Abstract
The shear strength of the soil (SSS) is a significant attribute that is employed most frequently throughout the design phase of construction projects. The conventional approach of determining shear strength (SS) in the laboratory is one that is both costlier and more time-consuming. The ability to precisely predict the SSS without the need for laborious and expensive testing in a laboratory is just one of the real-world needs of geotechnical professionals. In this paper, an attempt has been made to develop a common methodology for predicting the SSS using optimized models. For this purpose, three additional optimized algorithms (GA, MPA, and PSO) were utilized to improve the bias and weight of the ANN's learning parameters, and three optimized ANNs (ANN-GA, ANN-MPA, and ANN-PSO) were developed. Validation of all the developed optimized models was executed using RMSE, R2, RSR, WI, and NSE indices. After validation of optimized models, it was found that out of three, ANN-GA produces good modelling outcomes in training as well as in the testing phase, outperforming other models. It has been shown that the GA develops the most trustworthy ANN, and this was also validated by the rank analysis of developed models. When trying to predict SSS, it has been shown that the liquidity index (LI) is the key factor to take into consideration. This was determined by plotting the feature significance plot along with the feature selection plot. Following the LI, the water content (wc) is the second most important input variable that has an effect on the value of the parameter of interest being investigated in the present investigation. In a broad sense, it was found that the factors associated with water were the primary characteristics that impacted the prediction of SSS.
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The datasets created and/or analysed during the present investigation can be found in the [Cao et al. (2020)] repository [https://doi.org/10.1007/s00366-020-01116-6].
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Rabbani, A., Samui, P. & Kumari, S. Optimized ANN-based approach for estimation of shear strength of soil. Asian J Civ Eng 24, 3627–3640 (2023). https://doi.org/10.1007/s42107-023-00739-6
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DOI: https://doi.org/10.1007/s42107-023-00739-6