Investigation into the permeability and strength of pervious geopolymer concrete containing coated biomass aggregate material

Arafa, Salaheddin; Milad, Abdalrhman; Yusoff, Nur Izzi Md.; Al‐Ansari, Nadhir; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher

doi:10.1016/j.jmrt.2021.09.045

Cited by 22 publications

(13 citation statements)

References 37 publications

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“…Their predictive ability was evaluated and compared using statistical checks and R 2 values, and the results were published in JML. The selection of two individual ML techniques (DT and GEP) and two ensemble ML algorithms (BR and RF) was due to their popularity in giving Contemporary innovations in artificial intelligence (AI) have explained the wide application of supervised machine learning (SML) approaches for forecasting the characteristics of several materials [37][38][39][40][41][42][43]. Ahmad et al [14] performed a comparative study on various SML approaches, i.e., decisions tree (DT), AdaBoost, and bagging regressor (BR), to forecast the compressive strength (CS) of GPC incorporating fly ash.…”

Section: Alkali Activator Aluminosilicatementioning

confidence: 99%

“…[10]. Contemporary innovations in artificial intelligence (AI) have explained the wide application of supervised machine learning (SML) approaches for forecasting the characteristics of several materials [37][38][39][40][41][42][43]. Ahmad et al [14] performed a comparative study on various SML approaches, i.e., decisions tree (DT), AdaBoost, and bagging regressor (BR), to forecast the compressive strength (CS) of GPC incorporating fly ash.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Artificial Intelligence Methods to Estimate the Compressive Strength of Geopolymers

Zou

Zheng

Alzahrani

et al. 2022

Gels

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The depletion of natural resources and greenhouse gas emissions related to the manufacture and use of ordinary Portland cement (OPC) pose serious concerns to the environment and human life. The present research focuses on using alternative binders to replace OPC. Geopolymer might be the best option because it requires waste materials enriched in aluminosilicate for its production. The research on geopolymer concrete (GPC) is growing rapidly. However, substantial effort and expenses are required to cast specimens, cures, and tests. Applying novel techniques for the said purpose is the key requirement for rapid and cost-effective research. In this research, supervised machine learning (SML) techniques, including two individual (decision tree (DT) and gene expression programming (GEP)) and two ensembled (bagging regressor (BR) and random forest (RF)) algorithms were employed to estimate the compressive strength (CS) of GPC. The validity and comparison of all the models were made using the coefficient of determination (R2), k-fold, and statistical assessments. It was noticed that the ensembled SML techniques performed better than the individual SML techniques in forecasting the CS of GPC. However, individual SML model results were also in the reasonable range. The R2 value for BR, RF, GEP, and DT models was 0.96, 0.95, 0.93, and 0.88, respectively. The models’ lower error values such as mean absolute error (MAE) and root mean square errors (RMSE) also verified the higher precision of ensemble SML methods. The RF (MAE = 2.585 MPa, RMSE = 3.702 MPa) and BR (MAE = 2.044 MPa, RMSE = 3.180) results are better than the DT (MAE = 4.136 MPa, RMSE = 6.256 MPa) and GEP (MAE = 3.102 MPa, RMSE = 4.049 MPa). The application of SML techniques will benefit the construction sector with fast and cost-effective methods for estimating the properties of materials.

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Section: Alkali Activator Aluminosilicatementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of Artificial Intelligence Methods to Estimate the Compressive Strength of Geopolymers

Zou

Zheng

Alzahrani

et al. 2022

Gels

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“…A pervious concrete is achieved by carefully controlling the quantity of water and cementitious materials to generate a thick coating paste with a substantial void content and highly permeable and interconnecting voids that can drain the surface runoff very quickly, which thereby preserves the service life of road pavement [ 1 , 2 ]. Pervious concrete applications for road pavement provide an effective and unique approach to address important environmental challenges in support of sustainable and green infrastructural development by providing a systematic technique to capture and convey storm water, allowing it to seep into the ground [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Scheffe’s Simplex Optimization of Flexural Strength of Quarry Dust and Sawdust Ash Pervious Concrete for Sustainable Pavement Construction

