An Artificial Neural Networks Model for Predicting Permeability Properties of Nano Silica–Rice Husk Ash Ternary Blended Concrete

Najigivi, Alireza; Khaloo, Alireza; zad, Azam Iraji; Rashid, Suraya Abdul

doi:10.1007/s40069-013-0038-z

Cited by 23 publications

(5 citation statements)

References 27 publications

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“…Nowadays, artificial intelligence computational tools such as artificial neural networks (ANN) are widely regarded by researchers worldwide owing to their efficiency in pattern recognition and fitting complex datasets [38]. This prediction method has proven to be much more accurate than the theoretical models and other conventional statistical methods [53,54]. The authors have successfully employed ANN in the earlier studies reporting the prediction of bond strength of SCGC when reinforced with Basalt FRP bars [38].…”

Section: Artificial Neural Network (Ann) For Prediction Of Sorptivity...mentioning

confidence: 99%

Experimental and Artificial Neural Network-Based Study on the Sorptivity Characteristics of Geopolymer Concrete with Recycled Cementitious Materials and Basalt Fibres

Rahman

Al-Ameri

2022

Recycling

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The environmental concerns regarding the production of the most widely consumed cement construction material have led to the need for developing sustainable alternatives. Using recycled industry waste products such as fly ash and slag via geopolymerisation has led to the development of geopolymer cement—an efficient replacement for ordinary Portland cement (OPC). Adopting geopolymer cement and concrete as a construction material reduces greenhouse gas and promotes the recycling of waste products. This study explores the suitability of a unique geopolymer concrete mix made of recycled cementitious materials including industry waste products such as fly ash, micro fly ash and slag for use in aggressive environments. Sorptivity tests are conducted to assess the durability of concrete and indicate the cementitious material’s ability to transmit water through the capillary forces. This study thus reports on the sorptivity characteristics of a newly developed self-compacting geopolymer concrete and two other fibre geopolymer concrete mixes containing 1% (by weight) of 12 mm- or 30 mm-long basalt fibres. The addition of basalt fibres indicated less water absorption and moisture ingress than the mix without fibres. The study used 18 specimens from three geopolymer concrete mixes, and the results showed that adding fibres improved the durability performance in terms of resistance to moisture ingress. Finally, an artificial neural network model is developed to predict the absorption rates of geopolymer concrete specimens using MATLAB. The prediction models reported excellent agreement between experimental and simulated datasets.

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Section: Artificial Neural Network (Ann) For Prediction Of Sorptivity...mentioning

confidence: 99%

Experimental and Artificial Neural Network-Based Study on the Sorptivity Characteristics of Geopolymer Concrete with Recycled Cementitious Materials and Basalt Fibres

Rahman

Al-Ameri

2022

Recycling

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“…It is very interesting to introduce the closed-loop schemes as an alternative of traditional insulin injection method which can endlessly monitor the level of blood sugar and release insulin as soon as needing. The several benefits of this system over the traditional methods can be nominated as: 1) better control of BGLs, which caused in the reduction of the problems of the diabetic patients [39,40,99,100] and also 2) this system could lead to reduction of the dosage of consumed insulin, as well as 3) reduction of the amount of hypoglycemic and hyperglycemic proceedings. These compensations have led to substantial attention in developing the mentioned close-loop schemes.…”

Section: The Technology Of Closed-loop Insulin Delivery By Aid Of Nanotechnologymentioning

confidence: 99%

Contribution of Nanotechnology and Nanomaterials to the Treatment of Diabetic Patients by Aid of Novel Inventions

