Abstract
Uncertainty and variability are inherent to pile design and consequently, there have been considerable researches in quantifying the reliability or probability of failure of structures. This paper aims at examining and comparing the applicability and adaptability of Minimax Probability Machine Regression (MPMR), Emotional Neural Network (ENN), Group Method of Data Handling (GMDH), and Adaptive Neuro-Fuzzy Inference System (ANFIS) in the reliability analysis of pile embedded in cohesionless soil and proposes an AI-based prediction method for bearing capacity of pile foundation. To ascertain the homogeneity and distribution of the datasets, Mann–Whitney U (M–W) and Anderson–Darling (AD) tests are carried out, respectively. The performance of the developed soft computing models is ascertained using various statistical parameters. A comparative study is implemented among reliability indices of the proposed models by employing First Order Second Moment Method (FOSM). The results of FOSM showed that the ANFIS approach outperformed other models for reliability analysis of bearing capacity of pile and ENN is the worst performing model. The value of R2 for all the developed models is close to 1. The best RMSE value is achieved for the training phase of the ANFIS model (0 in training and 2.13 in testing) and the poorest for the ENN (2.03 in training and 31.24 in testing) model. Based on the experimental results of reliability indices, the developed ANFIS model is found to be very close to that computed from the original data.
Highlights
Pile foundations are used in weak soils to increase its bearing capacity and reduction of foundation settlements
MATLAB have been used to construct the program of Minimax Probability Machine Regression (MPMR), Emotional Neural Network (ENN), Group Method of Data Handling (GMDH) and Adaptive Neuro-Fuzzy Inference System (ANFIS)
The Regression Error Characteristic (REC) curves, Taylor diagrams are drawn in R-studio
Summary
Pile foundations are used in weak soils to increase its bearing capacity and reduction of foundation settlements. Due to scarcity of space and demand to build taller and heavier buildings, study on reliability analysis of bearing capacity of the pile foundations is increasingly attracting attention of geotechnical researchers. Reliability is the probability of performance of the essential function by the system in consideration, effectively for a given period of time under specified conditions [1]. It is defined as (1- probability of failure). MPMR is built improving minimax probability machine classification algorithm (MPMC). It constructs regression function using Mercer byKputting direct minimum probability and maximizing it [20]. MPMR do not make assumptions respecting the data distribution which of validity and generality.
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