Applicability of mixed solidification material based on inorganic waste as soil stabilizer

Inazumi, Shinya; Intui, Sutasinee; Jotisankasa, Apiniti; Chaiprakaikeow, Susit; Shinsaka, Takashi

doi:10.1016/j.cscm.2019.e00305

Cited by 9 publications

(16 citation statements)

References 18 publications

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“…In addition, when trying to attain early solidification, there is a method of partially replacing the fine powder of blast furnace slag with ordinary cement. (4) Compared with the solidifying material using the heat-treating type of ES additive [7][8][9][10] developed in the past, there is a slight delay in terms of early solidification.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, the cement-based solidifying material (hereinafter, C-type) used in the high-pressure injection stirring method was applied for comparison. Based on the previous tests [7][8][9][10], the solidifying material of the A-type has a slow initial speed of solidification, and early solidification is achieved by partially replacing the fine powder of blast furnace slag with ordinary Portland cement (hereinafter, ordinary cement). This study also compared and verified whether the solidifying material of the B-type had the same properties as above.…”

Section: Specimen Mixingmentioning

confidence: 99%

“…The mixing ratio of the target soil (S) and the slurry solution (M = W + B) was unified to S/M = 2.0 in terms of the volume ratio in all the proportions. The approximate value of the quantity of each material per 1 m 3 of target soil, in the case of an actual on-site construction, is as follows: The specimen preparation process was carried out in the same manner as in [7][8][9][10] by the following procedure.…”

Section: Specimen Mixingmentioning

confidence: 99%

“…The fine powder of blast furnace slag is pulverized blast furnace granulated Materials 2021, 14, 5169 2 of 14 slag produced as a by-product in steel production. The characteristics of this solidifying material include the following items [7][8][9][10]:…”

Section: Introductionmentioning

confidence: 99%

“…In a series of studies [ 7 , 8 , 9 , 10 ], the authors developed a solidifying material for ground improvement that contributes to a reduction in the amount of waste treatment by utilizing inorganic waste as a starting material. This solidifying material is composed of the fine powder of blast furnace slag and an additive developed independently.…”

Section: Introductionmentioning

confidence: 99%

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Applicability of Additives for Ground Improvement Utilizing Fine Powder of Waste Glass

Inazumi

Shinsaka²,

Nontananandh

et al. 2021

Materials

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As a solidifying material for ground improvement using inorganic waste as a raw material, the authors have been developing an additive mixture of the fine powder of waste glass containing a large amount of silica generated during the production of glass cullet and an alkaline aid (heat-treating type of “Earth-Silica; ES” additive). Furthermore, a solidifying material that solidifies by mixing this additive with the fine powder of blast furnace slag, which is a by-product of steel production, is also being developed. In this study, the authors reviewed the mixing process of the solidified materials, especially the one made with the heat-treating type of ES additive, omitting the heat treatment of the fine powder of waste glass and the alkaline aid and applying only the mixing treatment. As a result, a mixing type of ES additive was manufactured to simplify the manufacturing process, and the difference in the performance of the solidifying material, depending on the presence or absence of the heat-treating process during the additive manufacturing, was verified in terms of the effect on the solidifying action. Specifically, the solidifying materials to which the heat-treating type of ES additive and the mixing type of ES additive were added, respectively, were applied to the high-pressure injection stirring method, one of the ground-improvement methods. Various tests clarified the changes in viscosity of these solidifying materials over time and the acceleration of their solidifying rates when adding ordinary Portland cement separately.

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Section: Discussionmentioning

confidence: 99%

Section: Specimen Mixingmentioning

confidence: 99%

Section: Specimen Mixingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Applicability of Additives for Ground Improvement Utilizing Fine Powder of Waste Glass

Inazumi

Shinsaka²,

Nontananandh

et al. 2021

Materials

Self Cite

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Experimental study on properties of liquefied stabilized soil produced with different types of solidifiers and thickeners

Shigematsu,

Inazumi,

Hashimoto

et al. 2023

Case Studies in Construction Materials

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Artificial neural networks applied for solidified soils data prediction: a bibliometric and systematic review

Pacheco

Bragagnolo

Thomé

2021

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Purpose The purpose of this article is to analyze the state-of-the art in a systematic way, identifying the main research groups and their related topics. The types of studies found are fundamental for understanding the application of artificial neural networks (ANNs) in cemented soils and the potential for using the technique, as well as the feasibility of extrapolation to new geotechnical or civil and environmental engineering segments. Design/methodology/approach This work is characterized as being bibliometric and systematic research of an exploratory perspective of state-of-the-art. It also persuades the qualitative and quantitative data analysis of cemented soil improvement, biocemented or microbially induced calcite precipitation (MICP) soil improvement by prediction/modeling by ANN. This study sought to compile and study the state of the art of the topic which possibilities to have a critical view about the theme. To do so, two main databases were analyzed: Scopus and Web of Science. Systematic review techniques, as well as bibliometric indicators, were implemented. Findings This paper connected the network between the achievements of the researches and illustrated the main application of ANNs in soil improvement prediction, specifically on cemented-based soils and biocemented soils (e.g. MICP technique). Also, as a bibliometric and systematic review, this work could achieve the key points in the absence of researches involving soil-ANN, and it provided the understanding of the lack of exploratory studies to be approached in the near future. Research limitations/implications Because of the research topic the article suggested other applications of ANNs in geotechnical engineering, such as other tests not related to geomechanical resistance such as unconfined compression test test and triaxial test. Practical implications This article systematically and critically presents some interesting points in the direction of future research, such as the non-approach to the use of ANNs in biocementation processes, such as MICP. Social implications Regarding the social environment, the paper brings approaches on methods that somehow mitigate the computational use, or elements necessary for geotechnical improvement of the soil, thereby optimizing the same consequently. Originality/value Neural networks have been studied for a long time in engineering, but the current computational power has increased the implementation for several engineering applications. Besides that, soil cementation is a widespread technique and its prediction modes often require high computational strength, such parameters can be mitigated with the use of ANNs, because artificial intelligence seeks learning from the implementation of the data set, reducing computational cost and increasing accuracy.

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Applicability of mixed solidification material based on inorganic waste as soil stabilizer

Cited by 9 publications

References 18 publications

Applicability of Additives for Ground Improvement Utilizing Fine Powder of Waste Glass

Applicability of Additives for Ground Improvement Utilizing Fine Powder of Waste Glass

Experimental study on properties of liquefied stabilized soil produced with different types of solidifiers and thickeners

Artificial neural networks applied for solidified soils data prediction: a bibliometric and systematic review

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