AI modelling &amp;amp; mapping functions: a cognitive, physics-guided, simulation-free and instantaneous approach to fire evaluation

Naser, M.Z.; Hostetter, Haley; Daware, Aditya

doi:10.14264/a0b3b36

Cited by 3 publications

(1 citation statement)

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“…For example, Jonnalagadda et al [32] used ANN to investigate the effects of skew and span length on prestressed concrete bridge deck and superstructure condition ratings. Naser et al [33] utilized ANN and other machine learning methods to study reinforced concrete beams and their response in fires. These studies demonstrate the ability of ANN to analyze outcomes in the built environment that are dependent on complex relationships and interactions between variables.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Quantitative evaluation of the relationship between physical parameters and building demolition or adaptation outcomes

McFarland

Ross

Naser

et al. 2021

Archit. Struct. Constr.

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Building adaptation and re-use can contribute to a circular and sustainable built environment, as existing buildings are adapted and the need for new construction materials is reduced. The “adaptability” of buildings has been widely studied; however, few of these studies are quantitative. This paper uses Artificial Neural Networks (ANN) and Logistic Regression (LR) models to explore relationships between the physical features of buildings and their demolition or adaptation outcomes. Source data were taken from 59 buildings that were either demolished or adapted in the Netherlands. After the models were created and validated, a series of sensitivity studies were conducted to evaluate relationships between physical parameters and building outcomes. The physical parameter with the strongest relationship to adaptation outcomes was demountability (ease of removal) of building service elements. The quantitative results were then compared to results from an adjacent qualitative study. The relationships observed from the quantitative sensitivity studies align well with the qualitative observations.

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Section: Artificial Neural Networkmentioning

confidence: 99%