Artificial Intelligence Prediction Model for Swelling Potential of Soil and Quicklime Activated Rice Husk Ash Blend for Sustainable Construction

Onyelowe, Kennedy C.; Jalal, Fazal E.; Onyia, Michael E.; Onuoha, Ifeanyichukwu C.; Alaneme, George Uwadiegwu; Ikpa, Chidozie

doi:10.17576/jkukm-2021-33(4)-07

Cited by 8 publications

(1 citation statement)

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“…This was closely followed by the mixture containing 10% GBFS15% SSA, achieving an MDD of 1543 kg/m 3 at an OMC of 14%. Evidently, the concurrent presence of SSA and GBFS yielded an intriguing interplay, leading to the discernible augmentation of compaction characteristics in these amalgamated samples 24 , 49 . These findings hold significant implications in the broader context of landfill liner design and construction.…”

Section: Resultsmentioning

confidence: 94%

Compaction and compressibility characteristics of snail shell ash and granulated blast furnace slag stabilized local bentonite for baseliner of landfill

Fadugba,

Adeniran,

Alaneme

et al. 2024

Sci Rep

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This study comprehensively explores the compaction and compressibility characteristics of snail shell ash (SSA) and ground-granulated blast-furnace slag (GBFS) in stabilizing local bentonite for landfill baseliner applications. The untreated soil, with a liquid limit of 65%, plastic limit of 35%, and plasticity index of 30%, exhibited optimal compaction at a moisture content of 32% and a maximum dry density of 1423 kg/m3. SSA revealed a dominant presence of 91.551 wt% CaO, while GBFS contained substantial 53.023 wt% SiO2. Treated samples with 20% GBFS and 5% SSA exhibited the highest maximum dry density (1561 kg/m3) and optimal moisture content (13%), surpassing other mixtures. The 15% SSA-treated sample demonstrated superior strength enhancement, reaching an unconfined compressive strength of 272.61 kPa over 28 days, while the 10% GBFS-treated sample achieved 229.95 kPa. The combination of 15% SSA exhibited the highest shear strength (49 kPa) and elastic modulus (142 MPa), showcasing robust mechanical properties. Additionally, the 15% SSA sample displayed favourable hydraulic conductivity (5.57 × 10–8 cm/s), outperforming other mixtures. Notably, the permeability test, a critical aspect of the study, was meticulously conducted in triplicate, ensuring the reliability and reproducibility of the reported hydraulic conductivity values. Treated samples with SSA and GBFS showed reduced compressibility compared to the control soil, with the 15% SSA-treated sample exhibiting a more consistent response to applied pressures. Scanning Electron Microscopy analysis revealed substantial composition changes in the 15% SSA mixture, suggesting its potential as an effective base liner in landfill systems. In conclusion, the 15% SSA sample demonstrated superior mechanical properties and hydraulic conductivity, presenting a promising choice for landfill liner applications.

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Section: Resultsmentioning

confidence: 94%

Compaction and compressibility characteristics of snail shell ash and granulated blast furnace slag stabilized local bentonite for baseliner of landfill

Fadugba,

Adeniran,

Alaneme

et al. 2024

Sci Rep

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Prediction of cost and duration of building construction using artificial neural network

2022

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Critical review on the application of artificial intelligence techniques in the production of geopolymer-concrete

Alaneme¹,

Olonade²,

Esenogho³

2023

SN Appl. Sci.

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The need to employ technology that replaces traditional engineering methods which generate gases that worsen our environment has emerged in an era of dwindling ecosystem owing to global warming has a negative influence on the earth system’s ozone layer. In this study, the exact method of using artificial intelligence (AI) approaches in sustainable structural materials optimization was investigated to ensure that concrete construction projects for buildings have no negative environmental effects. Since they are used in the forecasting/predicting of an agro-waste-based green geopolymer concrete system, the intelligent learning algorithms of Fuzzy Logic, ANFIS, ANN, GEP and other nature-inspired algorithms were reviewed. A systematic literature search was conducted to identify relevant studies published in various databases. The included studies were critically reviewed to analyze the types of AI techniques used, the research methodologies employed, and the main findings reported. To meticulously sort the crucial components of aluminosilicate precursors and alkaline activators blend and to optimize its engineering behavior, laboratory methods must be carried out through the mixture experiment design and raw materials selection. Such experimental activities often fall short of the standards set by civil engineering design guidelines for sustainable construction purposes. At some instances, specific shortcomings in the design of experiments or human error may degrade measurement correctness and cause unforeseen discharge of pollutants. Most errors in repetitive experimental tests have been eliminated by using adaptive AI learning techniques. Though, as an extensive guideline for upcoming investigators in this cutting-edge and developing field of AI, the pertinent smart intelligent modelling tools used at various times, under varying experimental testing methodologies, and leveraging different source materials were addressed in this study review. The findings of this review study demonstrate the benefits, challenges and growing interest in utilizing AI techniques for optimizing geopolymer-concrete production. The review identified a range of AI techniques, including machine learning algorithms, optimization models, and performance evaluation measures. These techniques were used to optimize various aspects of geopolymer-concrete production, such as mix design, curing conditions, and material selection.

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Artificial Intelligence Prediction Model for Swelling Potential of Soil and Quicklime Activated Rice Husk Ash Blend for Sustainable Construction

Cited by 8 publications

References 0 publications

Compaction and compressibility characteristics of snail shell ash and granulated blast furnace slag stabilized local bentonite for baseliner of landfill

Compaction and compressibility characteristics of snail shell ash and granulated blast furnace slag stabilized local bentonite for baseliner of landfill

Prediction of cost and duration of building construction using artificial neural network

Critical review on the application of artificial intelligence techniques in the production of geopolymer-concrete

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