ABSTRACT
Purpose: This study aims to focus on the sustainability of prefabricated medical emergency buildings (PMEBs) renovation after the epidemic, to address the problem that large numbers of PMEBs may be abandoned for losing their original architectural functions. This study develops an evaluation system to identify and measure sustainable factors for PMEBs’ renovation schemes. Qualitative and quantitative analysis of PMEBs’ renovation scheme was conducted based on cloud model evaluation method and selected the renovation scheme in line with sustainable development. The study promotes evaluation methods and decision-making basis for the renovation design of global PMEBs and realizes the use-value of building functions again. Design/methodology/approach: By referring to the existing literature, design standards and expert visiting a set of evaluation index systems which combines the renovation of the PMEBs and the sustainability concept has been established, which calculates the balanced optimal comprehensive weight of each indicator utilizing combination weighting method, and quantifies the qualitative language of different PMEBs’ renovation schemes by experts through characteristics of the cloud model. This paper takes Huoshenshan hospital a representative PMEB during the epidemic period as an example, to verify the feasibility of the cloud model evaluation method. Findings: The research results of this paper are that in the PMEBs’ renovation scheme structural reformative (T11) and corresponding nature with the original building (T13) have the most important influence;the continuity of architectural cultural value (T22) and regional development coherence (T23) are the key factors affecting the social dimension;the profitability of renovated buildings (T34) is the key factor affecting the economic dimension;the environmental impact (T41), resource utilization (T42) and ecological technology (T43) are the key factors in the environmental dimension. Originality/value: This study contributes to the existing body of knowledge by supplementing a set of scientific evaluation methods to make up for the sustainability measurement of PMEBs’ renovation scheme. The main objective was to make renovated PMEBs meet the needs of urban sustainable development, retain the original cultural value of the buildings, meanwhile enhance their social and economic value and realize the renovation with the least impact on the environment. © 2022, Emerald Publishing Limited.
ABSTRACT
The COVID-19 epidemic poses a great threat to public health and people's lives, which has initiated new challenges to the prevention and control system of the epidemic in China. In all efforts for epidemic control and prevention, predicting the risk of epidemic spread is of great practical importance for scientific prevention and control, and precise strategies. To predict the risk of an epidemic rapidly and quantitatively, this paper fused multi-source spatiotemporal data and established a risk prediction model for epidemic transmission by coupling LSTM algorithm and cloud model. Firstly, a simulation model of the spatiotemporal spread of infectious diseases was built based on GIS and LSTM algorithm, which simulated the infectious disease's spatiotemporal transmission process by learning rules in historical epidemic data. At the same time, to improve the simulation accuracy, this paper took 1 km × 1 km for the spatial scale, and days for the temporal scale as the study scale. Secondly, this paper applied the simulated data of infectious cases and the spatiotemporal influence factors on the spread of the epidemic to construct risk evaluation indicators. Finally, the cloud model and adaptive strategies were applied to construct an epidemic risk assessment model. In this way, the epidemic risk assessment at multiple spatial scales was achieved. In the empirical study phase, based on the Beijing COVID-19 epidemic data from 11 June 2020 to 25 June 2020, this paper simulated the process of the spatial evolution of the epidemic from 26 June 2020 to 1 July 2020. To test the advantage of the LSTM model applied to simulate spatiotemporal spread of infectious diseases, four machine learning models were introduced for comparison, including GA-BP Neural Network, Decision Regression Tree, Random Forest, and Support Vector Machine. The results were as follows: ① Compared with other conventional machine learning models, the LSTM model with time-series relationship had higher simulation accuracy (MAE=0.002 61) and better fitting degree (R-Square=0.9455). This showed that the LSTM model considering the temporal relationship between epidemic data was more suitable for epidemic spatial evolution simulation. ② The application results showed that the coupled model can not only fully consider the influence of infection source factors, weather factors, epidemic spread factors and epidemic prevention factors on the spread of transmission risk and reflect the trend of risk evolution, but also quickly quantify regional risk levels. Therefore, the coupled model based on LSTM algorithm and cloud model can effectively predict the transmission risk of epidemic, and also provide a method reference for establishing spatial-temporal transmission models and assessing epidemic risk. 2021, Science Press. All right reserved.