Prediction of axial load bearing capacity of PHC nodular pile using Bayesian regularization artificial neural network

Nguyen, Thang Trung; Ly, Khuong-Duy; Nguyen-Thoi, Trung; Nguyen, Ba-Phu; Doan, Nhat-Phi

doi:10.1016/j.sandf.2022.101203

Cited by 27 publications

(7 citation statements)

References 50 publications

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“…To address these challenges, Ouyang et al [59] proposed a flexible solution for the analysis of laterally loaded piles, leveraging the power of machine learning. Machine learning, an emerging artificial intelligence technology, offers distinct advantages in intensive computation and universal approximation capabilities (He et al [37], Huynh et al [42], Hsiao et al [40], Zhang et al [83], Zhang et al [84], Nguen et al [57], [58]). It commonly utilizes neural networks, which are mathematical models with layered structures comprising linear transformations and nonlinear activation functions.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Laterally Loaded Piles in Multi-layered Cohesive Soils: a Hybrid Bnwf Approach With Validation and Parametric Study

Gendy

2024

Preprint

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Pile foundations frequently encounter lateral loads originating from various hazards. These types of foundations are commonly utilized in structures like bridges, retaining walls, and high-rise buildings. Analyzing laterally loaded piles presents a complex geotechnical problem that entails considering multiple interrelated design factors. It requires accounting for structural bending behavior, soil-structure interaction, soil nonlinearity, and optimizing for cost-effectiveness. In this paper, the commonly used approach beam on nonlinear Winkler foundation is developed. This methodology involves representing the pile using one-dimensional finite elements in the vertical direction, incorporating nonlinear bending stiffness. Additionally, soil deformation is determined using empirically derived P-y curves, which are obtained from full-scale field tests. By combining the pile stiffness with the soil stiffness considering the full interaction between the pile and the surrounding soil, the complete stiffness matrix of the single pile is formed, leading to a reduction in the number of equations that need to be solved. Both Euler and Timoshenko beams are considered, and the analysis is conducted using both finite elements and finite difference methods. The proposed hybrid approach is validated by comparing its results from analyzing laterally loaded piles in multi-layered soil profiles with those obtained from different models in existing literature and available field measurements. The well-known software ELPLA is equipped with the proposed hybrid technique. Furthermore, a parametric study investigates the behavior of laterally loaded pipe piles in soft and stiff clay, culminating in the presentation of dimensionless curves from this study.

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Section: Introductionmentioning

confidence: 99%

Analyzing Laterally Loaded Piles in Multi-layered Cohesive Soils: a Hybrid Bnwf Approach With Validation and Parametric Study

Gendy

2024

Preprint

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“…Furthermore, the reliability of the model was rigorously verified using independent datasets. Nguyen et al [16] utilized a feedforward neural network (FFNN) to investigate the ultimate axial bearing capacity of pre-stressed precast high-strength concrete (PHC) joint piles. They employed the regularization backpropagation technique (BRB) for network training, and the resulting output values closely matched the measured values, showcasing the robustness and reliability of the FFNN model.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Ultimate Bearing Capacity of Soil–Cement Mixed Pile Composite Foundation Using SA-IRMO-BPNN Model

Xi,

Jin,

et al. 2024

Mathematics

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The prediction of the ultimate bearing capacity (UBC) of composite foundations represents a critical application of test monitoring data within the field of intelligent geotechnical engineering. This paper introduces an effective combinational prediction algorithm, namely SA-IRMO-BP. By integrating the Improved Radial Movement Optimization (IRMO) algorithm with the simulated annealing (SA) algorithm, we develop a meta-heuristic optimization algorithm (SA-IRMO) to optimize the built-in weights and thresholds of backpropagation neural networks (BPNN). Leveraging this integrated prediction algorithm, we forecast the UBC of soil–cement mixed (SCM) pile composite foundations, yielding the following performance metrics: RMSE = 3.4626, MAE = 2.2712, R = 0.9978, VAF = 99.4339. These metrics substantiate the superior predictive performance of the proposed model. Furthermore, we utilize two distinct datasets to validate the generalizability of the prediction model presented herein, which carries significant implications for the safety and stability of civil engineering projects.

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“…A pretensioned prestressed high strength concrete pipe is called a PHC pile for short [1][2][3][4]. Its bearing capacity includes vertical bearing capacity, horizontal bearing capacity and seismic bearing capacity [5][6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…Zhou et al conducted a field study on the pullout bearing capacity of PHC piles buried in cohesive soil, and the soil around the PHC piles treated with cement slurry [18]. Nguyen et al used feedforward neural network (FFN) to study the ultimate axial bearing capacity of PHC node piles [2]. Kim et al investigated the performance of Extended end (EXT) piles by field tests and confirmed that the bearing capacity of EXT piles is better than PHC piles [19].…”

Section: Introductionmentioning

confidence: 99%

Field Test Study on the Bearing Capacity of Extra-Long PHC Pipe Piles under Dynamic and Static Loads

Xiao¹,

Liu²,

Zhou³

et al. 2023

Sustainability

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Pretensioned prestressed high strength concrete (PHC) pipe piles are widely used in various engineering foundations, which have the advantages of high single pile bearing capacity, strong adaptability to geological conditions and high degree of construction mechanization. In order to study the vertical compressive bearing performance and settlement characteristics of ultra-long PHC pipe piles, high strain dynamic detections and static load tests were carried out on four PHC piles with a diameter of 0.9 m on site. It can be seen from the field test that the bearing capacity of the prefabricated pipe piles was time-dependent. By the end of the dynamic test, the bearing capacity of each test pile increased by 27% to 66%. The static load test also verified the rationality of the value of the restitution coefficient. Therefore, the final bearing capacity of the pile foundation can be predicted by using the high strain initial driving results and the restitution coefficient, which can reduce the repeated driving process, effectively save the cost and improve the engineering efficiency. Under 2.1 times the design load, the change range of the pile concrete modulus is from 37.5 GPa to 52 GPa, the change range of the pile side friction resistance is from 0 kPa to 97 kPa and the change range of the pile end to pile bottom load ratio is from 0% to 7.54%. During the test, the shaft friction and end bearing of the lower part of the piles were not fully mobilized. The shaft friction resistance, the end resistance and the movement behavior of the pile top and the end of the piles can provide parameter references for the subsequent design and construction of the piles.

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Prediction of axial load bearing capacity of PHC nodular pile using Bayesian regularization artificial neural network

Cited by 27 publications

References 50 publications

Analyzing Laterally Loaded Piles in Multi-layered Cohesive Soils: a Hybrid Bnwf Approach With Validation and Parametric Study

Analyzing Laterally Loaded Piles in Multi-layered Cohesive Soils: a Hybrid Bnwf Approach With Validation and Parametric Study

Prediction of Ultimate Bearing Capacity of Soil–Cement Mixed Pile Composite Foundation Using SA-IRMO-BPNN Model

Field Test Study on the Bearing Capacity of Extra-Long PHC Pipe Piles under Dynamic and Static Loads

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