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Emergency Plans and Disaster Management in the Era of Smart...
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Emergency Plans and Disaster Management in the Era of Smart Cities

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (1 February 2024) | Viewed by 2807

Special Issue Editors

Prof. Dr. Min Hu

E-Mail Website
Guest Editor
Shanghai Engineering Research Center of Urban Infrastructure Renewal, Shanghai 200032, China
Interests: intelligent information processing; machine learning; intelligent control; infrastructure construction; operation and maintenance
Prof. Dr. Vijayan Sugumaran

E-Mail Website
Guest Editor
Department of Decision and Information Sciences, School of Business Administration, Oakland University, Rochester, MI 48309, USA
Interests: artificial intelligence; statistical modeling; semantic; big data analytics in health, business, and engineering

Special Issue Information

Dear Colleagues,

Modern city management has entered the "smart cities" era through developing technologies such as the internet of things, artificial intelligence, and data communication. As a result, urban disaster management and emergency response have become crucial tasks in developing smart cities because disasters regularly result in significant economic and human losses worldwide and constitute a severe threat to the lives and property of city residents.

In addition to improving the infrastructural robustness of urban transportation, water supply, and sanitation，applying smart city technologies to urban disaster management and emergency responses, identifying urban risks in real-time, conducting early disaster warnings, optimizing emergency response plans, organizing more effective disaster response, and improving urban management and services has become critical to improving cities' resilience capacity to disasters.

The issues covered by this theme, such as disaster management theory under new technologies for smart cities, risk assessment and disaster prediction and warning methods based on big data ecology, disaster emergency response planning and decision-making, and urban disaster management systems, have seen significant research and application progress.

However, in the face of various damages caused by frequent artificial and natural disasters, urban disaster management and emergency response still have many unsolved problems. New issues keep emerging, for example，a disaster prediction model construction based on sparse spatial and temporal data, urban infrastructure urban robustness and recoverability assessments, urban disaster management system designs with cost and safety balance, disaster simulation using technologies such as collaborative virtual environment and context-aware computing, and disaster rescue and recovery decisions under uncertainties and complex social relationships.

It is essential to regularly bring together high-quality research, innovative management concepts, and practices. By sharing and discussing theories, methods, and cases of disaster management in smart cities, it is vital to support the progress of human society and the development of cities. Therefore, this Special Issue aims to provide a platform to promote in-depth thinking and innovative practices on problems in this field. 

Prof. Dr. Min Hu
Prof. Dr. Vijayan Sugumaran
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

  • disaster
  • forecast
  • management
  • emergency response
  • artificial intelligence
  • smart city
  • safety
  • machine learning

Published Papers (4 papers)

