Systematic multiscale models to predict the compressive strength of self-compacting concretes modified with nanosilica at different curing ages

Faraj, Rabar H.; Mohammed, Azad A.; Mohammed, Ahmed; Omer, Khalid M.; Ahmed, Hemn Unis

doi:10.1007/s00366-021-01385-9

Cited by 50 publications

(32 citation statements)

References 127 publications

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“…The larger group of a dataset, which included 340 datasets, was used to create the models. The second group consists of 85 datasets used to test the proposed models, and the last group, which includes 85 datasets, was used to validate the provided models [57,69]. The data collection, comprehensive review, and modeling work are summarized in a flow chart, as depicted in Fig.…”

Section: Research Significancementioning

confidence: 99%

“…The models suggested in this paper are used to forecast the fc′ of the FA-GPC and choose the optimum solution that delivers a better estimate of fc′ than the experimentally determined fc′. All the collected datasets were randomly split into three parts: training, testing,, and validating datasets [57,69]. 340 training dataset is used to train the LR, MLR, ANN, and M5P-tree model and obtain the optimal weights and biases, while 85 testing dataset is used to confirm the fulfillment of the proposed models.…”

Section: Modelingmentioning

confidence: 99%

“…One of the most common methods to predict the fc′ of concrete is the linear regression model (LR) [166], as shown in Eq.1, and it is considered as a general form of the linear regression model [66,69,165].…”

Section: Linear Regression Model (Lr)mentioning

confidence: 99%

“…Same as the former models, a multi-logistic regression analysis model was carried out for the collected datasets, and the general form of the MLR is shown in Eq.3 based on the research studied that had been conducted by Mohammed et al [66] and Faraj et al [69]. MLR is used to clarify the difference between a nominal predictor variable and one or more independent variables.…”

Section: Multilogistic Model (Mlr)mentioning

confidence: 99%

“…Due to the good ability of machine learning regarding prioritization, optimization, forecasting and planning were widely used in the various engineering fields [57]. In the literature, machine learning systems were used to model the various characteristics of different types of concrete composites such as compression strength of green concrete [62], splitting tensile and flexural strength of recycled aggregate concrete [63], modulus of elasticity of recycled concrete aggregate [64,65], the fc′ of high volume fly ash concrete [66], the fc′ of eco-friendly GPC containing natural zeolite and silica fume [67], splitting tensile strength of fiber-reinforced concrete [68], the fc′ of self-compacting concrete modified with nanosilica [69], and so on.…”

Section: Introductionmentioning

confidence: 99%

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Compressive Strength of Geopolymer Concrete Composites: Modeling and Comprehensive Systematic Review

Ahmed

Mohammed

2021

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The growing concern about global climate change and its adverse impacts on societies is putting severe pressure on the construction industry as one of the largest producers of greenhouse gases. Given the environmental issues associated with cement production, geopolymer concrete has emerged as a sustainable construction material. Geopolymer concrete is cementless concrete that uses industrial or agro by-product ashes as the main binder instead of ordinary Portland cement; this leads to being an eco-efficient and environmentally friendly construction material. Compressive strength is one of the most important mechanical property for all types of concrete composites including geopolymer concrete, and it is affected by several parameters like an alkaline solution to binder ratio (l/b), fly ash (FA) content, SiO2/Al2O3 (Si/Al) of the FA, fine aggregate (F) and coarse aggregate (C) content, sodium hydroxide (SH) and sodium silicate (SS) content, ratio of sodium silicate to sodium hydroxide (SS/SH), molarity (M), curing temperature (T), curing duration (CD) inside the oven and specimen ages (A). In this regard, a comprehensive systematic review was carried out to show the effect of these different parameters on the compressive strength of the fly ash-based geopolymer concrete (FA-GPC). In addition, multi-scale models such as Artificial Neural Network (ANN), M5P-tree (M5P), Linear Regression (LR), and Multi-logistic Regression (MLR) models were developed to predict the compressive strength of FA-GPC composites. For the first time, in the modeling process, twelve effective parameters including l/b, FA, Si/Al, F, C, SH, SS, SS/SH, M, T, CD, and A were considered the modeling input parameters. Then, the efficiency of the developed models was assessed by various statistical assessment tools like Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), OBJ value, and the Coefficient of determination (R2). Results show that the curing temperature, sodium silicate content, and ratio of the alkaline solution to the binder content are the most significant independent parameters that influence on the compressive strength of the FA-GPC, and the ANN model has better performance for predicting the compressive strength of FA-GPC in compared to the other developed models.

