Predicting the pile static load test using backpropagation neural network and generalized regression neural network – a comparative study

Alzo’ubi, Abdel Kareem; Ibrahim, Farid

doi:10.1080/19386362.2018.1519975

Cited by 12 publications

(4 citation statements)

References 24 publications

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“…The accuracy of the predictions was evaluated by comparing them with monitoring values. Alzo'Ubi [16] established a BP ANN and a generalized regression neural network to predict the settlement displacements of helical bored piles under static loading tests. The fitting performance was determined by the goodness of fit R 2 of the regression model and comparison of predicted results with actual results.…”

Section: Discussionmentioning

confidence: 99%

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Study on Soil Parameter Evolution during Ultra-Large Caisson Sinking Based on Artificial Neural Network Back Analysis

Liang

Zhang

et al. 2023

Sustainability

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The determination of soil parameters in geotechnical engineering and their variations during the construction process have long been a focal point for engineering designers. While the artificial neural network (ANN) has been employed for back analysis of soil parameters, its application to caisson sinking processes remains limited. This study focuses on the Nanjing Longtan Yangtze River Bridge project, specifically the south anchoring of an ultra-large rectangular caisson. A comprehensive analysis of the sinking process was conducted using 400 finite element method (FEM) models to obtain the structural stress and earth pressure at key locations. Multiple combinations of soil parameters were considered, resulting in a diverse set of simulation results. These results were then utilized as training samples to develop a back-propagating artificial neural network (BP ANN), which utilized the structural stress and earth pressure as input sets and the soil parameters as output sets. The BP ANN was individually trained for each stage of the sinking process. Subsequently, the trained ANN was employed to predict the soil parameters under different working conditions based on actual monitoring data from engineering projects. The obtained soil parameter variations were further analyzed, leading to the following conclusions: (1) The soil parameters estimated by the ANN exhibited strong agreement with the original values from the geological survey report, validating their reliability; (2) The surrounding soil during the caisson sinking exhibited three distinct states: a stable state prior to the arrival of the cutting edges, a strengthened state upon the arrival of the cutting edges, and a disturbed state after the passage of the cutting edges; (3) In the stable state, the soil parameters closely resembled the original values, whereas in the strengthened state, the soil strength and stiffness significantly increased, while the Poisson’s ratio decreased. In the disturbed state, the soil strength and stiffness were slightly lower than the original values. This study represents a valuable exploration of back analysis for caisson engineering. The findings provide important insights for similar engineering design and construction projects.

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Section: Discussionmentioning

confidence: 99%

“…In terms of pile foundation, A variety of ANN models have been used to predict the bearing capacity of single pile [15] and the displacement of pile top [16]. Ismail, A. developed a robust hybrid training algorithm by combining particle swarm optimization (PSO) and BP algorithms [17].…”

Section: Introductionmentioning

confidence: 99%

Study on Soil Parameter Evolution during Ultra-Large Caisson Sinking Based on Artificial Neural Network Back Analysis

Liang

Zhang

et al. 2023

Sustainability

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“…Karimipour et al [15] used neural networks to predict the bearing capacity of concrete columns under axial loads and obtained results with relatively high accuracy. Alzo and Ibrahim [16] used a back-propagation (BP) method algorithm and general regression neural network (GR-ANN) to reasonably predict the static load experiment, verifying the mode with the existing experimental data. The results show that, based on the same quality and quantity of data, the BP algorithm obtains better results than the GR-ANN, proving the feasibility of using a BP neural network to predict the ultimate bearing capacity of pile foundations.…”

Section: Introductionmentioning

confidence: 89%

Prediction of Ultimate Bearing Capacity of Pile Foundation Based on Two Optimization Algorithm Models

Ren

Xian-bin

2023

Buildings

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The determination of the bearing capacity of pile foundations is very important for their design. Due to the high uncertainty of various factors between the pile and the soil, many methods for predicting the ultimate bearing capacity of pile foundations focus on correlation with field tests. In recent years, artificial neural networks (ANN) have been successfully applied to various types of complex issues in geotechnical engineering, among which the back-propagation (BP) method is a relatively mature and widely used algorithm. However, it has inevitable shortcomings, resulting in large prediction errors and other issues. Based on this situation, this study was designed to accomplish two tasks: firstly, using the genetic algorithm (GA) and particle swarm optimization (PSO) to optimize the BP network. On this basis, the two optimization algorithms were improved to enhance the performance of the two optimization algorithms. Then, an adaptive genetic algorithm (AGA) and adaptive particle swarm optimization (APSO) were used to optimize a BP neural network to predict the ultimate bearing capacity of the pile foundation. Secondly, to test the performance of the two optimization models, the predicted results were compared and analyzed in relation to the traditional BP model and other network models of the same type in the literature based on the three most common statistical indicators. The models were evaluated using three common evaluation metrics, namely the coefficient of determination (R2), value account for (VAF), and the root mean square error (RMSE), and the evaluation metrics for the test set were obtained as AGA-BP (0.9772, 97.8348, 0.0436) and APSO-BP (0.9854, 98.4732, 0.0332). The results show that compared with the predicted results of the BP model and other models, the test set of the AGA-BP model and APSO-BP model achieved higher accuracy, and the APSO-BP model achieved higher accuracy and reliability, which provides a new method for the prediction of the ultimate bearing capacity of pile foundations.

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“…Several researchers investigated the field performance of root piles [1][2][3]5,6] and other types of piles [7][8][9][10][11][12][13], with some important conclusions, such as that pile design has traditionally been based on collecting and analyzing data from load tests, revealing a scarcity of alternatives to support engineers when assessing pile performance during its installation. 2019) [3].…”

Section: Introductionmentioning

confidence: 99%

Performance control of root piles using a digital odometer

Filho

Monteiro

Moura

2022

DYNA

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Root piles are injected and installed during mortar shaft construction, using pressures of up to 500 kPa. The executive control is typically done through static load testing, an expensive and time-consuming method. Static load tests on eight controlled piles (diameters between 310 mm and 410 mm) were performed, aiming at evaluating pile ultimate load. This study suggests an innovative, non-destructive approach to validate root pile field performance, using a digital speedometer connected to the drilling rig’s rotating head. The proposed method monitors variables related to bearing capacity during pile installation and proposes an empirical equation to estimate the ultimate bearing capacity of root piles. For the assessed piles, the predictions obtained with the proposed equations agreed fairly well with results from static load tests, proving it as a feasible and helpful option for the executive control of root piles, especially when load tests are not available.

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Predicting the pile static load test using backpropagation neural network and generalized regression neural network – a comparative study

Cited by 12 publications

References 24 publications

Study on Soil Parameter Evolution during Ultra-Large Caisson Sinking Based on Artificial Neural Network Back Analysis

Study on Soil Parameter Evolution during Ultra-Large Caisson Sinking Based on Artificial Neural Network Back Analysis

Prediction of Ultimate Bearing Capacity of Pile Foundation Based on Two Optimization Algorithm Models

Performance control of root piles using a digital odometer

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