2022

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Application of machine learning methods in shallow foundation design
(UMT.Lahore, 2022) Ali Akbar; M. Hassaan Zahid; Ahsan Ahmad; Hassam Yahya; Muhammad Aimal
The ability to accurately predict the ultimate bearing capacity of a shallow foundation is crucial in the foundation design process for a variety of buildings. With the use of characteristicslike foundation width (B), depth (D), length (L), angle of internal friction (φ), and specific gravity (⋎), the authors hope to arrive at a more precise and explicit formulation for estimating ultimate bearing capacity. Artificial Neural Network (ANN) has been used to construct a more thorough model for calculating ultimate bearing capacity than earlier techniques, making it more accurate and easier to use. We evaluate our findings against the raw data, traditional analytical models, and existing soft computing techniques. As a result, the authors conclude that the suggested model outperforms competing models in terms of accuracy, performance, and error mode.