Using an evolutionary heterogeneous ensemble of artificial neural network and multivariate adaptive regression splines to predict bearing capacity in axial piles

Influence of Settlement on Base Resistance of Long Piles in Soft Soil—Field and Machine Learning Assessments

“…The major advantage of these pile tests compared to ma others in previous studies was the installation of strain gauges along the pile and near toe (i.e., 0.5-1 m above the pile tip) to estimate the load distribution during testing, e bling the shaft and base resistances to be estimated. Compared to previous studies wh machine learning techniques were applied to pile foundations [20,23,27,28], the piles vestigated in this study were certainly longer, larger and embedded through various s soil strata, thereby exhibiting a more complex and distinct behaviour. With reference to the previous findings [9], 5 key parameters, including the applied load (P t ), settlement (S or the displacement of loading point), axial stiffness (i.e., A × E, where A is the cross-sectional area of pile, and E is the equivalent Young's modulus, E = 36 GPa), SPT values (N) of the soil beneath the pile toe, and the distance from the loading point to the pile toe (L e ), were considered in the current analysis.…”

“…The major advantage of these pile tests compared to many others in previous studies was the installation of strain gauges along the pile and near the toe (i.e., 0.5-1 m above the pile tip) to estimate the load distribution during testing, enabling the shaft and base resistances to be estimated. Compared to previous studies where machine learning techniques were applied to pile foundations [20,23,27,28], the piles investigated in this study were certainly longer, larger and embedded through various soft soil strata, thereby exhibiting a more complex and distinct behaviour.…”

“…In recent years, machine learning (ML) has emerged as a preferrable and effective approach, not only to predict various geotechnical issues, but also to assess the interaction between different features [20][21][22]. Past ML applications, including the artificial neural network (ANN) and other advanced algorithms for pile foundation, have mainly focused on prediction of pile bearing capacity and settlement as highlighted in recent review studies, e.g., Baghbani et al (2022) and Nguyen et al (2023b) [23,24].…”

“…Past ML applications, including the artificial neural network (ANN) and other advanced algorithms for pile foundation, have mainly focused on prediction of pile bearing capacity and settlement as highlighted in recent review studies, e.g., Baghbani et al (2022) and Nguyen et al (2023b) [23,24]. For example, many studies [25,26] employed shear parameters of soil from laboratory and field tests, including SPT and CPT data, to build ML models for predicting bearing capacity and settlement of piles, while the basic settlement-loading curves have also been used [20,27]. Nevertheless, there is a lack of attention to using ML to predict components of pile bearing capacity such as the skin friction and base resistance, mainly because of the complexity in measuring these individual parameters in the field.…”

Bearing capacity prediction of the concrete pile using tunned ANFIS system

“…Additionally, as learning data increases, the machine learning ( ML)-based models' efficiency may be enhanced progressively, keeping them current with the strict precision demands for complicated engineering challenges [25][26][27][28][29][30]. Numerous studies have shown how well ML-based models work in solving issues associated to civil engineering, such as forecasting the mechanical characteristics (compressive/ tensile strength/shear) of hardened concrete [31][32][33][34], the ultimate bond strength of corroded reinforcement and surrounding concrete [35,36], the bearing capability of piles [37,38], the pulling capability of ground anchors [39][40][41], and others.…”