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ANN-based prediction of cone tip resistance with Tabu-Search optimization for geotechnical engineering applications

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A Publisher Correction to this article was published on 13 June 2023

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Abstract

The present research employed an authentic multivariate dataset of J-CLAY/5/124 Jiangsu clays from China to construct an artificial neural network (ANN) model for the purpose of forecasting the subgrade resilience modulus (Mr) using cone tip resistance (qc), sleeve frictional resistance (fs), natural water content (w), and dry unit weight (gd) as predictors. The dataset underwent exploratory data analysis (EDA) and data preprocessing procedures to ensure data quality and integrity. Subsequently, the artificial neural network (ANN) model underwent training and optimization via the Tabu-Search algorithm. The model’s performance was evaluated using various metrics, namely adjusted R2, R2, MAE, MSE, and RMSE. The artificial neural network (ANN) model that was optimized demonstrated a notable level of precision, as indicated by an adjusted R2 value of 0.87560629 and an R2 value of 0.892192118. The outcomes of the prediction exhibited favourable efficacy with minimal inaccuracies, as demonstrated by the mean absolute error (MAE) of 0.217086317 and the mean squared error (MSE) of 0.071266013. In addition, the utilization of Tabu-Search optimization resulted in a notable enhancement of the model’s efficacy, as evidenced by its elevated levels of recall, precision, F1 score, accuracy, and reduced loss. The ANN model that was developed using Tabu-Search optimization demonstrated encouraging outcomes in forecasting the subgrade resilience modulus of J-CLAY/5/124 Jiangsu clays. This outcome has the potential to offer significant insights for geotechnical engineering applications.

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Data availability

The data used in this study are available upon request from the corresponding author. The data will be made available in a secure and confidential manner, consistent with applicable laws and regulations. Any requests for data will be reviewed on a case-by-case basis to ensure that the data are being used for legitimate research purposes.

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Authors and Affiliations

  1. Department of Civil Engineering, Aqaba University of Technology, Aqaba, Jordan

    Mahmoud Al Khazaleh

  2. Department of Civil Engineering, Universiti Putra Malaysia, Serdang, Malaysia

    Majdi Bisharah

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  1. Mahmoud Al Khazaleh
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  2. Majdi Bisharah
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MA and MB wrote the main manuscript text and prepared figures . All authors wrote and reviewed the manuscript.

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Correspondence to Mahmoud Al Khazaleh.

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Al Khazaleh, M., Bisharah, M. ANN-based prediction of cone tip resistance with Tabu-Search optimization for geotechnical engineering applications. Asian J Civ Eng 24, 3037–3054 (2023). https://doi.org/10.1007/s42107-023-00693-3

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