Predicting the Compressive Strength of Geopolymer Concrete: An Empirical Model for Both Recycled and Natural Aggregates

Le, Hoai-Bao; Bui, Quoc-Bao

doi:10.1007/978-981-16-7160-9_80

Cited by 6 publications

(5 citation statements)

References 4 publications

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“…Empirical models [1,2,19] An approximate compressive strength estimate is obtained based on statistical data and empirical rules [19][20][21].…”

Section: Detailed Approach Featuresmentioning

confidence: 99%

“…Then, the whole training set is used to train the classification model with optimal hyperparameters, and its predictive performance is evaluated on the test set. Regarding the proposed DT-FA model, min_sam-ples_split (within the empirical scope of [1,10]) and min_samples_leaf (within the empirical scope of [2,10]) are the hyperparameters which should be optimized. The parameter min_samples_split determines the minimum number of split samples that can continue to be separated while the parameter min_samples_leaf indicates the minimum number of leaf node samples.…”

Section: Rmse Values For Increasing Iteration Timesmentioning

confidence: 99%

“…However, the production of cement requires a large amount of energy and produces a large amount of carbon dioxide during the preparation process. Geopolymer concrete uses geopolymer materials as the main gelling agent [1][2][3][4]. Compared with traditional cement concrete, its material and preparation process have changed.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Towards a Reliable Design of Geopolymer Concrete for Green Landscapes: A Comparative Study of Tree-Based and Regression-Based Models

Wang,

Zhang,

et al. 2024

Buildings

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The design of geopolymer concrete must meet more stringent requirements for the landscape, so understanding and designing geopolymer concrete with a higher compressive strength challenging. In the performance prediction of geopolymer concrete compressive strength, machine learning models have the advantage of being more accurate and faster. However, only a single machine learning model is usually used at present, there are few applications of ensemble learning models, and model optimization processes is lacking. Therefore, this paper proposes to use the Firefly Algorithm (AF) as an optimization tool to perform hyperparameter tuning on Logistic Regression (LR), Multiple Logistic Regression (MLR), decision tree (DT), and Random Forest (RF) models. At the same time, the reliability and efficiency of four integrated learning models were analyzed. The model was used to analyze the influencing factors of geopolymer concrete and determine the strength of their influencing ability. According to the experimental data, the RF-AF model had the lowest RMSE value. The RMSE value of the training set and test set were 4.0364 and 8.7202, respectively. The R value of the training set and test set were 0.9774 and 0.8915, respectively. Therefore, compared with the other three models, RF-AF has a stronger generalization ability and higher prediction accuracy. In addition, the molar concentration of NaOH was the most important influencing factors, and its influence was far greater than the other possible factors including NaOH content. Therefore, it is necessary to pay more attention to NaOH molarity when designing geopolymer concrete.

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“…Empirical models [1,2,19] An approximate compressive strength estimate is obtained based on statistical data and empirical rules [19][20][21].…”

Section: Detailed Approach Featuresmentioning

confidence: 99%

Section: Rmse Values For Increasing Iteration Timesmentioning

confidence: 99%

See 1 more Smart Citation

Towards a Reliable Design of Geopolymer Concrete for Green Landscapes: A Comparative Study of Tree-Based and Regression-Based Models

Wang,

Zhang,

et al. 2024

Buildings

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show abstract

“…Physical specifications of RCAs are weaker than NAs. It is due to the porous structure of RCAs (Le and Bui, 2022) and the degradation of the RCA structure while crushing the parent concrete (Le et al, 2021). Besides, controlling the water content of RCA is one of the challenges in using recycled aggregate concrete in practices (Le and Bui, 2020), as their water absorption is substantially higher than NAs (Quattrone et al, 2016).…”

Section: Rca Treatmentmentioning

confidence: 99%

A review of the advanced treatment techniques for enriching the recycled concrete aggregates for recycled-based concrete: economic, environmental and technical analysis

