• Proceedings of the Korean Institute of Landscape Architecture Conference
• /
• 2023.04a
• /
• pp.118-120
• /
• 2023

• Journal of Distribution Science
• /
• v.22 no.4
• /
• pp.69-77
• /
• 2024

Purpose: This paper aims to tackle the challenges and opportunities of cities' response to COVID-19 and provide cities with policy implications for better adapting to the post-pandemic era. Cities around the world are facing new challenges and have had to adapt to maintain social distancing measures while also addressing equity and social inclusion issues. Research design, data and methodology: The research methodology relies on an examination of existing literature, coupled with trend analysis employing discourse analysis to investigate post-pandemic urban resilience. The article also attempts to employ the concepts of adaptive urbanism and spatial flexibility and their potential to address these challenges not only in response to the pandemic, but also in the long-term. Results: The article explores the impact of COVID-19 on urban spatial structure through a public health lens and proposes actions that cities are able to take to enhance their resilience in the aftermath of the pandemic. Conclusions: It underscores the significance of reconstructing with improved distribution dynamics and provides valuable guidance for companies and policymakers on navigating these challenges. Ultimately, it also suggests that the pandemic has initiated a worldwide restructuring of urban planning, potentially leading to the emergence of smart cities grounded in science and technology.

• Smart Structures and Systems
• /
• v.13 no.1
• /
• pp.81-98
• /
• 2014

This paper presents the elastic buckling of smart lightweight column structures integrated with a pair of surface piezoelectric layers using artificial intelligence. The finite element modeling of Smart lightweight columns is found using $ANSYS^{(R)}$ software. Then, the first buckling load of the structure is calculated using eigenvalue buckling analysis. To determine the accuracy of the present finite element analysis, a compression study is carried out with literature. Later, parametric studies for length variations, width, and thickness of the elastic core and of the piezoelectric outer layers are performed and the associated buckling load data sets for artificial intelligence are gathered. Finally, the application of soft computing-based methods including artificial neural network (ANN), fuzzy inference system (FIS), and adaptive neuro fuzzy inference system (ANFIS) were carried out. A comparative study is then made between the mentioned soft computing methods and the performance of the models is evaluated using statistic measurements. The comparison of the results reveal that, the ANFIS model with Gaussian membership function provides high accuracy on the prediction of the buckling load in smart lightweight columns, providing better predictions compared to other methods. However, the results obtained from the ANN model using the feed-forward algorithm are also accurate and reliable.