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During this pandemic, advanced epidemiological models have been widely used to determine intervention strategies for controlling the spread of the disease in public and healthcare settings. These models played a crucial role by providing predictive insights into disease transmission dynamics, informing resource allocation and guiding policy decisions. However, the accuracy of these predictions depends on the substantial amount of input data, which was not readily available at the onset of the pandemic. Another concern with the existing models is their inability to adequately account for the complex indoor built environments, which has been shown to significantly impact infection risk. To tackle these issues, this paper discusses the potential of developing a joint modelling technique that integrates machine learning models, building information models and agent-based models to assess the risk of nosocomial airborne infections. With limited available data, machine learning models can determine infection risk with high confidence. [ABSTRACT FROM AUTHOR]

The rise in early-age temperature concrete structures, driven by the exothermic reactions during cement hydration, significantly increases the risk of thermal cracking. To address this issue, the construction industry employs several strategies, including the partial substitution of cement with ground granulated blast furnace slag (GGBS) due to its lower heat of hydration. Accurately predicting the hydration temperature of concrete is critical for preventing thermal cracking. This task becomes more complex, with fluctuating ambient temperatures influencing hydration kinetics and heat dissipation. Previous studies often assume adiabatic or isothermal conditions, thus overlooking the impact of ambient temperature variations. This paper presents an innovative finite element modelling (FEM) approach to simulate the hydration temperature progression in in situ concrete slabs, incorporating the effects of ambient temperature fluctuations. Isothermal calorimetry curves were adjusted using the Arrhenius-based approach to express the cement hydration rate as a function of ambient temperature. The FEM outcomes, validated with semi-adiabatic calorimetry tests, demonstrate the model's capability to forecast temperature development in in situ concrete under varying ambient conditions. Additionally, the study examines the influence of partial cement replacement with GGBS on thermal behaviour, revealing that while GGBS effectively reduces thermal reactions at higher contents, its efficacy diminishes with rising ambient temperatures. [ABSTRACT FROM AUTHOR]