Table of Content

Computer Modelling for Safer Built Environment and Smart Cities

Submission Deadline: 31 May 2023 (closed)

Guest Editors

Prof. Rita Yi Man Li, Hong Kong Shue Yan University, China
Prof. Ramesh Agarwal, Washington University in St. Louis, USA
Prof. Xiao-Guang Yue, European University Cyprus, Cyprus


Safer built environment and smart cities have been the goal of many modern cities. The built environment includes 5 stages: urban planning, construction, occupation, demolition and renewal stage. Computer modelling aids safer urban planning by utilizing geographical information, artificial intelligence such as computer vision allows construction industry detects any risks on construction sites and demolition works. In occupation stage, property management allows camera to detect any strangers enter the buildings to enhance safety in buildings. In this special issue, we wish to shed light on the role of computer modelling in safer built environment and smart cities. Papers including but not limited to:

-Artificial intelligence modelling for construction safety
-Artificial neural network for building safety
-TinyML for built environment
-Digital Twins and computer modelling for the construction industry
-Building information modelling for safer built environment
-Smart cities modelling for safer built environment
-Smart urban planning by using computer vision modelling
-Natural Language Modelling for smart and safe cities analysis
-Ways to lower the safety risks at work in different stages of built environment
-Robot safety via computer modelling


-Artificial Intelligence
-Built Environment
-Computer Modelling
-Information Technology
-Natural Language Processing
-Safety Science
-Smart Cities

Published Papers

  • Open Access


    Predicting Carpark Prices Indices in Hong Kong Using AutoML

    Rita Yi Man Li, Lingxi Song, Bo Li, M. James C. Crabbe, Xiao-Guang Yue
    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 2247-2282, 2023, DOI:10.32604/cmes.2022.020930
    (This article belongs to this Special Issue: Computer Modelling for Safer Built Environment and Smart Cities)
    Abstract The aims of this study were threefold: 1) study the research gap in carpark and price index via big data and natural language processing, 2) examine the research gap of carpark indices, and 3) construct carpark price indices via repeat sales methods and predict carpark indices via the AutoML. By researching the keyword “carpark” in Google Scholar, the largest electronic academic database that covers Web of Science and Scopus indexed articles, this study obtained 999 articles and book chapters from 1910 to 2019. It confirmed that most carpark research threw light on multi-storey carparks, management and ventilation systems, and reinforced… More >

    Graphic Abstract

    Predicting Carpark Prices Indices in Hong Kong Using AutoML

Share Link