Relationship between reactive soil movement and footing deflection: A coupled hydro-mechanical finite element modelling perspective

Teodosio, Bertrand; Baduge, Shanaka Kristombu; Mendis, Priyan

doi:10.1016/j.compgeo.2020.103720

Cited by 10 publications

(16 citation statements)

References 33 publications

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“…The monolithic waffle rafts deformed excessively specifically when experiencing shrinking ground motion. The monolithic waffle rafts, based on the numerical simulations [27], do not perform satisfactorily on shrinking soil due to insufficient top reinforcing steel bars. The main flexural reinforcements were located at the bottom portion as stipulated in Standards Australia [24].…”

Section: Structural Performancementioning

confidence: 99%

“…An advanced three-dimensional coupled hydromechanical finite element model, developed and validated by Teodosio et al [18], Teodosio et al [27], was used to investigate the structural performance of a prefabricated footing system. Fluid diffusion in an isotropic porous soil medium was modelled as [28,29]:…”

Section: Structural Performance Using a Hydromechanical Modelmentioning

confidence: 99%

“…The possible reasons that caused the footing system to experience system deformation more than ∆ max are, first, the calculated EI/L from the recommended design in Standards Australia [24] may have been underestimated. This recommended design was based on the analysis using the Walsh Method, an uncoupled two-dimensional approach discussed in [27]. Secondly, due to the approach used in the design standard (i.e., AS 2870-2011), consideration of the non-linear behaviour and plastic mechanisms of soil, concrete and steel were not considered.…”

Section: Structural Performancementioning

confidence: 99%

“…The developed hydromechanical finite element model [18,27] was used to investigate the global performance of the developed prefabricated footing systems. The developed model by [18,27] was validated using three case studies. The first case study verified the field data collected by Fityus et al [53] in New Castle, Australia.…”

Section: Appendix a Details Of The Numerical Simulations Using The Hydromechanical Model By Teodosio Et Almentioning

confidence: 99%

“…The comparison highlighted the capability of the developed model to capture complex mound profiles that cannot be obtained using traditional uncoupled methods [18]. Moreover, mesh convergence and boundary effects studies were performed to determine the appropriate mesh size and required dimensions of the soil mass to prevent influence on the calculated soil movement values [27]. The recommended mesh size of the soil mass was 0.8 m. The applicable dimensions of the soil mass were 30 m for the width, more than three and a half times the length of the raft footing, and 12.5 m for the depth of the soil mass, which was at least eight times the depth of the raft footing.…”

Section: Appendix a Details Of The Numerical Simulations Using The Hydromechanical Model By Teodosio Et Almentioning

confidence: 99%

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

Teodosio

Bonacci²,

Seo

et al. 2021

Energies

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The need for advancements in residential construction and the hazard induced by the shrink–swell reactive soil movement prompted the development of the prefabricated footing system of this study, which was assessed and compared to a conventional waffle raft using a multi-criteria analysis. The assessment evaluates the structural performance, cost efficiency, and sustainability using finite element modelling, life cycle cost analysis, and life cycle assessment, respectively. The structural performance of the developed prefabricated system was found to have reduced the deformation and cracking by approximately 40%. However, the cost, GHG emission, and embodied energy were higher in the prefabricated footing system due to the greater required amount of concrete and steel than that of the waffle raft. The cost difference between the two systems can be reduced to as low as 6% when prefabricated systems were installed in a highly reactive sites with large floor areas. The life cycle assessment further observed that the prefabricated footing systems consume up to 21% more energy and up to 18% more GHG emissions. These can significantly be compensated by reusing the developed prefabricated footing system, decreasing the GHG emission and energy consumption by 75–77% and 55–59% with respect to that of the waffle raft.

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Section: Structural Performancementioning

confidence: 99%

Section: Structural Performance Using a Hydromechanical Modelmentioning

confidence: 99%

Section: Structural Performancementioning

confidence: 99%

Section: Appendix a Details Of The Numerical Simulations Using The Hydromechanical Model By Teodosio Et Almentioning

confidence: 99%

Section: Appendix a Details Of The Numerical Simulations Using The Hydromechanical Model By Teodosio Et Almentioning

confidence: 99%

See 3 more Smart Citations

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

Teodosio

Bonacci²,

Seo

et al. 2021

Energies

Self Cite

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Shrink–swell index prediction through deep learning

Teodosio

Wasantha

Yaghoubi

et al. 2022

Neural Comput & Applic

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Growing application of artificial intelligence in geotechnical engineering has been observed; however, its ability to predict the properties and nonlinear behaviour of reactive soil is currently not well considered. Although previous studies provided linear correlations between shrink–swell index and Atterberg limits, obtained model accuracy values were found unsatisfactory results. Artificial intelligence, specifically deep learning, has the potential to give improved accuracy. This research employed deep learning to predict more accurate values of shrink–swell indices, which explored two scenarios; Scenario 1 used the features liquid limit, plastic limit, plasticity index, and linear shrinkage, whilst Scenario 2 added the input feature, fines percentage passing through a 0.075-mm sieve (%fines). Findings indicated that the implementation of deep learning neural networks resulted in increased model measurement accuracy in Scenarios 1 and 2. The values of accuracy measured in this study were suggestively higher and have wider variance than most previous studies. Global sensitivity analyses were also conducted to investigate the influence of each input feature. These sensitivity analyses resulted in a range of predicted values within the variance of data in Scenario 2, with the %fines having the highest contribution to the variance of the shrink–swell index and a relevant interaction between linear shrinkage and %fines. The proposed model Scenario 2 was around 10–65% more accurate than the preceding models considered in this study, which can then be used to expeditiously estimate more accurate values of shrink–swell indices.

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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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Relationship between reactive soil movement and footing deflection: A coupled hydro-mechanical finite element modelling perspective

Cited by 10 publications

References 33 publications

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

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

Shrink–swell index prediction through deep learning

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

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