An Artificial Intelligence Based Data-Driven Method for Forecasting Unconfined Compressive Strength of Cement Stabilized Soil by Deep Mixing Technique

Mojtahedi, S. Farid F.; Ahmadihosseini, Adel; Sadeghi, Hamed

doi:10.1007/s10706-022-02297-1

Cited by 13 publications

(5 citation statements)

References 65 publications

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“…The results demonstrated that slag powder outperformed quicklime in enhancing silt stability. Mojtahedi et al [63] executed a comprehensive set of tests to explore how various parameters, such as cement content, water-cement ratio, and age, affect the performance characteristics of cementstabilized soil. The findings indicated a negative correlation between the water-cement ratio and the unconfined compressive strength of the cement-stabilized soil when the cement content was held constant.…”

Section: Silt Improvement Technologymentioning

confidence: 99%

Advancements in the Evolution of Engineering Characteristics and Reinforcement Technologies for Subgrade Silt

Liu,

Zhao,

Liu

2023

Materials

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Technical challenges associated with the treatment of silt subgrades frequently arise in coastal and river delta areas. Given the importance of environmental sustainability, the selection of efficient, cost-effective, and eco-friendly techniques for silt subgrade stabilization is paramount. While recycled polyester fibers primarily sourced from discarded polyester bottles have not yet been systematically employed in silt subgrade reinforcement, their potential is considerable. This paper offers a comprehensive review of the existing literature on the microstructural, physicochemical, and mechanical properties of silt, summarizing prior advancements in silt stabilization methodologies. Building upon this foundation, we introduce a novel approach utilizing recycled polyester fibers for silt subgrade improvement, outlining both its application prospects and challenges, which require further investigation. The findings of this study serve as a robust scientific foundation for the broader adoption and engineering implementation of this technology.

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Section: Silt Improvement Technologymentioning

confidence: 99%

Advancements in the Evolution of Engineering Characteristics and Reinforcement Technologies for Subgrade Silt

Liu,

Zhao,

Liu

2023

Materials

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“…At the design stage, the possibility of using AI technologies is important for predicting the shear strength of the soil, the preliminary cost and the duration of construction. At the initial stage, the volume of such information is limited, but at least the preliminary cost and duration of construction tasks are important for both bidders and customers [161,162]. Similarly, before starting construction, it is important to know the shear strength of the soil in order to have an idea of the ability of the building to withstand the harsh effects of flooding and/or earthquakes.…”

Section: Ai At the Design Stagementioning

confidence: 99%

Artificial Intelligence Methods for the Construction and Management of Buildings

Ivanova,

Kuznetsov,

Zverev

et al. 2023

Sensors

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Artificial intelligence covers a variety of methods and disciplines including vision, perception, speech and dialogue, decision making and planning, problem solving, robotics and other applications in which self-learning is possible. The aim of this work was to study the possibilities of using AI algorithms at various stages of construction to ensure the safety of the process. The objects of this research were scientific publications about the use of artificial intelligence in construction and ways to optimize this process. To search for information, Scopus and Web of Science databases were used for the period from the early 1990s (the appearance of the first publication on the topic) until the end of 2022. Generalization was the main method. It has been established that artificial intelligence is a set of technologies and methods used to complement traditional human qualities, such as intelligence as well as analytical and other abilities. The use of 3D modeling for the design of buildings, machine learning for the conceptualization of design in 3D, computer vision, planning for the effective use of construction equipment, artificial intelligence and artificial superintelligence have been studied. It is proven that automatic programming for natural language processing, knowledge-based systems, robots, building maintenance, adaptive strategies, adaptive programming, genetic algorithms and the use of unmanned aircraft systems allow an evaluation of the use of artificial intelligence in construction. The prospects of using AI in construction are shown.

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“…The behavior of soil is highly complex and nonlinear, and traditional analytical and empirical approaches may not be able to capture all of the underlying relationships between output and input variables. Because of this, the usage of ML techniques is particularly useful in soil mechanics [ 40 ].…”

Section: Introductionmentioning

confidence: 99%

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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An Artificial Intelligence Based Data-Driven Method for Forecasting Unconfined Compressive Strength of Cement Stabilized Soil by Deep Mixing Technique

Cited by 13 publications

References 65 publications

Advancements in the Evolution of Engineering Characteristics and Reinforcement Technologies for Subgrade Silt

Advancements in the Evolution of Engineering Characteristics and Reinforcement Technologies for Subgrade Silt

Artificial Intelligence Methods for the Construction and Management of Buildings

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

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