Machine learning application to predict the mechanical properties of glass fiber mortar

Nakkeeran, G.; Krishnaraj, L.; Bahrami, Alireza; Almujibah, Hamad; Panchal, Hitesh; Zahra, Musaddak Maher Abdul

doi:10.1016/j.advengsoft.2023.103454

Cited by 27 publications

(4 citation statements)

References 20 publications

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“…Temporal models of process-structure evolution processes have been successfully developed (Hashemi and Kalidindi 2021). Multiple researchers have used machine learning for the prediction of material properties (Zheng et al 2018, Menon et al 2019, Chaabene et al 2020, Yucel et al 2020, Liu et al 2022, Nakkeeran et al 2023, development of models from optical data (Furat et al 2019, Yucel et al 2020, Zuo et al 2022, and building models with consideration of multi-scale information (Han et al 2019, Reimann et al 2019, Yucel et al 2020, Bracconi 2022, Bishara et al 2023. Machine learning has even shown its ability to aid in constitutive modeling of temperature-dependent magneto-rheological fluids (Bahiuddin et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Machine-learned constitutive relations for mechanoluminescent ZnS:Cu–PDMS composites

Hoover,

Huang,

Ryu

2023

Smart Mater. Struct.

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Materials with novel properties, such as emerging smart materials, offer a design challenge to researchers who want to make use of their unique behaviors. The complex nature of these material responses can be difficult to model from a physics-based understanding as a full description of the multi-physics, multi-scale, and non-linear phenomena requires expertise from various scientific disciplines. Some new smart materials, such as the mechanoluminescent (ML) copper-doped zinc sulfide (ZnS:Cu)-embedded in polydimethylsiloxane (PDMS) (ZnS:Cu-PDMS), lack a constitutive model or an agreement on the mechanisms of action behind the unique material properties. As constitutive equations are essential to engineer devices, with existing knowledge gap in underlying physics of smart materials, a viable approach is to use empirical data for deriving constitutive equations. However, it is challenging to derive constitutive equations on non-linear, multi-variate, and multi-physics relationship using conventional data processing approaches due to the size and complexity of the empirical data. In this work, a machine learning framework is proposed for ones to de- rive constitutive equations using empirical data for novel materials. The framework is validated by creating constitutive models for ZnS:Cu-PDMS elastomeric composites undergoing a variety of tensile load patterns. To avoid confinement of the models to the programming environment, in which they are developed, numerical fits of the machine-learned models are created as constitutive equations for the non-linear, multi-variate, and multi-physics ML properties. These models can be used when designing ML ZnS:Cu-PDMS to develop devices to harness the unique ML properties. 

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Section: Introductionmentioning

confidence: 99%

Machine-learned constitutive relations for mechanoluminescent ZnS:Cu–PDMS composites

Hoover,

Huang,

Ryu

2023

Smart Mater. Struct.

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“…The density did not change significantly; however, only a slight change was observed. A marginal decrease in the density of the PC waste ash when 10% was replaced correlated with the lower specific gravity of the cement when 10% was replaced 31 . The formation of more porous transition zones around ash particles likely contributed further to the density reduction.…”

Section: Resultsmentioning

confidence: 95%

Modelling the mechanical properties of concrete produced with polycarbonate waste ash by machine learning

Sathvik,

Kumar,

Ulloa

et al. 2024

Sci Rep

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India’s cement industry is the second largest in the world, generating 6.9% of the global cement output. Polycarbonate waste ash is a major problem in India and around the globe. Approximately 370,000 tons of scientific waste are generated annually from fitness care facilities in India. Polycarbonate waste helps reduce the environmental burden associated with disposal and decreases the need for new raw materials. The primary variable in this study is the quantity of polycarbonate waste ash (5, 10, 15, 20 and 25% of the weight of cement), partial replacement of cement, water-cement ratio and aggregates. The mechanical properties, such as compressive strength, split tensile strength and flexural test results, of the mixtures with the polycarbonate waste ash were superior at 7, 14 and 28 days compared to those of the control mix. The water absorption rate is less than that of standard concrete. Compared with those of conventional concrete, polycarbonate waste concrete mixtures undergo minimal weight loss under acid curing conditions. Polycarbonate waste is utilized in the construction industry to reduce pollution and improve the economy. This study further simulated the strength characteristics of concrete made with waste polycarbonate ash using least absolute shrinkage and selection operator regression and decision trees. Cement, polycarbonate waste, slump, water absorption, and the ratio of water to cement were the main components that were considered input variables. The suggested decision tree model was successful with unparalleled predictive accuracy across important metrics. Its outstanding predictive ability for split tensile strength (R2 = 0.879403), flexural strength (R2 = 0.91197), and compressive strength (R2 = 0.853683) confirmed that this method was the preferred choice for these strength predictions.

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“…Parameters' effects on the experiment are more noticeable when their associated F values are greater. If the result of the P value is less than 0.05, the significance of the model or parameters is accepted 22 , 23 .…”

Section: Methodsmentioning

confidence: 99%

Machine learning optimization of bio-sandcrete brick modelling using response surface methodology

Ganasen,

Krishnaraj,

Onyelowe

et al. 2024

Sci Rep

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In this study, raw grinded groundnut shell (RGGNS) was used as a fine aggregate in the brick industry to reuse agricultural waste in building materials. In this study, an experimental approach was used to examine a new cement brick with raw groundnut shells integrated with compressive strength, water absorption and dry density optimization utilizing response surface methodology (RSM). The raw ground-nut shell content improved the fine aggregate performance of the 40%, 50%, and 60% samples. The 28-day high compressive strength with the raw ground-nut shell was 6.1 N/mm2 maximum, as needed by the technical standard. Samples made from 40%, 50%, and 60% raw groundnut shells yielded densities of 1.7, 2.2, and 1.9 kg/cm3 for groundnut shell (GNS) brick, respectively. A product's mechanical properties meet the IS code standard’s minimum requirements. RSM was then utilized to develop a model for the addition of raw groundnut shell to concrete. R-square and Adeq precision values indicated that the results are highly significant, and equations for predicting compressive strength, water absorption, and dry density have been developed. In addition, optimization was performed on the RSM findings to determine the efficiency optimization of the model. Following the optimization results, experiments were conducted to determine the applicability of the optimized model.

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Machine learning application to predict the mechanical properties of glass fiber mortar

Cited by 27 publications

References 20 publications

Machine-learned constitutive relations for mechanoluminescent ZnS:Cu–PDMS composites

Machine-learned constitutive relations for mechanoluminescent ZnS:Cu–PDMS composites

Modelling the mechanical properties of concrete produced with polycarbonate waste ash by machine learning

Machine learning optimization of bio-sandcrete brick modelling using response surface methodology

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