Effective thermal conductivity of sands estimated by Group Method of Data Handling (GMDH)

Rizvi, Zarghaam Haider; Husain, Syed Mohammad Baqir; Hasan, Haider; Wuttke, Frank

doi:10.1016/j.matpr.2020.02.454

Cited by 12 publications

(4 citation statements)

References 17 publications

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“…The reported work of the GMDH application to compute the ETC is limited to Rizvi et al [46], where 80 measurements of four different sandy soils were used for training and testing. Soil porosity, saturation and particle size were considered as inputs.…”

Section: Gmdh Resultsmentioning

confidence: 99%

“…The group method of data handling (GMDH) has the advantage to phase out the less influential parameters, thus reducing the demand for training data and parameters, thus reducing the complexity of the network [43][44][45]. The GMDH is implemented to estimate the thermal conductivity of sand considering porosity, saturation and particle shape [46].…”

Section: Introductionmentioning

confidence: 99%

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Neural Network Approaches for Computation of Soil Thermal Conductivity

et al. 2022

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The effective thermal conductivity (ETC) of soil is an essential parameter for the design and unhindered operation of underground energy transportation and storage systems. Various experimental, empirical, semi-empirical, mathematical, and numerical methods have been tried in the past, but lack either accuracy or are computationally cumbersome. The recent developments in computer science provided a new computational approach, the neural networks, which are easy to implement, faster, versatile, and reasonably accurate. In this study, we present three classes of neural networks based on different network constructions, learning and computational strategies to predict the ETC of the soil. A total of 384 data points are collected from literature, and the three networks, Artificial neural network (ANN), group method of data handling (GMDH) and gene expression programming (GEP), are constructed and trained. The best accuracy of each network is measured with the coefficient of determination (R2) and found to be 91.6, 83.2 and 80.5 for ANN, GMDH and GEP, respectively. Furthermore, two sands with 80% and 99% quartz content are measured, and the best performing network from each class of ANN, GMDH and GEP is independently validated. The GEP model provided the best estimate for 99% quartz sand and GMDH with 80%.

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Section: Gmdh Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neural Network Approaches for Computation of Soil Thermal Conductivity

et al. 2022

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“…Eventually, it was assumed that concrete with 45% of plastic waste aggregate by volume was selected as structural lightweight concrete. Rizvi et al [31] used neural networks method to compute the effective thermal conductivity of two sands of different moisture content. The model anticipated experimental data was in line for the pure quartz sand, however, for quartz-feldspar the model over predicts the efficient thermal conductivity.…”

Section: Concrete Thermal Conductivity Studiesmentioning

confidence: 99%

Comprehensive review study for the effect of utilizing waste ma-terials on the thermal conductivity of concretes

Mohammed¹,

Ahmed²,

Dawood³

2021

AJES

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Since concrete is one of the most popularly utilized building mixtures in construction, a high demand of natural resources is significantly emerged. Therefore, a skyrocketed attention has been paid to create new opportunities for the use of recycle materials to develop a new construction substance with more satisfactory properties. The use of waste products in concrete is not only economical, but it helps in solid waste management as well. Among various properties of concrete, thermal conductivity is a crucial factor that plays an important role in in building insulation by evaluating a material's capacity to transfer heat. This paper aims to review the potential application of waste materials in concrete as additive ingredients and investigate the effect of this waste material on thermal conductivity of concrete. The review of literature revealed that the application of most of the waste materials exhibited an obvious potential as thermal insulator. However, further investigated work is needed to highlight the advantages of utilizing waste materials in concrete containing various type of waste materials K ey w ord s: Concrete, fresh and mechanical properties, thermal conductivity, waste material

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“…Web of Conferences 205, 04006 (2020) ICEGT 2020 https://doi.org/10.1051/e3sconf/202020504006 different class of neural networks known as group method of data handling (GMDH)[22].…”

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confidence: 99%

Effective thermal conductivity of unsaturated soils based on deep learning algorithm

et al. 2020

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Soil thermal conductivity plays a critical role in the design of geo-structures and energy transportation systems. Effective thermal conductivity (ETC) of soil depends primarily on the degree of saturation, porosity and mineralogical composition. These controlling parameters have nonlinear dependencies, thus making prediction a nontrivial task. In this study, an artificial neural network (ANN) model is developed based on the deep learning (DL) algorithm to predict the effective thermal conductivity of unsaturated soil. A large dataset is constructed including porosity, degree of saturation and quartz content from literature to train and validate the developed model. The model is constructed with a different number of hidden layers and neurons in each hidden layer. The standard errors for training and testing are calculated for each variation of hidden layers and neurons. The network with the least error is adopted for prediction. Two sand types independent of training and validation data reported in the literature are considered for prediction of the ETC. Five simulation runs are performed for each sand, and the computed results are plotted against the reported experimental results. The results conclude that the developed ANN model provides an efficient, easy and straightforward way to predict soil thermal conductivity with reasonable accuracy.

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Effective thermal conductivity of sands estimated by Group Method of Data Handling (GMDH)

Cited by 12 publications

References 17 publications

Neural Network Approaches for Computation of Soil Thermal Conductivity

Neural Network Approaches for Computation of Soil Thermal Conductivity

Comprehensive review study for the effect of utilizing waste ma-terials on the thermal conductivity of concretes

Effective thermal conductivity of unsaturated soils based on deep learning algorithm

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