Experimental study on sustainable stabilization of marine soil with ultrafine slag and activator for controlling its cracking characteristics

Wanare, Ram; Jayanthi, Prathyusha; Iyer, Kannan K. R.

doi:10.1016/j.conbuildmat.2022.128310

Cited by 11 publications

(4 citation statements)

References 56 publications

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“…Environmental factors may likely play a pivotal role in the evaporation process. Uday et al (2015) and Wanare et al (2022) have documented the significant impact of temperature, humidity, and solar radiation on soil moisture dynamics. These studies underscore the necessity of incorporating a broad spectrum of environmental conditions into predictive models to enhance their applicability to real-world scenarios.…”

Section: Discussionmentioning

confidence: 99%

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Can machine learning models predict soil moisture evaporation rates? An investigation via novel feature selection techniques and model comparisons

Priyanka,

Kumar,

Panda

et al. 2024

Front. Earth Sci.

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Extreme weather events and global climate change have exacerbated the problem of evaporation rates. Thus, accurately predicting soil moisture evaporation rates affecting soil cracking becomes crucial. However, less is known about how novel feature engineering techniques and machine-learning predictions may account for estimating the soil moisture evaporation rate. This research focuses on predicting the evaporation rate of soil using machine learning (ML) models. The dataset comprised twenty-one ground-based parameters, including temperature, humidity, and soil-related features, used as features to predict evaporation potential. To tackle the high number of features and potential uncorrelated features, a novel guided backpropagation-based feature selection technique was developed to rank the most relevant features. The top-10 features, highly correlated with evaporation rate, were selected for ML model input, alongside the top-5 and all features. Several ML models, including multiple regression (MR), K-nearest neighbor (KNN), multilayer perceptron (MLP), sequential minimal optimization regression (SMOreg), random forest (RF), and a novel K-Nearest Oracles (KNORA) ensemble, were constructed for the purpose of forecasting the evaporation rate. The average error of these models was assessed using the root mean squared error (RMSE). Experimental results showed that the KNORA ensemble model performed the best, achieving a 7.54 mg/h RMSE in testing with the top-10 features. MLP was followed closely by a 25.1 mg/h RMSE in the same testing. An empirical model using all features showed a higher RMSE of 1319.1 mg/h, indicating the superiority of the ML models for accurate evaporation rate predictions. We highlight the implications of our results for climate-induced soil cracking in the real world.

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Section: Discussionmentioning

confidence: 99%

“…The results of these ML models can be compared to existing analytic methods that are grounded in geotechnical engineering. Fine-grained soil cracking can also be controlled by controlling evaporation rates using appropriate materials (Naresh and Uday, 2018;Wanare et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Can machine learning models predict soil moisture evaporation rates? An investigation via novel feature selection techniques and model comparisons

Priyanka,

Kumar,

Panda

et al. 2024

Front. Earth Sci.

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“…Table 2 summarizes the materials used, their main properties and potentials, examples of applications, and additional factors to consider for using NGs. Most of the literature regarding composites focuses on technical testing, particularly those using traditional geotechnical methods [14,[31][32][33]. However, microstructural analysis is required for a comprehensive understanding of the behavior of composites in tailings.…”

Section: Identification Of Ksfsmentioning

confidence: 99%

“…Their formulation was based on the authors' experience. A5: Experimental evaluation of environmental contamination aspects [35,101] A6: Collaboration between universities and governmental agencies for the development of applied research [110] A7: Verification of cost-benefits through quantitative financial methods [38,111] A8: Verification of cost-benefits through socio-environmental justifications [38] A9: Low implementation complexity [112,113] A10: Clear, concise, and easily accessible information on the advantages of new geotechnical materials compared to traditional construction methods [38,71,111] A11: Promotion of solutions suitable for the regional context [33,108] B B1: Development of materials more resistant to the effects of climate change [24,[114][115][116][117][118] B2: Development of materials that use waste as reinforcement or matrix [99,103,119,120] B3: Development of materials that propose decarbonization [38,40,121,122] B4: Life cycle analysis of materials and verification of carbon footprint [39,121] B5: Compatibility with the Sustainable Development Goals [24,[123][124][125] B6: Compatibility with the Nationally Determined Contributions [13,111] B7: Development of construction techniques that stimulate regional development [108,119,126] B8: Development of ...…”

Section: Rubbermentioning

confidence: 99%

Key Success Factors for the Practical Application of New Geomaterials

Alelvan,

Santos,

Pierozan

et al. 2023

Sustainability

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Geomaterials comprise naturally formed materials through geological processes, such as soils and rocks, or artificially processed materials, including mineral waste and geosynthetics. These materials find extensive use in geotechnical structures, such as slopes, dams, and pavements, among others. However, two issues commonly arise in earthworks: the materials available in the region do not meet the minimum engineering requirements, resulting in high transportation costs, and the exploitation of new deposits increases environmental impacts. Consequently, there is a need to develop stabilization and reinforcement techniques aimed at creating new geomaterials (NGs) to expand the range of local material applications. In this context, the present study evaluates the key success factors (KSFs) related to the application of NGs in geotechnical structures. The Delphi method was employed through a structured questionnaire developed after an extensive literature review. Brazilian experts from the public, private, and academic sectors were selected to identify the obstacles and potential pathways for the practical application of NGs. The outcomes of the study indicated that the lack of standardization, the complex behavior of geomaterials under varying conditions, as well as technical and economic limitations serve as barriers impeding the widespread adoption of NGs. Finally, a roadmap proposal was devised, encompassing a series of actions intended to facilitate the broader utilization of NGs.

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Novel protection systems for the improvement in soil and water stability of expansive soil slopes

Ma,

He,

et al. 2023

J. Mt. Sci.

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Experimental study on sustainable stabilization of marine soil with ultrafine slag and activator for controlling its cracking characteristics

Cited by 11 publications

References 56 publications

Can machine learning models predict soil moisture evaporation rates? An investigation via novel feature selection techniques and model comparisons

Can machine learning models predict soil moisture evaporation rates? An investigation via novel feature selection techniques and model comparisons

Key Success Factors for the Practical Application of New Geomaterials

Novel protection systems for the improvement in soil and water stability of expansive soil slopes

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