way, Loughborough LE11 3TU, UK
Interests: common data environments from BIM to digital twin; digital twin for construction sites and O&M management; construction schedule monitoring and prediction (for complex projects); digital sustainability and resilience
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The fourth industrial revolution has transformed whole industries at an exponential pace, implying changes in manufacturing, transportation, agriculture, and many other sectors. Leveraging information and communications technologies (ICT), digital transformation erases the borders between the physical and the digital world, supporting better-informed decision-making. However, based on the McKinsey Global Institute's Industry Digitalization Index, as the second least digitised sector, the digital void plaguing the construction industry is still hard to penetrate. It is due to multifold reasons, such as the constrained investment in catalysing digital innovations and the strong interdependencies among multiple systems across micro to macro scales in the built environment.

This Special Issue aims to publish original research, both theoretical and empirical, contributing to the uptake of digital technologies to provoke and facilitate the digitalisation of the built environment from all perspectives. We welcome the contributions setting out feasible decarbonisation paths and sustainability transitions for this carbon-intensive sector, which is responsible for approximately 40% of the global carbon emission. Meanwhile, the exposure of the built environment to climate-driven disturbances increases significantly, such as heatwave, flooding, and droughts. We invite researchers to share their contributions regarding the adoption of emerging and disruptive digital technologies to enhance resilience to multiple hazards and climate change. Among the technologies with significant potential that lead toward a smarter and more sustainable built environment, we mention the Internet of Things, Building Information Modelling (BIM), semantic web technology, knowledge graph, artificial intelligence, digital twin, and cyber–physical system. We look forward to receiving your contributions elaborating on the adaption of these technologies to tackle domain-specific challenges.

Topics may include, but are not limited to, the following:

  • Additive manufacturing and robotics as enablers for digital construction;
  • Digital twin for revamping the operation and maintenance processes of built assets;
  • Advanced energy informatics to improve the energy performance of built assets;
  • System-of-systems approach to build climate-resilient interconnected infrastructure;
  • Optimisation of integrated urban energy systems under a renewable energy dominant future.

References

  1. Chen, L.; Xie, X.; Lu, Q.; Parlikad, A.K.; Pitt, M.; Yang, J. Gemini principles-based digital twin maturity model for asset management. Sustainability 2021, 13, 8224. https://doi.org/10.3390/su13158224.
  2. McKinsey Global Institute Research. Available online: https://www.ntia.doc.gov/files/ntia/publications/james_manyika_digital_economy_deba_may_16_v4.pdf (accessed on 23 November 2023).
  3. Pomponi, F.; Moncaster, A. Embodied carbon mitigation and reduction in the built environment–What does the evidence say? J. Environ. Manag. 2016. 181, 687–700. https://doi.org/10.1016/j.jenvman.2016.08.036.
  4. Schwab, K. The Fourth Industrial Revolution; Portfolio Penguin: London, UK, 2017.
  5. Xie, X.; Lu, Q.; Herrera, M.; Yu, Q.; Parlikad, A.K.; Schooling, J.M. Does historical data still count? Exploring the applicability of smart building applications in the post-pandemic period. Sustain. Cities Soc. 2021, 69, 102804. https://doi.org/10.1016/j.scs.2021.102804.

Dr. Xiang Xie
Dr. Long Chen
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

  • digital transformation
  • informatics
  • built environment
  • construction industry
  • sustainability
  • climate resilience

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Published Papers (3 papers)

