Prediction of compressive strength in plain and blended cement concretes using a hybrid artificial intelligence model

Al-Jamimi, Hamdi A.; Al-Kutti, Walid A.; Alwahaishi, Saleh; Alotaibi, Khalid Saqer

doi:10.1016/j.cscm.2022.e01238

Cited by 8 publications

(3 citation statements)

References 62 publications

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“…Concrete is a complex engineering material with different materials and design variables. Therefore, an accurate prediction of the compressive strength of concrete must consider its nonlinearity [23]. The compressive strength of the concrete cube to be achieved is based on the design results of 24.5 MPa or 250 Kg/cm2.…”

Section: Compressive Strength Planmentioning

confidence: 99%

Characteristics of Concrete With Rice Husk Ash Local Kutai Kartanegara

Syahrul

2022

JTSP

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Abstract. Concrete material made from natural waste was developed to balance the use of concrete materials resulting from natural exploration with an impact on environmental damage, rice husk is a waste material from rice mills and contains reactive silica and has the benefit of reducing energy consumption in cement production. Laboratory test method in the form of testing the compressive characteristics used standard cube concrete 150 mm x 150 mm x 150 mm with a total sample of 24 samples. The effect of concrete strength on the amount of cement replacement is 0%; 5%; 10% ; and 15% which could be compared with concrete without using husk ash. The compressive strength obtained in terms of the age of the concrete is 28 days. The mechanical properties of husk ash at the age of 28 days gave optimal compressive strength results with the addition of 5% husk ash.

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Section: Compressive Strength Planmentioning

confidence: 99%

Characteristics of Concrete With Rice Husk Ash Local Kutai Kartanegara

Syahrul

2022

JTSP

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“…23,30 However, accurate modelling for structured sand with inherent anisotropy is not considered yet and deserves to be further explored. Existing surrogate models are merely used to predict the strength of concrete, [31][32][33] cemented sand 13 and slope stability prediction considering inherent anisotropy. 34,35 Conventional data-driven approaches often require numerous data to ensure desirable accuracy and generalization ability.…”

Section: Introductionmentioning

confidence: 99%

A multi‐fidelity residual neural network based surrogate model for mechanical behaviour of structured sand

Zhou,

Yin,

et al. 2024

Num Anal Meth Geomechanics

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The structured sand presents significant interparticle bonding and anisotropy, resulting in significant differences in the physical and mechanical properties from the pure sand. This study proposes a new surrogate model based on the concept of multi‐fidelity residual neural network (MR‐NN) as an alternative to DEM simulation for predicting the mechanical behaviours of structured sand with different initial anisotropy and saving largely computational costs. The model is initially trained using low‐fidelity (LF) data to focus on capturing the main underpinning correlations between macroscopic mechanical parameters with inter‐particle properties and anisotropic state variables (i.e., microscopic fabric and tilting angle of sand), where the LF data are generated from previously proposed anisotropic macro‐micro quantitative correlation. Subsequent training on sparser high‐fidelity (HF) data is used to calibrate and refine the model with HF data generated from DEM simulations for anisotropic structured sand. Feedforward neural network (FNN) is adopted as the baseline algorithm for training models. The macroscopic anisotropic parameters of structured sand predicted by the surrogate model are compared with DEM simulations to examine its feasibility and generalization ability. Furthermore, the robustness of the surrogate model is examined by discussing the effect of LF data on the performance of MR‐NN. The superiority of the MR‐NN is further verified by comparing the performance of the trained MR‐NN with the one‐shot trained FNN based on the same HF data. All results demonstrate that the proposed surrogate model can provide a fast and accurate simulation of the anisotropic parameters of structured sand.

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“…Ma et al [8], Xu et al [9], and Jovic et al [10] used the waterbinder ratio as well as the contents of cement, coal gangue powder, lithium slag, water reducer, and coarse and fine aggregates as input variables, and the BP neural network model, the multivariate regression-based model, and the adaptive neural fuzzy inference system are used individually to predict the compressive strength of stone powder concrete, lithium slag concrete, and silica fumes. Al-Jamimi et al [11] got a conclusion that support vector machine (SVM) and genetic algorithm (GA) as the mixed model (SVM GA) had the best effect on the prediction of concrete compressive strength. Naser et al [12] proposed multivariate adaptive regression splines (MARS) to predict the compressive strength of ecofriendly concrete.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Compressive Strength of Concrete Using the Spearman and PCA-Based BP Neural Network

Wang

Zhao

Zhang³

et al. 2022

Wireless Communications and Mobile Computing

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Using real-time production data of concrete to predict its 28-day compressive strength has great significance for improving the engineering structure quality and overcoming the shortage of the traditional tests long period of concrete compressive strength. The current research has the shortcomings such as insufficient prediction accuracy, inadequate matching between data characteristics and model characteristics, and redundant input parameter information. This paper proposes a BP neural network prediction model optimized by Spearman and PCA. The model first uses Spearman method to reduce the number of the input variables by eliminating variables that have a low correlation with the compressive strength and then uses PCA to eliminate the correlation between material-related variables. Following this, the new uncorrelated input variables optimized by Spearman and PCA are input to the BP neural network-based model to predict the compressive strength of concrete. The results showed that it yielded the mean absolute percentage error (MAPE) of 2.78% and the root mean-squared error (RMSE) of 1.66 MPa, far lower than the error of 4.82% and 2.92 MPa obtained by the nonoptimized BP neural network, respectively. The proposed model fully exploits real-time monitoring data from the concrete mixing station, and its results are close to those of traditional tests. It has great practical significance to guide the concrete production and construction, shorten the production cycle, and reduce the project cost.

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Prediction of compressive strength in plain and blended cement concretes using a hybrid artificial intelligence model

Cited by 8 publications

References 62 publications

Characteristics of Concrete With Rice Husk Ash Local Kutai Kartanegara

Characteristics of Concrete With Rice Husk Ash Local Kutai Kartanegara

A multi‐fidelity residual neural network based surrogate model for mechanical behaviour of structured sand

Prediction of Compressive Strength of Concrete Using the Spearman and PCA-Based BP Neural Network

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