Development of multiple linear regression, artificial neural networks and fuzzy logic models to predict the efficiency factor and durability indicator of nano natural pozzolana as cement additive

al-Swaidani, Aref M.; Khwies, Waed T.; al-Bali, Mohamad; Lala, Tarek

doi:10.1016/j.jobe.2022.104475

Cited by 15 publications

(3 citation statements)

References 38 publications

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“…The main minerals traced in NP are Anorthite, Forstrite, Fujasite, Diopside and Calcite. NP because of its vesicular nature has a bulk density value of less than 0.7 [3] . To reach the studied sizes; namely 100 nm & 500 nm, NP was ground for 360 min and 275 min, respectively, using a laboratory centrifugal ball mill (Retsch, S100, Germany).…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Experimental data on permeability-related properties of concrete containing nano natural pozzolana as cement replacement

al-Swaidani

2022

Data in Brief

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Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Experimental data on permeability-related properties of concrete containing nano natural pozzolana as cement replacement

al-Swaidani

2022

Data in Brief

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“…In the literature, the most used data-driven model for building and material behavior is the artificial neural network (ANN) [71][72][73]. Numerous types of ANNs have been developed over the years with varying characteristics.…”

Section: Artificial Neural Network Modelingmentioning

confidence: 99%

Insight into the Behavior of Mortars Containing Glass Powder: An Artificial Neural Network Analysis Approach to Classify the Hydration Modes

et al. 2023

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In this paper, an artificial neural network (ANN) model is proposed to predict the hydration process of a new alternative binder. This model overcomes the lack of input parameters of physical models, providing a realistic explanation with few inputs and fast calculations. Indeed, four mortars are studied based on ordinary Portland cement (CEM I), cement with limited environmental impact (CEM III), and glass powder (GP) as the cement substitution. These mortars are named CEM I + GP and CEM III + GP. The properties of the mortars are characterized, and their life cycle assessment (LCA) is established. Indeed, a decrease in porosity is observed at 90 days by 4.6%, 2.5%, 12.4%, and 7.9% compared to those of 3 days for CEMI, CEMIII, CEMI + GP, and CEMIII + GP, respectively. In addition, the use of GP allows for reducing the mechanical strength in the short term. At 90 days, CEMI + GP and CEMIII + GP present a decrease of about 28% and 57% in compressive strength compared to CEMI and CEMIII, respectively. Nevertheless, strength does not cease increasing with the curing time, due to the continuous pozzolanic reactions between Ca(OH)2 and silica contained in GP and slag present in CEMIII as demonstrated by the thermo-gravimetrical (TG) analysis. To summarize, CEMIII mortar provides similar performance compared to mortar with CEMI + GP in the long term. This can later be used in the construction sector and particularly in prefabricated structural elements. Moreover, the ANN model used to predict the heat of hydration provides a similar result compared to the experiment, with a resulting R² of 0.997, 0.968, 0.968, and 0.921 for CEMI, CEMIII, CEMI + GP, and CEMIII + GP, respectively, and allows for identifying the different hydration modes of the investigated mortars. The proposed ANN model will allow cement manufacturers to quickly identify the different hydration modes of new binders by using only the heat of hydration test as an input parameter.

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“…Linear regression is used for the continuous target values. For the prediction of strength and durability of the concrete linear regression (Simple linear or Multilinear regression) is used since the strength values are continuous [15]. Logistic regression is a simple tool when modeling the dependence of binary and multipleclass response variables on one or more independent variables [16].…”

Section: Introductionmentioning

confidence: 99%

Fiber reinforced self compacting concrete workability properties prediction and optimization of mix using machine learning modeling

Periyasamy,

Kanagaraj

2024

Matéria (Rio J.)

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Self Compacting Concrete (SCC) is an engineered concrete manufactured in such a way that it can compact itself independently, without any external vibrations or equipment. The self-weight of the SCC is specifically higher than that of the Conventionally Vibrated Concrete (CVC) because of more fines in the SCC. The fines help to achieve self-compaction, but at the same time, it creates more shrinkage in the SCC. The fibers were used in the SCC to reduce these shrinkages. This investigation uses, various percentages of natural kenaf fibers such as 0.1%, 0.2%, 0.3% and 0.4%. Due to this variation in the fiber fractions, the workability properties are affected in the SCC. If the workability gets affected, the concrete does not have the self-compaction property and behaves as CVC. Hence the current research focuses on the SCC Workability Properties (WP) and optimization of SCC mix utilizing machine learning techniques. Considering the advantages of past research, a model was developed with a fusion approach that incorporates Principal Component Analysis (PCA) for SCCWP. Initially, the dataset is processed with the help of standardization using an SCC mix. The processed output is fed into principal component analysis for a dimensional shift from high to low. Then the low dimensional data is given as input to the effect of various workability properties of Fiber Reinforced Self Compacting Concrete (FRSCC) which was modeled using a Support Vector Machine (SVM) and Logistic Regression (LR). A comparison has been made, logistic regression produces a more reliable outcome compared to support vector machine in terms of all the evaluation metrics used.

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Development of multiple linear regression, artificial neural networks and fuzzy logic models to predict the efficiency factor and durability indicator of nano natural pozzolana as cement additive

Cited by 15 publications

References 38 publications

Experimental data on permeability-related properties of concrete containing nano natural pozzolana as cement replacement

Experimental data on permeability-related properties of concrete containing nano natural pozzolana as cement replacement

Insight into the Behavior of Mortars Containing Glass Powder: An Artificial Neural Network Analysis Approach to Classify the Hydration Modes

Fiber reinforced self compacting concrete workability properties prediction and optimization of mix using machine learning modeling

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