Influence of Biochar Obtained from Invasive Weed on Infiltration Rate and Cracking of Soils: An Integrated Experimental and Artificial Intelligence Approach

Gopal, Phani; Ratnam, Raval; Farooq, Muhammad; Garg, Ankit; Gogoi, Nirmali

doi:10.1007/978-981-13-2221-1_35

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…The realm of geotechnical engineering, while somewhat reserved in its adoption of AI, has begun to witness the application of AI-based techniques in addressing complex challenges. AI methods such as artificial neural networks (ANNs), fuzzy inference systems (FISs), adaptive neuro-fuzzy inference systems (ANFISs), and others have shown remarkable potential in deciphering intricate relationships within complex datasets across diverse domains such as soil dynamics [15][16][17][18][19][20], deep foundations [21][22][23][24], soil cracking [25][26][27], recycled materials [28][29][30][31][32][33][34][35][36], soil mechanics [37,38], tunnelling and rock mechanics [39][40][41] and other fields [42][43][44][45][46][47][48][49][50][51]. The beauty of these techniques lies in their capacity to capture nonlinear interactions between a myriad of variables, even when the underlying relationships are not fully understood.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Ultimate Bearing Capacity Prediction of Cohesionless Soils Beneath Shallow Foundations with Grey Box and Hybrid AI Models

Kiany,

Baghbani,

Abuel-Naga

et al. 2023

Algorithms

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This study examines the potential of the soft computing technique, namely, multiple linear regression (MLR), genetic programming (GP), classification and regression trees (CART) and GA-ENN (genetic algorithm-emotional neuron network), to predict the ultimate bearing capacity (UBC) of cohesionless soils beneath shallow foundations. For the first time, two grey-box AI models, GP and CART, and one hybrid AI model, GA-ENN, were used in the literature to predict UBC. The inputs of the model are the width of footing (B), depth of footing (D), footing geometry (ratio of length to width, L/B), unit weight of sand (γd or γ′), and internal friction angle (ϕ). The results of the present model were compared with those obtained via two theoretical approaches and one AI approach reported in the literature. The statistical evaluation of results shows that the presently applied paradigm is better than the theoretical approaches and is competing well for the prediction of qu. This study shows that the developed AI models are a robust model for the qu prediction of shallow foundations on cohesionless soil. Sensitivity analysis was also carried out to determine the effect of each input parameter. The findings showed that the width and depth of the foundation and unit weight of soil (γd or γ′) played the most significant roles, while the internal friction angle and L/B showed less importance in predicting qu.

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