Optimal Design of Ecological Concrete Mix Proportion Based on AHP-GWO-BP Neural Network

Guangyao, Dong; Songmin, Li; Xiaoling, Wang; Yazhi, Zheng; Qihang, Wang

doi:10.1007/s41742-023-00562-6

Cited by 3 publications

(1 citation statement)

References 41 publications

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“…Other manufacturing methods can be implemented to improve the durability of hemp concrete, such as the use of natural pozzolan and microsilica as partial substitutes for lime in hemp concrete mixes [10,37]. A study presented an optimization model to determine the optimal mixture proportions for different types of eco-friendly concrete, using the Grey Wolf optimizer and a backpropagation neural network [38]. These works demonstrated the efficiency of the model in calculating optimal mixture proportions and highlighted the performance of eco-friendly concrete under specific parameters.…”

Section: Introductionmentioning

confidence: 99%

Hygrothermal Performance of the Hemp Concrete Building Envelope

Kaboré,

Maref,

Ouellet-Plamondon

2024

Energies

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The search for environmentally friendly and low-carbon-footprint construction materials continues progressively. Researchers are now interested in innovative materials that connect with the principles of sustainable construction, and materials such as hemp concrete prove to be promising. This article presents the results of a study that aimed to evaluate the hygrothermal performance of hemp concrete integrated into the building envelope using the hygrothermal tool WUFI Pro 6.2. The simulation model was compared and verified with existing models before its utilization for this study. The results of this verification were in good agreement, which gave us more confidence in its application for further parametric studies of building envelopes in hot climate zones. Three wall systems were simulated: (i) a wall system with hemp concrete, (ii) a compressed earth block wall, and (iii) a cement block wall. The most important variables used in the simulations were the hygrothermal properties of the materials or wall components and the incident solar radiation. The simulation results showed that hemp concrete has good thermal performance and temperature and humidity regulation capabilities of the building envelope. The interior surface temperatures of the hemp concrete walls were between 22.1 °C and 24.6 °C compared to the compressed earth block and cement block walls, where the surface temperatures were between 22.0 °C and 27 °C and between 21.2 °C and 28.7 °C, respectively, and between 23 °C and 45 °C for the exterior temperatures. These values remain the same with the increase in exterior temperatures for hemp concrete walls. In conclusion, hemp concrete could be a great alternative material for use in construction for hot climate zones.

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Section: Introductionmentioning

confidence: 99%

Hygrothermal Performance of the Hemp Concrete Building Envelope

Kaboré,

Maref,

Ouellet-Plamondon

2024

Energies

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Compressive strength prediction of nano-modified concrete: A comparative study of advanced machine learning techniques

Tao

2024

AIP Advances

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This study aims to develop predictive models for accurately forecasting the uniaxial compressive strength of concrete enhanced with nanomaterials. Various machine learning algorithms were employed, including the backpropagation neural network (BPNN), random forest (RF), extreme gradient boosting (XGB), and a hybrid ensemble stacking method (HEStack). A comprehensive dataset containing 94 data points for nano-modified concrete was collected, with eight input parameters: water-to-cement ratio, carbon nanotubes, nano-silica, nano-clay, nano-aluminum, cement, coarse aggregates, and fine aggregates. To evaluate the performance of these models, tenfold cross-validation and a case study prediction were conducted. It has been shown that the HEStack model is the most effective approach for precisely predicting the properties of nano-modified concrete. During cross-validation, the HEStack method was found to have superior predictive accuracy and resilience against overfitting compared to the stand-alone models. This underscores the potential of the HEStack algorithm in enhancing model performance. In the case study, the predicted results were assessed using metrics such as the coefficient of determination (R2), mean absolute percentage error (MAPE), root mean square error (RMSE), the ratio of RMSE to the standard deviation of observations (RSR), and the normalized mean bias error (NMBE). The HEStack model achieved the lowest MAPE of 2.84%, RMSE of 1.6495, RSR of 0.0874, and absolute NMBE of 0.0064. In addition, it attained a remarkable R2 value of 0.9924, surpassing the R2 scores of 0.9356 (BPNN), 0.9706 (RF), and 0.9884 (XGB), indicating its exceptional generalization capability.

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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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Optimal Design of Ecological Concrete Mix Proportion Based on AHP-GWO-BP Neural Network

Cited by 3 publications

References 41 publications

Hygrothermal Performance of the Hemp Concrete Building Envelope

Hygrothermal Performance of the Hemp Concrete Building Envelope

Compressive strength prediction of nano-modified concrete: A comparative study of advanced machine learning techniques

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

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