Reliability Analysis of Pile Foundation Using Soft Computing Techniques: A Comparative Study

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.

Modelling Compression Strength of Waste PET and SCM Blended Cementitious Grout Using Hybrid of LSSVM Models

“…It is important to note that, right after model development, various performance metrics including Adj.R 2 , NS, PI, R 2 , RMSE, RSR, VAF, and WI, were used to evaluate hybrid LSSVMs. Note that these indices are frequently used [ 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 ] to evaluate the generalization capabilities of any prediction model from a variety of perspectives, including correlation accuracy, related error, variance, and so on. The expressions of these indices can be given as follows: where p and represent the total number of input parameters and observations, respectively; …”

The Analysis Performance of Project Delays in Pile Foundation Work

“…The use of pile foundations enhances the bearing capacity of weak soils and reduces foundation settlement. As a result of the scarcity of space as well as demands for taller and heavier buildings, researchers in geotechnical sciences are increasingly studying pile foundations' reliability (Kumar et al, 2021). At the construction stage of the project, generally, the first work to be done is foundation work.…”

A Hybrid SVR-Based Prediction Model for the Interfacial Bond Strength of Externally Bonded FRP Laminates on Grooves with Concrete Prisms

“…Eight different performance indices (Equations (3)–(10)), namely the determination coefficient (R 2 ), the performance index (PI), the variance account factor (VAF), Willmott’s index of agreement (WI), the root mean square error (RMSE), the mean absolute error (MAE), the RMSE observation standard deviation ratio (RSR) and the weighted mean absolute percentage error (WMAPE), were determined to evaluate the performance of the developed models [ 38 , 44 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 ]. For a flawless prediction model, the values of these indices should be identical to their ideal values, as shown in Table 2 .…”