Prediction of pile settlement using artificial neural networks based on standard penetration test data

Nejad, F. Pooya; Jaksa, M.; Kakhi, M.; McCabe, Bryan A.

doi:10.1016/j.compgeo.2009.04.003

Cited by 112 publications

(12 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Most traditional pile bearing capacity determination methods included the following parameters: pile geometry, pile material properties, and soil properties [4,5]. The depth of the water table was not included in this study, as it is believed that the effect is already accounted in the SPT blow counts [43]. Since the bearing capacity of piles depended on the soil compressibility and the SPT was one of the most commonly used tests in practice (indicating the in situ compressibility of soils), the SPT blow count/300 mm (N) along the embedded length of the pile was used as a measure of soil compressibility.…”

Section: Data Preparationmentioning

confidence: 99%

“…Nawari et al [42] developed an ANN algorithm to predict the deflection of drilled shafts based on SPT data and the shaft geometry. In addition, Pooya et al [43] developed a model to predict the pile settlement using ANN, based on SPT data using 12 input factors. Last but not least, Momeni et al [44] presented an ANN model to predict the shaft and tip resistance of concrete piles.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Pile Axial Bearing Capacity Using Artificial Neural Network and Random Forest

Pham

Tran

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

Axial bearing capacity of piles is the most important parameter in pile foundation design. In this paper, artificial neural network (ANN) and random forest (RF) algorithms were utilized to predict the ultimate axial bearing capacity of driven piles. An unprecedented database containing 2314 driven pile static load test reports were gathered, including the pile diameter, length of pile segments, natural ground elevation, pile top elevation, guide pile segment stop driving elevation, pile tip elevation, average standard penetration test (SPT) value along the embedded length of pile, and average SPT blow counts at the tip of pile as input variables, whereas the ultimate load on pile top was considered as output variable. The dataset was divided into the training (70%) and testing (30%) parts for the construction and validation phases, respectively. Various error criteria, namely mean absolute error (MAE), root mean squared error (RMSE), and the coefficient of determination (R2) were used to evaluate the performance of RF and ANN algorithms. In addition, the predicted results of pile load tests were compared with five empirical equations derived from the literature and with classical multi-variable regression. The results showed that RF outperformed ANN and other methods. Sensitivity analysis was conducted to reveal that the average SPT value and pile tip elevation were the most important factors in predicting the axial bearing capacity of piles.

show abstract

Section: Data Preparationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of Pile Axial Bearing Capacity Using Artificial Neural Network and Random Forest

Pham

Tran

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Regarding rock-socketed piles, numerous investigators [6,7], have shown that the uniaxial compressive strength (UCS) of the rocks has a direct relationship with the pile bearing capacity and its settlement. A popular intelligence system namely the artificial neural network (ANN) was designed and introduced in study by Pooya Nejad et al [8] to predict piling settlement using the standard penetration test data. They utilized nearly 1000 data, collected from previous studies, to develop the ANN models and concluded that the ANN is a powerful technique for solving problem related to pile settlement.…”

Section: Introductionmentioning

confidence: 99%

On the Use of Neuro-Swarm System to Forecast the Pile Settlement

et al. 2020

View full text Add to dashboard Cite

In civil engineering applications, piles (deep foundations) are pushed into the ground in order to perform as steady support of structures. As these type of foundations are able to carry a huge amount of load, they should be carefully designed in terms of their settlement. Therefore, the control and estimation of settlement is a significant issue in pilling design and construction. The objective of the present study is to introduce a modeling process of a hybrid intelligence system namely neural network optimized by particle swarm optimization (neuro-swarm) for estimation of pile settlement. To do that, properties results of several piles socketed into rock mass together with their settlements were considered as established databased to propose neuro-swarm model. Then, several sensitivity analyses were carried out to determine the most influential particle swarm optimization parameters for pile settlement prediction. Eventually, five neuro-swarm models were constructed to understand the behavior of this hybrid model on them in pile settlement prediction. As a result, according to results of five performance indices, dataset number 4 showed the highest prediction capacity among all five datasets. The coefficient of determination (R2) and system error values of (0.851 and 0.079) and (0.892 and 0.099) were obtained respectively for train and test stages of the best neuro-swarm model which reveal the capability level of this hybrid model in predicting pile settlement. The modeling process introduced in this study can be useful for the researchers who are interested to work on the same hybrid technique.

show abstract

“…Fan [41] used a logistic model and a Gompertz model to propose a combination forecasting model for the settlement prediction of soft soil foundations, and Li et al [42] employed a combined prediction model based on an improved set pair analysis to predict subgrade settlement. Nejad et al [43] proposed a back propagation neural network model to test the feasibility of predicting pile foundation settlements using artificial neural networks. Oliveira et al [44] proposed a new improved quasi-equal time gray model (QGM (1,1)) prediction model to overcome the shortcomings of traditional non-isochronous models, such as cumbersome calculations and low medium-and short-term prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Three-Point Hyperbolic Combination Model for the Settlement Prediction of Subgrade Filled with Construction and Demolition Waste

Wang

Shen

et al. 2020

Materials

View full text Add to dashboard Cite

Using construction and demolition waste (CDW) as road subgrade filling materials is an excellent way to solve the disparity between increased demand and road construction aggregate shortages. However, a key quality control problem is predicting the subgrade settlement, primarily because the CDW subgrade settlement prediction methods are not yet mature. To go some way in overcoming this problem, in this paper we developed a three-point hyperbolic combination model to predict CDW subgrade settlement, in which three appropriate points for the measured settlement curve were selected in the prediction samples to improve the hyperbolic model. Then, common prediction models—namely, the hyperbolic model, the three-point model, and the Hushino model—were compared with the proposed combination model to assess its viability. Finally, the three-point hyperbolic combination prediction accuracy was analyzed for different start points t0 and time intervals Δt. The analyses found that the proposed model was in good agreement with the measured data, had a high correlation coefficient, and had only small errors. However, the time interval Δ t needed to be greater than 80 days and the start point t0 needed to be selected at the beginning of the stable post-filling period, that is, t0 = 90–100 days. The application parameters were also determined to provide a reference for the large-scale application and settlement predictions of CDW subgrade.

show abstract

Prediction of pile settlement using artificial neural networks based on standard penetration test data

Cited by 112 publications

References 29 publications

Prediction of Pile Axial Bearing Capacity Using Artificial Neural Network and Random Forest

Prediction of Pile Axial Bearing Capacity Using Artificial Neural Network and Random Forest

On the Use of Neuro-Swarm System to Forecast the Pile Settlement

A Three-Point Hyperbolic Combination Model for the Settlement Prediction of Subgrade Filled with Construction and Demolition Waste

Contact Info

Product

Resources

About