Runtime-based metaheuristic prediction of the compressive strength of net-zero traditional concrete mixed with BFS, FA, SP considering multiple curing regimes

Onyelowe, Kennedy C.; Kontoni, Denise-Penelope N.; Pilla, Sita Rama Murty; Hanandeh, Shadi; Ebid, Ahmed M.; Razzaghian Ghadikolaee, Mehrdad; Stephen, Liberty U.

doi:10.1007/s42107-023-00839-3

Cited by 8 publications

(21 citation statements)

References 49 publications

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“…An artificial neural network (ANN), the typical framework if which is illustrated in Fig 4 is a computational model inspired by the structure and function of biological neural networks, such as the human brain [ 49 ]. It is a powerful machine learning technique used for solving complex problems such as pattern recognition, classification, regression, and more [ 50 ].…”

Section: Research Programmentioning

confidence: 99%

“…Activation Function: Each neuron in a neural network typically applies an activation function to the weighted sum of its inputs. Common activation functions include sigmoid, tanh, ReLU (Rectified Linear Unit), and their variants [ 49 ]. Types of Neural Networks: There are various architectures of neural networks, such as feedforward neural networks (the most basic type), convolutional neural networks (CNNs) for image processing, recurrent neural networks (RNNs) for sequential data, and more advanced architectures like deep neural networks (DNNs) and generative adversarial networks (GANs) [ 50 ].…”

Section: Research Programmentioning

confidence: 99%

“…Evolutionary Polynomial Regression (EPR) is a non-linear regression technique that uses a genetic programming approach to evolve mathematical models [ 49 ]. It was proposed by Dr. Nordin Zakaria in the early 1990s.…”

Section: Research Programmentioning

confidence: 99%

“…During the evolutionary process, the algorithm evaluates the fitness of each mathematical expression by comparing its performance in fitting the dataset [ 50 ]. The fittest expressions are then selected to produce offspring through genetic operators, and this process is repeated for multiple generations until a satisfactory mathematical model is obtained [ 49 ]. EPR has been used in various fields, including engineering, economics, and environmental science, to model complex relationships between input variables and output data [ 50 ].…”

Section: Research Programmentioning

confidence: 99%

“…Techniques that fall into this category contain empirical, analytical, and numerical approaches [ 48 ]. Empirical methods are frequently used in the field of soil mechanics to develop correlations between the engineering properties of soil and other factors such as soil type, moisture content, and confining pressure [ 49 ]. Statistical analysis of experimental data forms the basis for these empirical methodologies [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

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Modeling the influence of lime on the unconfined compressive strength of reconstituted graded soil using advanced machine learning approaches for subgrade and liner applications

Guo,

Garcia,

Andrade Valle

et al. 2024

PLoS ONE

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In the field of soil mechanics, especially in transportation and environmental geotechnics, the use of machine learning (ML) techniques has emerged as a powerful tool for predicting and understanding the compressive strength behavior of soils especially graded ones. This is to overcome the sophisticated equipment, laboratory space and cost needs utilized in multiple experiments on the treatment of soils for environmental geotechnics systems. This present study explores the application of machine learning (ML) techniques, namely Genetic Programming (GP), Artificial Neural Networks (ANN), Evolutionary Polynomial Regression (EPR), and the Response Surface Methodology in predicting the unconfined compressive strength (UCS) of soil-lime mixtures. This was for purposes of subgrade and landfill liner design and construction. By utilizing input variables such as Gravel, Sand, Silt, Clay, and Lime contents (G, S, M, C, L), the models forecasted the strength values after 7 and 28 days of curing. The accuracy of the developed models was compared, revealing that both ANN and EPR achieved a similar level of accuracy for UCS after 7 days, while the GP model performed slightly lower. The complexity of the formula required for predicting UCS after 28 days resulted in decreased accuracy. The ANN and EPR models achieved accuracies of 85% and 82%, with R2 of 0.947 and 0.923, and average error of 0.15 and 0.18, respectively, while the GP model exhibited a lower accuracy of 66.0%. Conversely, the RSM produced models for the UCS with predicted R2 of more than 98% and 99%, for the 7- and 28- day curing regimes, respectively. The RSM also produced adequate precision in modelling UCS of more than 14% against the standard 7%. All input factors were found to have almost equal importance, except for the lime content (L), which had an average influence. This shows the importance of soil gradation in the design and construction of subgrade and landfill liners. This research further demonstrates the potential of ML techniques for predicting the strength of lime reconstituted G-S-M-C graded soils and provides valuable insights for engineering applications in exact and sustainable subgrade and liner designs, construction and performance monitoring and rehabilitation of the constructed civil engineering infrastructure.

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Section: Research Programmentioning

confidence: 99%

Section: Research Programmentioning

confidence: 99%

Section: Research Programmentioning

confidence: 99%