et al. 2023

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Pervious concrete provides a tailored surface course with high permeability properties which permit the easy flow of water through a larger interconnected porous structure to prevent flooding hazards. This paper reports the modeling of the flexural properties of quarry dust (QD) and sawdust ash (SDA) blended green pervious concrete for sustainable road pavement construction using Scheffe’s (5,2) optimization approach. The simplex mixture design method was adapted to formulate the mixture proportion to eliminate the set-backs encountered in empirical or trials and the error design approach, which consume more time and resources to design with experimental runs required to evaluate the response function. For the laboratory evaluation exercise, a maximum flexural strength of 3.703 N/mm2 was obtained with a mix proportion of 0.435:0.95:0.1:1.55:0.05 for water, cement, QD, coarse aggregate and SDA, respectively. Moreover, the minimal flexural strength response of 2.504 N/mm2 was obtained with a mix ratio of 0.6:0.75:0.3:4.1:0.25 for water, cement, QD, coarse aggregate and SDA, respectively. The test of the appropriateness of the developed model was statistically verified using the Student’ t-test and an analysis of variance (ANOVA), and was confirmed to be acceptable based on computational outcomes at the 95% confidence interval. Furthermore, the scanning electron microscopy (SEM) and energy dispersive X-ray (EDX) were used to evaluate the morphological and mineralogical behavior of green prior concrete samples with various additive mixture compositions. The addition of QD and SDA, on the other hand, aided the creation of porous microstructures in the concrete matrix due to fabric changes in the concrete mixture, potentially aided by the formation of cementitious compounds such as calcium aluminate hydrate and calcium silicate hydrate.

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“…Workability and compressive strength, are important for basic structural light-weight concrete [9]. Concrete is one of the most often used materials in the building sector [10].…”

Section: Introductionmentioning

confidence: 99%

Enhancing of Concrete Properties by Using Aluminium and Iron Residues as a Partial Replacement of Fine Aggregate

Hammadi¹,

Mohammed²,

Ramal³

2022

ACSM

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According to the growing of the world's population, the necessitate for using different building materials such as cement, steel, admixtures, wood and aluminium increased. The main problem faces the world with the increasing need for building materials is the construction wastes, which are a type of environmental pollution that must be reduced. This study focused on aluminium and iron wastes resulted from factories and the possibility to reuse these wastes again like partial replacement from sand with proportions (0.25, 0.5, 0.75 and 1%) in concrete mixtures for both types of residues at ages (7, 14, and 28) days. It had studied the effect of the using of aluminium residues (AR) and iron residues (IR) especially on the mechanical characteristics of concrete. These mechanical characteristics were the compression strength (CS) and tensile strength (TS). The achieved results were compared between the residues concrete specimens and those of reference concrete. Also, the effect of age and replacement percentage for samples which they contain AR and IR had been discussed. The results had shown that the (CS) of iron residues concrete samples (IRC) was increasing with increasing of the iron residues percentage till (0.5%) (IR) in opposite of the observed values of aluminium residues concrete (ARC), as well as for the results of the (TS) of concrete samples which containing the mineral residues at all ages. Where it was observed that the using of aluminium residues (AR) showed a decreasing in the values of (TS) and (CS) compare with reference concrete, but with the use of iron residues (IR) both of (TS) and (CS) results were more than their counterparts of reference concrete at all ages for the first two percentages of replacement.

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Investigation into the permeability and strength of pervious geopolymer concrete containing coated biomass aggregate material

Cited by 22 publications

References 37 publications

Evaluation of Artificial Intelligence Methods to Estimate the Compressive Strength of Geopolymers

Evaluation of Artificial Intelligence Methods to Estimate the Compressive Strength of Geopolymers

Scheffe’s Simplex Optimization of Flexural Strength of Quarry Dust and Sawdust Ash Pervious Concrete for Sustainable Pavement Construction

Enhancing of Concrete Properties by Using Aluminium and Iron Residues as a Partial Replacement of Fine Aggregate

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