Najigivi

Mirmotallebi

Najigivi

2020

AJOCS

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Nanoscience and Nanotechnology are highly growing their significance in diabetic supplies and research nowadays. It is an area that included nanomaterials, nanosensors and nanostructures as well as nanoparticle projects and also their usage in human health research. Particularly, nanotechnology helps to the production of diabetic supplies, materials together with the development of novel glucose and insulin injection devices as well as their measurement sensors by the aid of nanomaterials. These materials mostly could be metal nanoparticles together with carbon nanostructures by nano dimension delivery mechanisms modalities which hold the potential to vividly recover the excellence of life of diabetic patients. Nanoscience and nanotechnology in diabetic research have facilitated and provided more truthful data for identifying diabetes mellitus. It is also worth mentioning that the nanotechnology could highly enhance the impact of drug delivery by addition of nanoscale materials and increase the glucose feeling, temporal response as well as glucose nursing. Furthermore, it is proposing novel nanoscale methods named closed-loop insulin delivery approaches which mechanically release insulin drug in reply to fluctuating blood glucose heights. Besides, the mixture of nanotechnology by medication has shaped a novel field of nanomedicine which could enhance human health level. It is worthwhile to mention that some of the applications of nanotechnology for the treatment of diabetic patients can be the production of diabetic supplies by nanotechnology. One of the most important diabetic instruments that could highly relieve the life of patients these days could be nano diabetic shoes which will describe here. In this research, applications of nanoscience and nanotechnology in treating diabetic patients were discussed.

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“…The results show that the ANN model can predict the fresh properties of SCC accurately. Najigivi et al (2013) have predicted the permeability property of ternary blended concrete with ANN tool. Their study reveals that the ANN model has a strong capacity to predict the ternary blended concrete properties.…”

Section: Introductionmentioning

confidence: 99%

Prediction of shear strength of concrete produced by using pozzolanic materials and partly replacing NFA by MS using ANN

Mane¹,

Kulkarni²,

Prakash³

2020

JEDT

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Purpose The use of huge quantity of natural fine aggregate (NFA) and cement in civil construction work which have given rise to various ecological problems. The industrial waste like blast furnace slag (GGBFS), fly ash, metakaolin and silica fume can be partly used as a replacement for cement and manufactured sand obtained from crusher and partly used as fine aggregate. The purpose of this paper is to predict the shear strength of concrete using artificial neural network (ANN) for concrete made by using different pozzolans and partly replacing NFA by manufactured sand (MS) which can reduce the time and experimental cost. Design/methodology/approach In this work, MATLAB software model is developed using neural network toolbox to predict the shear strength of concrete made by using pozzolanic materials and partly replacing NFA by manufactured sand (MS). Shear strength was experimentally calculated, and results obtained from experiment were used to develop the ANN model. A total of 131 results values were used to modeling formation, and from that 30% data record was used for testing purpose and 70% data record was used for training purpose. In total, 25 input materials properties were used to find the 28 days shear strength of concrete obtained from partly replacing cement with pozzolans and partly replacing NFA by manufactured sand (MS). Findings The results obtained from ANN model provide very strong accuracy to predict shear strength of concrete obtained from partly replacing cement with pozzolans and NFA by manufactured sand. Originality/value This research study is on determining shear strength of concrete using ANN. The use of this study is to predict the shear strength of concrete using ANN for concrete made by using different pozzolans and partly replacing NFA by manufactured sand (MS) which can reduce the time and experimental cost.

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An Artificial Neural Networks Model for Predicting Permeability Properties of Nano Silica–Rice Husk Ash Ternary Blended Concrete

Cited by 23 publications

References 27 publications

Experimental and Artificial Neural Network-Based Study on the Sorptivity Characteristics of Geopolymer Concrete with Recycled Cementitious Materials and Basalt Fibres

Experimental and Artificial Neural Network-Based Study on the Sorptivity Characteristics of Geopolymer Concrete with Recycled Cementitious Materials and Basalt Fibres

Contribution of Nanotechnology and Nanomaterials to the Treatment of Diabetic Patients by Aid of Novel Inventions

Prediction of shear strength of concrete produced by using pozzolanic materials and partly replacing NFA by MS using ANN

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