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Computers and Concrete
  Volume 32, Number 3, September 2023 , pages 327-337

Deep learning method for compressive strength prediction for lightweight concrete
Yaser A. Nanehkaran, Mohammad Azarafza, Tolga Pusatli, Masoud Hajialilue Bonab, Arash Esmatkhah Irani, Mehdi Kouhdarag, Junde Chen and Reza Derakhshani

    Concrete is the most widely used building material, with various types including high- and ultra-high-strength, reinforced, normal, and lightweight concretes. However, accurately predicting concrete properties is challenging due to the geotechnical design code's requirement for specific characteristics. To overcome this issue, researchers have turned to new technologies like machine learning to develop proper methodologies for concrete specification. In this study, we propose a highly accurate deep learning-based predictive model to investigate the compressive strength (UCS) of lightweight concrete with natural aggregates (pumice). Our model was implemented on a database containing 249 experimental records and revealed that water, cement, water-cement ratio, fine-coarse aggregate, aggregate substitution rate, fine aggregate replacement, and superplasticizer are the most influential covariates on UCS. To validate our model, we trained and tested it on random subsets of the database, and its performance was evaluated using a confusion matrix and receiver operating characteristic (ROC) overall accuracy. The proposed model was compared with widely known machine learning methods such as MLP, SVM, and DT classifiers to assess its capability. In addition, the model was tested on 25 laboratory UCS tests to evaluate its predictability. Our findings showed that the proposed model achieved the highest accuracy (accuracy=0.97, precision=0.97) and the lowest error rate with a high learning rate (R2=0.914), as confirmed by ROC (AUC=0.971), which is higher than other classifiers. Therefore, the proposed method demonstrates a high level of performance and capability for UCS predictions.
Key Words
    aggregate; compressive strength; deep learning; lightweight concrete; predictive model
Yaser A. Nanehkaran: School of Information Engineering, Yancheng Teachers University, Yancheng 224002, Jiangsu, China
Mohammad Azarafza and Masoud Hajialilue Bonab: Department of Civil Engineering, University of Tabriz, Tabriz 5166616471, Iran
Tolga Pusatli: Department of Management Information Systems, Cankaya University, Ankara 06790, Turkey
Arash Esmatkhah Irani: Department of Civil Engineering, Islamic Azad University, Tabriz Branch, Tabriz 5157944533, Iran
Mehdi Kouhdarag: Department of Civil Engineering, Malekan Branch, Islamic Azad University, Malekan 5561788389, Iran
Junde Chen: Department of Electronic Commerce, Xiangtan University, Xiangtan 411105, Hunan, China
Reza Derakhshani: Department of Earth Sciences, Utrecht University, Netherlands

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