Predicting Loading–Unloading Pile Static Load Test Curves by Using Artificial Neural Networks

Alzo’ubi, Abdel Kareem; Ibrahim, Farid

doi:10.1007/s10706-018-0687-4

Cited by 11 publications

(4 citation statements)

References 17 publications

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“…The maximum number of input features is 68, and the maximum number of network structures is 4. Multilayer neural network, the input dimension is 17, the number of hidden layers is 2, the number of hidden layer nodes is (20,20), and the output dimension is 2. The activation function uses logarithmic Sigmoid function…”

Section: Artificial Intelligence Methods For Pile Testmentioning

confidence: 99%

“…Jebur et al [19] used a new artificial neural network (ANN) method to examine pile bearing capacity and to provide a reliable model to simulate pile load-settlement behavior. Alzo'ubi et al [20] proposed an artificial neural network approach to build a model that can predict a complete static load pile test. In this paper, it was shown that, by incorporating the pile configuration, soil properties, and groundwater table in an artificial neural network model, the static load test can be predicted with confidence.…”

Section: Introductionmentioning

confidence: 99%

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Classification of Low-Strain Foundation Pile Testing Signal Using Recurrent Neural Network

Wang

Zhang

et al. 2023

Buildings

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The testing of the foundation pile is an important means to ensure the quality of the foundation pile in the construction process, and the low-strain pile test is one of the most commonly used testing technologies. However, in order to ensure that the testing signal is effective and reliable, it is necessary to provide the preliminary judgment results when acquiring the testing signal in the field. In this paper, we propose a data classification method for low-strain pile testing data using a recurrent neural network as the core. In this method, after identification, tailoring, and normalization, the input feature vector with a sequential structure is sent into this model. The model ensures the efficient use of data values while considering the sequential relationship among the data. At last, we designed and produced one complete model pile and six asymmetric model piles, which can form thirteen kinds of testing signals. The optimal application model was selected by the 10-fold cross verification method, and the influence of increasing the input feature dimension on the accuracy was discussed. Finally, compared with the other two methods, this model has the highest accuracy, at 98.46%, but it requires more training parameters and a longer training time.

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Section: Artificial Intelligence Methods For Pile Testmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of Low-Strain Foundation Pile Testing Signal Using Recurrent Neural Network

Wang

Zhang

et al. 2023

Buildings

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show abstract

“…Until now, there have been many researches, especially in the field of geotechnical engineering, using this artificial neural network capability. Several related studies such as in determining foundation behavior like prediction of shallow foundation reliability [11], pile raft foundation [12], axial capacity of pile foundation [13], shaft resistance [14], elastic settlement [15], settlement shallow foundation [16] and loading-unloading pile static load [17]. Other related research such as predicting soil physical and mechanical properties like prediction of CBR value [18], uniaxial compressive strength [19], undrained shear strength [20]- [21], bearing capacity [22]- [23], unit weight [24], compression index & compression ratio [25], classification [26], compression coefficient [27], liquefaction [28], and electrical resistivity of soil [29].…”

Section: Table 1 Summarize Of Literature Reviewmentioning

confidence: 99%

Prediction of standard penetration test value on cohesive soil using artificial neural networks

Nugroho¹,

Fernando²,

Suryanita³

2021

Jurnal Informatika

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Soil investigation is the main key in starting a construction. Standard Penetration Test (SPT) and Cone Penetration Test (CPT) are field tests that are often used in estimating soil parameters for foundation design purposes. The SPT value shows a correlation with the CPT value and other soil parameters. At present, there have been many conventional correlations examining these correlations, but the nonlinear nature of the soil due to very complex soil formations means that this correlation cannot be used in all situations. Artificial neural networks (ANN) are often used to estimate a complex and nonlinear value. In this study, that will predict the value of SPT on cohesive soil based on CPT test data and soil physical properties using artificial neural network capabilities using the Backpropagation algorithm and the activation function is bipolar sigmoid. This study uses 284 data from several places in Sumatra Island, Indonesia with data input are tip resistance (qc), shaft resistance (fs), effective overburden pressure (σ'0), percentage of liquid limit, plastic limit, sand, silt and clay. This study shows that the artificial neural network is able and effective in predicting the N-SPT value with a small error value and a strong regression equation. In this study, RMSE 3,441, MAE 2,318 and R 2 0,9451 for training data and RMSE 2,785, MAE 2,085, R 2 0,9792 for test data. This model is hereinafter referred to as NN_Nspt(C).

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“…In the last three decades, artificial intelligence (AI) techniques are increasingly applied to civil and geotechnical engineering problems (Zhu et al 1998;Banimahd et al 2005;Johari et al 2006;Lamorski et al 2008;Yurtcu and Özocak 2016;Dağdeviren and Kaymak 2018;Mohanty and Das 2018;Alzo'ubi and Ibrahim 2019;Kumar and Samui 2019). Predicting the load capacity and settlement response of shallow and deep foundations by MLM needs more comprehensive studies due to irregularity among the results and requirement of large datasets.…”

Section: Introductionmentioning

confidence: 99%

Data-driven modeling of ultimate load capacity of closed- and open-ended piles using machine learning

Ülker,

Altınok,

Taşkın

2023

International Journal of Geotechnical Engineering

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Field pile load tests are fairly expensive experiments that can be applied to certain pile types required to be installed in full scale. Hence, it is neither practical nor efficient to perform a load test for every installed pile. While there exist many empirical relations for predicting pile capacities, such methods typically suffer from accuracy and generality. Therefore, current geotechnical practice still looks for methods to accommodate full-scale pile load testing to serve as both accurate and practical tools. In this study, load bearing capacities of closed and open-ended piles in cohesive and cohesionless soils are predicted using machine learning. Nine such methods are utilized in the analyses where CPT and pile data are considered as the learning features necessary to teach those methods the database gathered via a comprehensive search. Then, machine learning models are developed, and the databases are separated into five-folds according to the cross-validation-principle, which are used for both training and testing of the machine learning methods. Model predictions are validated with classical empirical equations.Results indicate that the Relevance Vector Regression and the Random Forest methods typically generate considerably better predictions than the other methods and empirical equations. Hence, machine learning methods are found as reliable tools to predict the pile load capacities of both openended and closed-ended piles provided that there is a large enough database and an appropriate method to use.

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Predicting Loading–Unloading Pile Static Load Test Curves by Using Artificial Neural Networks

Cited by 11 publications

References 17 publications

Classification of Low-Strain Foundation Pile Testing Signal Using Recurrent Neural Network

Classification of Low-Strain Foundation Pile Testing Signal Using Recurrent Neural Network

Prediction of standard penetration test value on cohesive soil using artificial neural networks

Data-driven modeling of ultimate load capacity of closed- and open-ended piles using machine learning

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