Multi-Criteria Analysis of a Developed Prefabricated Footing System on Reactive Soil Foundation

Teodosio, Bertrand; Bonacci, Francesco; Seo, Seongwon; Baduge, Shanaka Kristombu; Mendis, Priyan

doi:10.3390/en14227515

Cited by 3 publications

(1 citation statement)

References 23 publications

(34 reference statements)

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“…In the United States of America and the United Kingdom, reactive soil damage resulted to US$15 billion and £3 billion for a span of one year (Jones and Jefferson 2012;Li et al 2014). In Australia, the surface geology is approximately 20% Basaltic clays (Richards et al 1983;Teodosio et al 2021a). The world's largest Basaltic plain is located in the Western suburbs of Melbourne and has detrimental impacts on residential structures and lightweight infrastructures.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Residential Slab Foundation Movement Through a Finite Element-Based Deep Learning Algorithm

et al. 2022

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Deep learning networks were employed to predict the maximum differential deflection of stiffened and waffle rafts due to reactive soil movements, Δmax. Four deep learning networks were used to predict Δmax, these are (1) stiffened rafts on shrinking soil, (2) stiffened rafts on swelling soil, (3) waffle rafts on shrinking soil, and (4) waffle rafts on swelling soil. The deep learning models were used to create design lines, which showed that both soil and structural features strongly influence the stiffened rafts. In contrast, waffle rafts showed a strong dependence on soil features in shrinking soils and beam depth in swelling soils. This demonstrates that the finite element-based deep learning networks captured the effect of the embedment of the beams. The results of the deep learning models led to non-linear design curves, which are disparate from the suggested standard Australian design. These results suggest that increasing the value of beam depth can have a positive or negative impact on the global residential slab depending on the type of substructure and whether the founding reactive soil is shrinking or swelling. Global sensitivity analyses of the deep learning models showed that for stiffened rafts on shrinking soil, the slab length, slab width and active depth zone of reactive soil had the most significant influence on Δmax, whilst for stiffened rafts on swelling soil, the primary drivers are ground movement, beam depth, and slab width. The prediction of Δmax for waffle rafts on shrinking soil was driven by the surface characteristic and mound movements, and the active depth zone, whilst waffle rafts on swelling soil was driven by the beam depth. Overall, the finite element-based deep learning showed the capacity to estimate Δmax in both shrinking and swelling design scenarios for different types of residential footing systems to further understand the characteristic behaviour of shallow residential slab foundations on reactive soils leading to improved designs.

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Section: Introductionmentioning

confidence: 99%