The prediction and evaluation of recycled polypropylene fiber and aggregate incorporated foam concrete using Artificial Neural Networks

Yildizel, Sadik Alper; Uzun, Mehmet; Arslan, Mehmet Akif; Ozbakkaloglu, Togay

doi:10.1016/j.conbuildmat.2023.134646

Cited by 2 publications

(1 citation statement)

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“…In general, increasing the number of input variables of a neural network can result in better prediction results, and the condition of input variables has a good correlation with the output variables. However, some inputs may be irrelevant or contribute no information to the output, and may introduce system noise, fool the training algorithm and degrade the performance of the model [30,31]. Therefore, there is an urgent problem to be solved about the sensitivity analysis of input variables.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of Influencing Factors and Two-Stage Prediction of Frost Resistance of Active-Admixture Recycled Concrete Based on Grey Theory–BPNN

Fu,

2024

Materials

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Sensitivity analysis of influencing factors on frost resistance is carried out in this paper, and a two-stage neural network model based on grey theory and Back Propagation Neural Networks (BPNNs) is established for the sake of predicting the frost resistance of active-admixture recycled concrete quickly and accurately. Firstly, the influence degree of cement, water, sand, natural aggregate, recycled aggregate, mineral powder, fly ash, fiber and air-entraining agent on the frost resistance of active-admixture recycled-aggregate concrete was analyzed based on the grey system theory, and the primary and secondary relationships of various factors were effectively distinguished. Then, the input layer of the model was determined as cement, water, sand, recycled aggregate and air-entraining agent, and the output layer was the relative dynamic elastic modulus. A total of 120 datasets were collected from the experimental data of another author, and the relative dynamic elastic modulus was predicted using the two-stage BPNN prediction model proposed in this paper and compared with the BPNN prediction results. The results show that the proposed two-stage BPNN model, after removing less-sensitive parameters from the input layer, has better prediction accuracy and shorter run time than the BPNN model.

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Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of Influencing Factors and Two-Stage Prediction of Frost Resistance of Active-Admixture Recycled Concrete Based on Grey Theory–BPNN

Fu,

2024

Materials

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Using meta-heuristic optimization in ANFIS models to estimate compressive strength for recycled aggregate concrete

Hong,

Wang

2024

Multiscale and Multidiscip. Model. Exp. and Des.

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The prediction and evaluation of recycled polypropylene fiber and aggregate incorporated foam concrete using Artificial Neural Networks

Cited by 2 publications

References 53 publications

Sensitivity Analysis of Influencing Factors and Two-Stage Prediction of Frost Resistance of Active-Admixture Recycled Concrete Based on Grey Theory–BPNN

Sensitivity Analysis of Influencing Factors and Two-Stage Prediction of Frost Resistance of Active-Admixture Recycled Concrete Based on Grey Theory–BPNN

Using meta-heuristic optimization in ANFIS models to estimate compressive strength for recycled aggregate concrete

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