Utilizing Artificial Intelligence to Predict the Superplasticizer Demand of Self-Consolidating Concrete Incorporating Pumice, Slag, and Fly Ash Powders

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Due to the limitation of sample size in predicting the torsional strength of Reinforced Concrete (RC) beams, this paper aims to discuss the feasibility of employing a novel machine learning approach with K-fold cross-validation in a small sample range, which combines the advantages of a Genetic Algorithm (GA) and a Neural Network (NN) to predict the torsional strength of RC beams. This research study not only utilizes the application of a Back Propagation (BP) neural network and the Gene Algorithm-Back Propagation (GA-BP) neural network in the prediction of the torsional strength of the RC beam, but it also investigates neural network parameter optimization, including connection weights and thresholds, using K-fold cross-validation. The root mean square error (RMSE), mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE) and correlation coefficient (R2) are among the evaluation metrics used to assess the performance of the trained model. To elaborate on the superiority of the proposed network models in predicting the torsional strength of RC beams, a parametric study is conducted by comparing the proposed model to three commonly used empirical formulae from existing design codes. The comparative findings of this research study demonstrate that the performance of the BP neural network is highly similar to that of design codes; however, its accuracy is inadequate. After improving the weights and thresholds by k-fold cross-validation and GA, the prediction of the BP neural network shows higher consistency with the actual measured values. The outcome of this study can be used as a theoretical reference for the optimal design of RC beams in practical applications.

Section: Introductionmentioning

confidence: 64%

Back-Propagation Neural Network Optimized by K-Fold Cross-Validation for Prediction of Torsional Strength of Reinforced Concrete Beam

Lyu

Samali

et al. 2022

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“…When FA was used, an additional amount of the superplasticizer was required compared to the reference mixture at a substitution rate of 25%; a higher slump than that of the reference mix was observed at a substitution rate of 50%. Previous studies have shown that FA tends to decrease slump when FA replaces weight because FA density is lower than that of cement, and the specific surface area of the binder increases [44,45]. When a substitution rate of 50% was used, the ball bearing effect of using FA had a greater impact.…”

Section: Slumpmentioning

confidence: 97%

Study on Mechanical Properties of Concrete Using Basalt-Based Recycled Aggregate and Varying Curing Conditions

Choi

Park

2022

To replace porous basalt, the mechanical properties of concrete with recycled resources and durability improvement were analyzed in this study. The analysis was based on the quality improvement of recycled aggregate, use of fly ash, and changes in curing conditions. Basalt aggregate (BA) with a 3% water absorption, raw recycled aggregate and basalt (RRA), and improved recycled aggregate and basalt (PRA) were the main experimental variables. As PRA was applied to concrete, the compressive strength was lower than that of the specimen comprising BA in the normal strength region, but the modulus of elasticity (22.9 GPa) was equivalent or higher. The initial drying shrinkage increased because of the use of basalt-based recycled aggregate (B-RA). The drying shrinkage of PRA was similar to that of BA with an average difference of <7% as the age increased. The specimens subjected to steam curing exhibited the lowest drying shrinkage. These results showed that pores in the old paste of recycled aggregate increased freeze–thaw resistance because of the increase in the spacing factor. Although the PRA did not satisfy the quality criteria, the efficient use of recycled aggregate achieved an equivalent or higher performance than that of concrete comprising BA and improved durability.

“…In the PSO algorithm, particles are the building blocks of the population, and they work together to obtain the optimum approach to the target [51,62,67]. For this reason, it is called swarm intelligence.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

“…Neural networks (NNs) are a common group of techniques that are employed to predict and analyse test results [58][59][60][61]. The development of different types of NNs has led to several algorithms including artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS) and multi-layer perceptron (MLP) [62][63][64][65].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Performance of a Composite Profile at Elevated Temperatures Using Finite Element and Hybrid Artificial Intelligence Techniques

Ding¹,

Alharbi

Almadhor

et al. 2022

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It is very important to keep structures and constructional elements in service during and after exposure to elevated temperatures. Investigation of the structural behaviour of different components and structures at elevated temperatures is an approach to manipulate the serviceability of the structures during heat exposure. Channel connectors are widely used shear connectors not only for their appealing mechanical properties but also for their workability and cost-effective nature. In this study, a finite element (FE) evaluation was performed on an authentic composite model, and the behaviour of the channel shear connector at elevated temperature was examined. Furthermore, a novel hybrid intelligence algorithm based on a feature-selection trait with the incorporation of particle swarm optimization (PSO) and multi-layer perceptron (MLP) algorithms has been developed to predict the slip response of the channel. The hybrid intelligence algorithm that uses artificial neural networks is performed on derived data from the FE study. Finally, the obtained numerical results are compared with extreme learning machine (ELM) and radial basis function (RBF) results. The MLP-PSO represented dramatically accurate results for slip value prediction at elevated temperatures. The results proved the active presence of the channels, especially to improve the stiffness and loading capacity of the composite beam. Although the height enhances the ductility, stiffness is significantly reduced at elevated temperatures. According to the results, temperature, failure load, the height of connector and concrete block strength are the key governing parameters for composite floor design against high temperatures.