The influence of nano-silica precursor on the compressive strength of mortar using Advanced Machine Learning for sustainable buildings

Onyelowe, Kennedy C.; Ebid, Ahmed M.; Hanandeh, Shadi

doi:10.1007/s42107-023-00832-w

Cited by 15 publications

(25 citation statements)

References 47 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 ]. Structure: ANN consists of interconnected nodes, called neurons or units, organized in layers.…”

Section: Research Programmentioning

confidence: 99%

“…Learning: ANNs learn from data through a process called training. During training, the network is presented with input data, and the weights of the connections are adjusted based on the network’s output and the expected output [ 50 ]. This process is often performed using optimization algorithms such as gradient descent and its variants.…”

Section: Research Programmentioning

confidence: 99%

“…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 ]. Applications: ANNs are used in a wide range of applications, including image and speech recognition, natural language processing, recommendation systems, financial forecasting, medical diagnosis, and many other fields where complex pattern recognition and prediction tasks are required [ 51 ].…”

Section: Research Programmentioning

confidence: 99%

“…The algorithm starts with a population of random mathematical expressions (polynomials) and uses genetic operators such as selection, crossover, and mutation to evolve these expressions over several generations [ 51 ]. 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 ].…”

Section: Research Programmentioning

confidence: 99%

“…Statistical analysis of experimental data forms the basis for these empirical methodologies [ 12 ]. Despite providing estimates of soil behavior in a fast and straightforward manner, it is possible that these techniques are limited in their ability to capture complex relationships [ 41 , 50 ].…”

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%