Nikmehr

Kafle

Al-Ameri

2023

SASBE

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PurposeThis study aimed to review various existing methods for improving the quality of recycled concrete aggregates (RCAs) as a possible substitution for natural aggregates (NAs) in concrete. It is vital as the old paste attached to the RCA weakens its structure. It is due to the porous structure of the RCA with cracks, weakening the interfacial transition zone (ITZ) between the RCA and binding material, negatively impacting the concrete's properties. To this end, various methods for reinforcement of the RCA, cleaning the RCA's old paste and enhancing the quality of the RCA-based concrete without RCA modification are studied in terms of environmental effects, cost and technical matters. Furthermore, this research sought to identify gaps in knowledge and future research directions.Design/methodology/approachThe review of the relevant journal papers revealed that various methods exist for improving the properties of RCAs and RCA-based concrete. A decision matrix was developed and implemented for ranking these techniques based on environmental, economic and technical criteria.FindingsThe identified methods for reinforcement of the RCA include accelerated carbonation, bio deposition, soaking in polymer emulsions, soaking in waterproofing admixture, soaking in sodium silicate, soaking in nanoparticles and coating with geopolymer slurry. Moreover, cleaning the RCA's old paste is possible using acid, water, heating, thermal and mechanical treatment, thermo-mechanical and electro-dynamic treatment. Added to these treatment techniques, using RCA in saturated surface dry (SSD) mixing approaches and adding fibres or pozzolana enhance the quality of the RCA-based concrete without RCA modification. The study ranked these techniques based on environmental, economic and technical criteria. Ultimately, adding fibres, pozzolana and coating RCA with geopolymer slurry were introduced as the best techniques based on the nominated criteria.Practical implicationsThe study supported the need for better knowledge regarding the existing treatment techniques for RCA improvement. The outcomes of this research offer an understanding of each RCA enrichment technique's importance in environmental, economic and technical criteria.Originality/valueThe practicality of the RCA treatment techniques is based on economic, environmental and technical specifications for rating the existing treatment techniques.

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“…These activators while reacting with silica, alumina and calcium form C-S-H gel. Consequently, the mechanical Characteristics of GPC are governed by different varying parameters such as Mix Proportion and Quality of the raw materials, Curing state (Oven Curing and Ambient curing), Alkaline activators involved (5)(6)(7) . https://www.indjst.org/ The mechanical properties and elastic properties of GPC have been examined by some researchers in order to utilize it effectively (8)(9)(10) .…”

Section: Introductionmentioning

confidence: 99%

Experimental Evaluation of the Mechanical Performance of Geo Polymer Concrete

Kumar,

Johnson,

Kumaran

2023

IJST

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Objectives: To determine the important Mechanical properties and Elastic modulus of Geopolymer concrete (GPC). Methods: Four mix proportions of GPC using two molarities (12 and 14) of NaOH have been used to achieve M25 grade of GPC. Compressive strength, Tensile strength, Flexural strength and Young's Modulus (E GPC ) tests have been performed as per Codal provisions. Findings: The modulus of elasticity of GPC (E GPC ) for the four mixes has been determined experimentally and the average of four values was noted. With the help of this value,5000 random variables have been generated using Monte Carlo Technique and the average E GPC (for 5000 values) has also been determined. Finally, it has been perceived that the elastic modulus of GPC is nearly equal to that of OPC in contrast to the previous studies.

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Predicting the Compressive Strength of Geopolymer Concrete: An Empirical Model for Both Recycled and Natural Aggregates

Cited by 6 publications

References 4 publications

Towards a Reliable Design of Geopolymer Concrete for Green Landscapes: A Comparative Study of Tree-Based and Regression-Based Models

Towards a Reliable Design of Geopolymer Concrete for Green Landscapes: A Comparative Study of Tree-Based and Regression-Based Models

A review of the advanced treatment techniques for enriching the recycled concrete aggregates for recycled-based concrete: economic, environmental and technical analysis

Experimental Evaluation of the Mechanical Performance of Geo Polymer Concrete

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