Characteristic analysis and improvement methods of the indoor thermal environment in post-disaster temporary residential buildings: A systematic review

Qin, Mingyuan; Chew, Bee Teng; Yau, Yat Huang; Yang, Zhen; Han, Xiaofei; Chang, Li; Liu, Yiqiao; Song, Peng

doi:10.1016/j.buildenv.2023.110198

Cited by 12 publications

(3 citation statements)

References 140 publications

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“…Many studies have detailed the reasonable technology for the indoor thermal environment of temporary residential buildings after disasters, and the thermal insulation performance of the selected envelope materials is the main research direction at present [13]. On the one hand, numerous scholars have studied passive methods to regulate the thermal environment of tents [2,[14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Indoor Thermal Environment Evaluation for Emergency Medical Tents in Heating Season: Onsite Testing and Case Study in China

Han,

Jin,

Zhao

et al. 2024

Atmosphere

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In this study, the standard tent used by the China International Medical Team (Sichuan) was used as the research object to study the internal temperature change in medical tents in a low-temperature environment relying on heating equipment. Method: Four temperature sensors were arranged along the horizontal direction at a 1.2 m height in the medical tent, and more sensors were installed at heights of 0.1, 0.2, 0.6, 1.2, 1.8, 2.4, and 2.5 m. A total of 11 temperature sensors were set. Temperature tests were conducted in January and February 2021 in Chengdu, Sichuan Province. During the test, the running time of the heating equipment was controlled in real time according to the temperature change trend. A Kolmogorov–Smirnov(K-S) test was used to verify the reliability of the experimental data. The temperature change trend was used to characterize the influence of the heating and cooling equipment on the temperature change inside the tent. Results: Due to the position angle of the heating equipment and the influence of the external environment, the spatial distribution of the ambient temperature inside the medical tent was obviously uneven. In winter, an electric heater with a heating power of about 2500 W can increase the internal temperature of the tent to 16.7 °C, significantly improving the internal thermal environment of the medical tent. The ambient temperature in the medical tent is positively correlated with the height and the installation position of the heating equipment. Conclusion: Medical tents can maintain the ambient temperature well to meet medical needs with the support of heating equipment with sufficient power. The temperature distribution law of medical tents in this experiment has good guiding significance for the placement angle of heating equipment and the configuration position of medical equipment and provides a reference for the development of thermal insulation materials for medical tents.

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Section: Introductionmentioning

confidence: 99%

Indoor Thermal Environment Evaluation for Emergency Medical Tents in Heating Season: Onsite Testing and Case Study in China

Han,

Jin,

Zhao

et al. 2024

Atmosphere

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“…Accommodation is the most important component of post-disaster relief and building. Because of outside factors, refugees frequently spend years or even months in shelters for emergencies or transitional shelters [2]. The research in [3] examines how people decide whether to evacuate during unexpected emergencies, with an emphasis on the variables that affect how people react to man-made disasters.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Post-Disaster Analysis Using Image Processing Techniques

Gupta,

Helena Raj,

Lal

et al. 2024

E3S Web of Conf.

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Post-disaster recovery is a multifaceted system essential for rebuilding communities and infrastructure. Despite its importance, many limitations obstruct powerful recuperation, main to tremendous loss of life and monetary assets. This paper synthesizes varied approaches in the direction of sustainable restoration, highlighting the increasing reliance on technology for disaster management. Image processing strategies, pivotal in addressing these demanding situations, are reviewed across studies. Those strategies range from SLIC segmentation and Random forest classification to advanced deep learning models together with U-net and YOLOv8, machine learning algorithms like SVM, and image category methodologies along with bi-temporal analysis. Comparative evaluation reveals that those strategies presents promising consequences, with accuracies starting from 75% to over 94%. The paper gives a framework for understanding the role of various image processing strategies in improving disaster control strategies, emphasizing their implications for future studies and application.

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“…The problem is that all these requirements are intended for construction under normal conditions, such as scheduled urban development, the implementation of a government housing policy, commercial investment projects, etc. There are a number of studies in the scientific community dedicated to the construction of prefabricated buildings and temporary housing for the resettlement of people who have lost their homes [4][5][6][7][8]; however, rebuilding urban infrastructure and cities itself is a problem that is not sufficiently studied. The rebuilding of cities after destruction is complicated by a number of factors, so it has a longer duration, which adversely affects the population [9].…”

Section: Introductionmentioning

confidence: 99%

A Decision Support System for Organizing Quality Control of Buildings Construction during the Rebuilding of Destroyed Cities

Lapidus,

Makarov,

Kozlova

2023

Buildings

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Natural disasters and warfare lead to the destruction of city buildings and infrastructure, leaving large numbers of people homeless. The rebuilding of destroyed cities needs to be carried out promptly while maintaining a balance between construction quality and duration. Rework due to defects and the lengthy approval of non-conformances significantly increases the duration of construction. This study aimed to develop a decision support system to fix or negotiate strategies to address construction defects, depending on their level of risk. The paper addresses the following objectives: classifying defects by the quality of construction that they affect; building a tree of construction defect risks; and developing an artificial neural network (ANN) to assess the defect risk. The weights of the links are represented by posterior probabilities of damage calculated using the Bayes’ theorem in the pre-training stage. The ANN has been adapted to cast-in-place reinforced concrete structures. When training the resulting ANN on a sample of precedents, the test sample demonstrated convergence and low errors. The resulting model will accelerate construction by automating assessments of defect severity and reducing the time spent on reworking defects with low quality risk.

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Characteristic analysis and improvement methods of the indoor thermal environment in post-disaster temporary residential buildings: A systematic review

Cited by 12 publications

References 140 publications

Indoor Thermal Environment Evaluation for Emergency Medical Tents in Heating Season: Onsite Testing and Case Study in China

Indoor Thermal Environment Evaluation for Emergency Medical Tents in Heating Season: Onsite Testing and Case Study in China

A Comparative Analysis of Post-Disaster Analysis Using Image Processing Techniques

A Decision Support System for Organizing Quality Control of Buildings Construction during the Rebuilding of Destroyed Cities

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