Optimized artificial neural network model for accurate prediction of compressive strength of normal and high strength concrete

Qayyum Khan, Arslan; Ahmad Awan, Hasnain; Rasul, Mehboob; Ahmad Siddiqi, Zahid; Pimanmas, Amorn

doi:10.1016/j.clema.2023.100211

Cited by 14 publications

(2 citation statements)

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“…Their results showed that the modified RNN predicted the thermal behavior of the concrete accurately, and the original RNN also predicted the behavior well. In another case, Khan et al [32] carried out a compressive analysis to predict the strength of concrete considering various material parameters, such as the cement and aggregates used, and the curing time, using an optimized artificial neural network. The predicted compressive strength was almost the same as that observed experimentally and the reported R 2 value was higher than 0.95, indicating that the results of Khan et al [32] have high reliability.…”

Section: Introductionmentioning

confidence: 99%

“…In another case, Khan et al [32] carried out a compressive analysis to predict the strength of concrete considering various material parameters, such as the cement and aggregates used, and the curing time, using an optimized artificial neural network. The predicted compressive strength was almost the same as that observed experimentally and the reported R 2 value was higher than 0.95, indicating that the results of Khan et al [32] have high reliability. In addition to these cases, the use of artificial neural networks, such as LSTM [33][34][35] and convolution neural networks [36][37][38], in concrete studies has already demonstrated several times that the accuracy of prediction is high.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning-Based Simulation of the Air Conditioner Operating Time in Concrete Structures with Bayesian Thresholding

Jang,

Kim,

Woo

2024

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Efficient energy use is crucial for achieving carbon neutrality and reduction. As part of these efforts, research is being carried out to apply a phase change material (PCM) to a concrete structure together with an aggregate. In this study, an energy consumption simulation was performed using data from concrete mock-up structures. To perform the simulation, the threshold investigation was performed through the Bayesian approach. Furthermore, the spiking part of the spiking neural network was modularized and integrated into a recurrent neural network (RNN) to find accurate energy consumption. From the training-test results of the trained neural network, it was possible to predict data with an R2 value of 0.95 or higher through data prediction with high accuracy for the RNN. In addition, the spiked parts were obtained; it was found that PCM-containing concrete could consume 32% less energy than normal concrete. This result suggests that the use of PCM can be a key to reducing the energy consumption of concrete structures. Furthermore, the approach of this study is considered to be easily applicable in energy-related institutions and the like for predicting energy consumption during the summer.

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Section: Introductionmentioning

confidence: 99%