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Research

24 pages, 4766 KiB  
Article
A Multi-Information Fusion Method for Repetitive Tunnel Disease Detection
by Zhiyuan Gan, Li Teng, Ying Chang, Xinyang Feng, Mengnan Gao and Xinwen Gao
Sustainability 2024, 16(10), 4285; https://doi.org/10.3390/su16104285 - 19 May 2024
Viewed by 414
Abstract
Existing tunnel defect detection methods often lack repeated inspections, limiting longitudinal analysis of defects. To address this, we propose a multi-information fusion approach for continuous defect monitoring. Initially, we utilized the You Only Look Once version 7 (Yolov7) network to identify defects in [...] Read more.
Existing tunnel defect detection methods often lack repeated inspections, limiting longitudinal analysis of defects. To address this, we propose a multi-information fusion approach for continuous defect monitoring. Initially, we utilized the You Only Look Once version 7 (Yolov7) network to identify defects in tunnel lining videos. Subsequently, defect localization is achieved with Super Visual Odometer (SuperVO) algorithm. Lastly, the SuperPoint–SuperGlue Matching Network (SpSg Network) is employed to analyze similarities among defect images. Combining the above information, the repeatability detection of the disease is realized. SuperVO was tested in tunnels of 159 m and 260 m, showcasing enhanced localization accuracy compared to traditional visual odometry methods, with errors measuring below 0.3 m on average and 0.8 m at maximum. The SpSg Network surpassed the depth-feature-based Siamese Network in image matching, achieving a precision of 96.61%, recall of 93.44%, and F1 score of 95%. These findings validate the effectiveness of this approach in the repetitive detection and monitoring of tunnel defects. Full article
(This article belongs to the Special Issue Emergency Plans and Disaster Management in the Era of Smart Cities)
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26 pages, 3598 KiB  
Article
Multimodal Framework for Smart Building Occupancy Detection
by Mohammed Awad Abuhussain, Badr Saad Alotaibi, Yakubu Aminu Dodo, Ammar Maghrabi and Muhammad Saidu Aliero
Sustainability 2024, 16(10), 4171; https://doi.org/10.3390/su16104171 - 16 May 2024
Viewed by 416
Abstract
Over the years, building appliances have become the major energy consumers to improve indoor air quality and occupants’ lifestyles. The primary energy usage in building sectors, particularly lighting, Heating, Ventilation, and Air conditioning (HVAC) equipment, is expected to double in the upcoming years [...] Read more.
Over the years, building appliances have become the major energy consumers to improve indoor air quality and occupants’ lifestyles. The primary energy usage in building sectors, particularly lighting, Heating, Ventilation, and Air conditioning (HVAC) equipment, is expected to double in the upcoming years due to inappropriate control operation activities. Recently, several researchers have provided an automated solution to turn HVAC and lighting on when the space is being occupied and off when the space becomes vacant. Previous studies indicate a lack of publicly accessible datasets for environmental sensing and suggest developing holistic models that detect buildings’ occupancy. Additionally, the reliability of their solutions tends to decrease as the occupancy grows in a building. Therefore, this study proposed a machine learning-based framework for smart building occupancy detection that considered the lighting parameter in addition to the HVAC parameter used in the existing studies. We employed a parametric classifier to ensure a strong correlation between the predicting parameters and the occupancy prediction model. This study uses a machine learning model that combines direct and environmental sensing techniques to obtain high-quality training data. The analysis of the experimental results shows high accuracy, precision, recall, and F1-score of the applied RF model (0.86, 0.99, 1.0, and 0.88 respectively) for occupancy prediction and substantial energy saving. Full article
(This article belongs to the Special Issue Emergency Plans and Disaster Management in the Era of Smart Cities)
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22 pages, 2074 KiB  
Article
A Variable-Weight Model for Evaluating the Technical Condition of Urban Viaducts
by Li Li, Huihui Rao, Minghao Wang, Weisheng Mao and Changzhe Jin
Sustainability 2024, 16(7), 2718; https://doi.org/10.3390/su16072718 - 26 Mar 2024
Viewed by 449
Abstract
Urban viaducts play a crucial role in transportation infrastructure and are closely linked to urban resilience. Accurate evaluation of their structural technical condition forms the basis for the scientific maintenance of urban viaducts. Currently, there is a lack of technical condition evaluation specifications [...] Read more.
Urban viaducts play a crucial role in transportation infrastructure and are closely linked to urban resilience. Accurate evaluation of their structural technical condition forms the basis for the scientific maintenance of urban viaducts. Currently, there is a lack of technical condition evaluation specifications for viaducts in China, and the existing bridge specifications that are similar do not fully align with the facility composition characteristics and maintenance management needs of viaducts. Therefore, this paper presents a technical condition assessment model for viaducts, based on existing bridge specifications. Considering the frequent damage to ancillary facilities of viaducts, the utilization of maintenance resources, and the impact on traffic operations, the model proposed in this paper adopts the Analytic Hierarchy Process (AHP) to introduce a new indicator layer for ancillary facilities. Subsequently, the weight values and deduction values of each layer of the model, as well as the findings of damage recorded in the new components, were determined using the Group Decision-Making (GDM) method and the Delphi method. This process forms a constant-weight evaluation model for assessing the technical condition of viaducts. Finally, to account for the impacts of significant damage to low-weight components on the structural condition, the variable-weight method was adopted to establish a comprehensive evaluation model with variable weights, which was then validated using practical viaduct examples. The results indicate that the variable-weight model provides a more accurate representation of the technical condition of viaducts, especially when components are severely damaged. Furthermore, this study examines the suitable conditions for implementing the constant-weight evaluation model and the variable-weight evaluation model, demonstrating that the variable-weight model is recommended when there is a significant disparity in the scores among the viaduct components, whereas the constant-weight model is applicable in other scenarios. Full article
(This article belongs to the Special Issue Emergency Plans and Disaster Management in the Era of Smart Cities)
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20 pages, 6830 KiB  
Article
Shield Tunnel (Segment) Uplift Prediction and Control Based on Interpretable Machine Learning
by Min Hu, Junchao Sun, Bingjian Wu, Huiming Wu and Zhenjiang Xu
Sustainability 2024, 16(2), 910; https://doi.org/10.3390/su16020910 - 21 Jan 2024
Viewed by 761
Abstract
Shield tunnel segment uplift is a common phenomenon in construction. Excessive and unstable uplift will affect tunnel quality and safety seriously, shorten the tunnel life, and is not conducive to the sustainable management of the tunnel’s entire life cycle. However, segment uplift is [...] Read more.
Shield tunnel segment uplift is a common phenomenon in construction. Excessive and unstable uplift will affect tunnel quality and safety seriously, shorten the tunnel life, and is not conducive to the sustainable management of the tunnel’s entire life cycle. However, segment uplift is affected by many factors, and it is challenging to predict the uplift amount and determine its cause accurately. Existing research mainly focuses on analyzing uplift factors and the uplift trend features for specific projects, which is difficult to apply to actual projects directly. This paper sorts out the influencing factors of segment uplift and designs a spatial-temporal data fusion mechanism for prediction. On this basis, we extract the key influencing factors of segment uplift, construct a prediction model of segment uplift amount based on Extreme Gradient Boosting (XGBoost) v2.0.3, and use SHapley Additive exPlanation (SHAP) v0.44.0 to locate factors affecting uplift, forming an Auxiliary Decision-making System for Segment Uplift Control (ADS-SUC). An ADS-SUC not only detects the sudden change of the segment uplift successfully and predicts the segment uplift in practical engineering accurately, it also provides a feasible method to control the uplift in time, which is of great significance for reducing the construction risk of the tunnel project and ensuring the quality of the completed tunnel. Full article
(This article belongs to the Special Issue Emergency Plans and Disaster Management in the Era of Smart Cities)
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