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Research

18 pages, 4208 KiB  
Article
Short-Term Multi-Step Wind Direction Prediction Based on OVMD Quadratic Decomposition and LSTM
by Banteng Liu, Yangqing Xie, Ke Wang, Lizhe Yu, Ying Zhou and Xiaowen Lv
Sustainability 2023, 15(15), 11746; https://doi.org/10.3390/su151511746 - 30 Jul 2023
Cited by 3 | Viewed by 1016
Abstract
Accurate and reliable wind direction prediction is important not only for enhancing the efficiency of wind power conversion and ensuring safe operation, but also for promoting sustainable development. Wind direction forecasting is a challenging task due to the random, intermittent and unstable nature [...] Read more.
Accurate and reliable wind direction prediction is important not only for enhancing the efficiency of wind power conversion and ensuring safe operation, but also for promoting sustainable development. Wind direction forecasting is a challenging task due to the random, intermittent and unstable nature of wind direction. This paper proposes a short-term wind direction prediction model based on quadratic decomposition and long short-term memory (LSTM) to improve the accuracy and efficiency of wind direction prediction. Firstly, the model adopts a seasonal-trend decomposition procedure based on the loess (STL) method to divide the wind direction series into three subsequences: trend, seasonality and the remainder, which reduces the impact of the original sequence’s complexity and non-stationarity on the prediction performance. Then, the remainder subsequence is decomposed by the optimal variational mode decomposition (OVMD) method to further explore the potential characteristics of the wind direction sequence. Next, all the subsequences are separately input into the LSTM model, and the prediction results of each subsequence from the model are superimposed to obtain the predicted value. The practical wind direction data from a wind farm were used to evaluate the model. The experimental results indicate that the proposed model has superior performance in the accuracy and stability of wind direction prediction, which also provides support for the efficient operation of wind turbines. By developing advanced wind prediction technologies and methods, we can not only enhance the efficiency of wind power conversion, but also ensure a sustainable and reliable supply of renewable energy. Full article
(This article belongs to the Special Issue When Digital Meets Traditional: How Does the Built Environment Head toward Sustainability?)
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19 pages, 4134 KiB  
Article
MACLA-LSTM: A Novel Approach for Forecasting Water Demand
by Ke Wang, Zanting Ye, Zhangquan Wang, Banteng Liu and Tianheng Feng
Sustainability 2023, 15(4), 3628; https://doi.org/10.3390/su15043628 - 16 Feb 2023
Cited by 6 | Viewed by 1616
Abstract
Sustainable and effective management of urban water supply is a key challenge for the well-being and security of current society. Urban water supply systems have to deal with a huge amount of data, and it is difficult to develop efficient intervention mechanisms by [...] Read more.
Sustainable and effective management of urban water supply is a key challenge for the well-being and security of current society. Urban water supply systems have to deal with a huge amount of data, and it is difficult to develop efficient intervention mechanisms by relying on the human experience. Deep learning methods make it possible to predict water demand in real-time; however, deep learning methods have a large number of hyperparameters, and the selection of hyperparameters can easily affect the accuracy of prediction. Within this context, a novel framework of short-term water demand forecast is proposed, in which a forecasting method clouded leopard algorithm based on multiple adaptive mechanisms—long short-term memory networks (MACLA-LSTM)—is developed to improve the accuracy of water demand predictions. Specifically, LSTM networks are used to predict water demand and the MACLA is utilized to optimize the input parameters of the LSTM. The MACLA-LSTM model is evaluated on a real dataset sampled from water distribution systems. In comparison with other methods, the MACLA-LSTM achieved MAE values of 1.12, 0.89, and 1.09; MSE values of 2.22, 1.21, and 2.38; and R2 values of 99.51%, 99.44%, and 99.01%. The results show the potential of the MACLA-LSTM model for water demand forecasting tasks and also demonstrate the positive effect of the MACLA on forecasting tasks by comparing results with LSTM variant models. The proposed MACLA-LSTM can provide a resilient, sustainable, and low-cost management strategy for water supply systems. Full article
(This article belongs to the Special Issue When Digital Meets Traditional: How Does the Built Environment Head toward Sustainability?)
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17 pages, 2378 KiB  
Article
Community Governance Based on Sentiment Analysis: Towards Sustainable Management and Development
by Xudong Zhang, Zejun Yan, Qianfeng Wu, Ke Wang, Kelei Miao, Zhangquan Wang and Yourong Chen
Sustainability 2023, 15(3), 2684; https://doi.org/10.3390/su15032684 - 2 Feb 2023
Cited by 3 | Viewed by 2039
Abstract
The promotion of community governance by digital means is an important research topic in developing smart cities. Currently, community governance is mostly based on reactive response, which lacks timely and proactive technical means for emergency monitoring. The easiest way for residents to contact [...] Read more.
The promotion of community governance by digital means is an important research topic in developing smart cities. Currently, community governance is mostly based on reactive response, which lacks timely and proactive technical means for emergency monitoring. The easiest way for residents to contact their properties is to call the property call center, and the call centers of many properties store many speech data. However, text sentiment classification in community scenes still faces challenges such as small corpus size, one-sided sentiment feature extraction, and insufficient sentiment classification accuracy. To address such problems, we propose a novel community speech text sentiment classification algorithm combining two-channel features and attention mechanisms to obtain effective emotional information and provide decision support for the emergency management of public emergencies. Firstly, text vectorization based on word position information is proposed, and a SKEP-based community speech–text enhancement model is constructed to obtain the corresponding corpus. Secondly, a dual-channel emotional text feature extraction method that integrates spatial and temporal sequences is proposed to extract diverse emotional features effectively. Finally, an improved cross-entropy loss function suitable for community speech text is proposed for model training, which can achieve sentiment analysis and obtain all aspects of community conditions. The proposed method is conducive to improving community residents’ sense of happiness, satisfaction, and fulfillment, enhancing the effectiveness and resilience of urban community governance. Full article
(This article belongs to the Special Issue When Digital Meets Traditional: How Does the Built Environment Head toward Sustainability?)
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