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Section: Research Significancementioning

confidence: 99%

Section: Modelingmentioning

confidence: 99%

Section: Linear Regression Model (Lr)mentioning

confidence: 99%

Section: Multilogistic Model (Mlr)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Compressive Strength of Geopolymer Concrete Composites: Modeling and Comprehensive Systematic Review

Ahmed

Mohammed

2021

Preprint

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Multifunctional computational models to predict the long‐term compressive strength of concrete incorporated with waste steel slag

Piro

Mohammed

Hamad

et al. 2022

Structural Concrete

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To preserve the environment and natural resources, steel slag recovery conserves natural resources and makes landfill space available. Steel slag as a waste material has been partially substituted for fine (sand) and coarse aggregate in concrete (gravel). Compressive strength (CS) is the most significant mechanical attribute for all forms of concrete composites. To save time, energy, and money, it is essential to create accurate models for forecasting the CS of normal concrete (NC). In addition, it offers essential information for organizing the building work and details the ideal time to remove the formwork. In total, 338 data points were gathered, processed, and modeled in total. During the modeling approach, the most influential elements impacting the compressive strength (CS) of concrete with steel slag replacement were addressed. According to the modeling method, the most effective parameter which affects the compressive strength of normal concrete is the curing time. This research employed a Multi Logistic Regression model (MLR), an Artificial Neural Network (ANN), a Full Quadratic model (FQ), an M5P‐tree model, and an Interaction model to predict the compressive strength of normal strength concrete (CS ranged from 10 to 55 MPa) with steel slag aggregate replacement. According to data from the literature, the steel slag concentration enhanced the compressive strength. Based on evaluations with statistical tools like the objective (OBJ) function, the scatter index, and the Taylar diagram, the ANN model with the lowest root mean square error did better at predicting compressive strength than the other models.

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Recent developments in utilization of nano silica in self‐compacting concrete: A review

Wang

Yang

Chu

et al. 2023

Structural Concrete

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Self‐compacting concrete (SCC) is a type of flowable, high‐strength and high‐performance concrete in the construction field, which can flow and compact under its own weight. The damage of concrete is often caused by internal microstructural defects. Nano silica (NS) has excellent pozzolanic properties, and with an appropriate content of NS as auxiliary cementing material, it can promote the generation of C‐S‐H gels in addition to serve as internal filler, thus improves the micro‐scale defects in concrete. This paper extracts data from studies published over the last decade which focus on the application of NS in SCC and discusses the similarities and differences between the studies. Ultimately, a systematic review of the physical and chemical characteristics of NS, and the effect of NS on the workability, mechanical and durability properties of SCC is presented. Based on the review, the most common NS content is 0%–6%, where the negative effect of 0%–6% NS on the workability of SCC can be controlled by increasing the dosage of superplasticizer. The NS content of 2%–3% is optimum for improving the mechanical strength and the ability of SCC to resist water permeability, corrosion, acid and sulfate attacks, elevated temperature as well as shrinkage.

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Systematic multiscale models to predict the compressive strength of self-compacting concretes modified with nanosilica at different curing ages

Cited by 50 publications

References 127 publications

Compressive Strength of Geopolymer Concrete Composites: Modeling and Comprehensive Systematic Review

Compressive Strength of Geopolymer Concrete Composites: Modeling and Comprehensive Systematic Review

Multifunctional computational models to predict the long‐term compressive strength of concrete incorporated with waste steel slag

Recent developments in utilization of nano silica in self‐compacting concrete: A review

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