Simulating pile load-settlement behavior from CPT data using intelligent computing

Alkroosh, Iyad; Nikraz, Hamid

doi:10.2478/s13531-011-0029-2

Cited by 13 publications

(9 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Alkroosh and Nikraz used a gene-expression-programming (GEP) model to investigate the correlation between pile axial capacity and data obtained in cone penetration tests. Simulations obtained a maximal coefficient R value of 0.96 between the pile axial capacity predicted by the GEP model and the actual pile capacity (Alkroosh and Nikraz 2011).…”

Section: Artificial Intelligence In Geotechnical Engineeringmentioning

confidence: 92%

“…Alkroosh and Nikraz simulated the subsidence of pile foundations under loading in cone penetration tests (Alkroosh and Nikraz 2011). Experimental applications of ANN for predicting the axial settlement of pile foundations embedded in sand showed that the models accurately predicted the nonlinear behaviors of soil under loading.…”

Section: Artificial Intelligence In Geotechnical Engineeringmentioning

confidence: 99%

“…AI-based approaches have attracted a great deal of scientific attention and have been widely used in civil engineering (Alkroosh and Nikraz 2011;Cao et al 2015;Chou and Lin 2013;Chou et al 2016;Pham 2013, 2015;Chou and Tsai 2012;Das 2012;Park and Kim 2011;Samui 2013;Tinoco et al 2014). Various supervised AI techniques [e.g., artificial neural network (ANN), classification and regression tree (CART), chi-squared automatic interaction detector (CHAID), linear regression (REG), generalized linear (GENLIN) models, and support vector machine (SVM)] are typically used individually to construct single models as the benchmark models (Wu et al 2008).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Peak Shear Strength of Discrete Fiber-Reinforced Soils Computed by Machine Learning and Metaensemble Methods

Chou

Yang

Lin

2016

J. Comput. Civ. Eng.

View full text Add to dashboard Cite

The accuracy of prior theoretical and empirical models for predicting the shear strength of fiber-reinforced soil (FRS) is questionable because of the difficulty of using these simplified models to describe the complex mechanism of soil-fiber interaction. This study compiled a large database of available high quality triaxial and direct shear tests on FRS documented in the literature from 1983 to 2015. The database includes information on the properties of sand, fibers, soil-fiber interface, and stress parameters. After data preprocessing, data mining technologies were employed to identify factors influencing shear strength and to predict the peak friction angle of FRS. The analysis techniques included (1) classification and regression methods, i.e., linear regression (REG) analysis, classification and regression tree (CART) analysis, a generalized linear (GENLIN) model, and chi-squared automatic interaction detection (CHAID); (2) machine learners, i.e., artificial neural network (ANN) and support vector machine (SVM) and support vector regression (SVR); and (3) metaensemble models, i.e., voting, bagging, stacking, and tiering. The analytical results indicated that fiber content, fiber aspect ratio, soil friction angle, and stress parameter had major effects on FRS shear strength. The optimal model obtained after further model training, cross-validation, and testing was the Tiering SVM-(SVR/SVR) method. The correlation coefficient (R) of the prediction values with the measured values in the database was 0.89, indicating a strong association. The mean absolute percentage error (MAPE) was 3.27%, root mean square error (RMSE) was 1.98°, and mean absolute error (MAE) was 1.07°. The overall improvement in performance measures was 9.31-79.50%, which was comparable to that of other models reported in the literature. This study contributes to the domain knowledge by developing an effective artificial intelligence (AI) model for predicting the peak friction angle of FRS.

show abstract

Section: Artificial Intelligence In Geotechnical Engineeringmentioning

confidence: 92%

Section: Artificial Intelligence In Geotechnical Engineeringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Peak Shear Strength of Discrete Fiber-Reinforced Soils Computed by Machine Learning and Metaensemble Methods

Chou

Yang

Lin

2016

J. Comput. Civ. Eng.

View full text Add to dashboard Cite

show abstract

“…Pile foundations are structural elements constructed underneath superstructures, frequently utilised as load carrying systems and soil settlement controls at sites with poor soil bearing capacity at sub-soil layers (Nazir and Nasr, 2013;Tomlinson and Woodward, 2014;Tschuchnigg and Schweiger, 2015). Pile bearing capacity and settlement are the most significant factors that influence pile foundation design procedures (Alkroosh and Nikraz, 2011;Alizadeh et al, 2012;Das, 2015;Nejad and Jaksa, 2017), making this a core feature of research in the field of geotechnical engineering. Accordingly, several procedures concerning pile bearing capacity, are available in the open literature, ranging from the application of complicated nonlinear numerical procedures to empirical relationships (Nasr, 2014).…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of an evolutionary LM algorithm to model the load-settlement response of steel piles embedded in sandy soil

et al. 2019

View full text Add to dashboard Cite

This study was implemented to examine pile load-settlement response and to develop a rapid, highly efficient predictive intelligent model, using a new computational intelligence (CI) algorithm. To achieve this aim, a series of experimental pile load tests were performed on steel, closed-ended pile models consisting of three piles with aspect ratios of 25, 17, and 12 in an attempt to make site in-situ pile-load tests unnecessary. An optimised, evolutionary, supervised Levenberg-Marquardt (LM) training algorithm was used for this process due to its remarkably robust performance. The model piles were penetrated and tested in three sand relative densities; dense, medium, and loose. Applied load (P), pile effective length (lc), pile flexural rigidity (EA), pile slenderness ratio (lc/d) and interface friction angle (δ) were identified, based on a comprehensive statistical analysis, as these parameters play a key role in governing pile settlement. To evaluate the efficiency and the generalisation ability of the proposed algorithm, graphical comparisons were made between the proposed algorithm and the experimental results with further comparisons made with conventional prediction approaches. The results revealed outstanding agreement between the targeted and predicted pile-load settlement with a coefficient of correlation of 0.985 and a Pearson's correlation coefficient, P = 2.22 x10-32 and root mean square error (RMSE) of 0.059 respectively. This, in parallel with a non-significant mean square error level (MSE) of 0.002, validates the feasibility of the proposed method and its potential in future applications.

show abstract

“…Approximately 1,000 data sets, obtained from the literature, were used for model development. Alkroosh and Nikraz (2011) developed ANN models for simulating the load-settlement behaviour of pile foundations embedded in sand or mixed soils, subjected to vertical loads.…”

Section: Introductionmentioning

confidence: 99%

Field and numerical analysis on effect of axial load on the lateral response of a pile

Tandel¹,

Vesmawala

2019

WJE

View full text Add to dashboard Cite

Purpose Piles often carry combination of axial and lateral. Currently, piles are designed separately for axial and lateral load. In the literature, few information is available on the influence of axial load on lateral behaviour of the pile. This paper aims to present the results of load deformation of a pile under pure lateral load and combined axial and lateral load. Design/methodology/approach The field load tests were carried out on four different pile diameters at two different bridge sites. Moreover, the paper addresses the numerical simulation of filed load test carried out on the pile under the combination of axial and horizontal load. Findings After field load tests and numerical simulation, it was found that the vertical load had a remarkable effect on the lateral load response of a pile. The lateral deflection of the pile was decreased about 25% under the effect of vertical load. In addition to this, the results from field and numerical simulation are quite comparable. Originality/value Typical field load tests were simulated numerically. This research adds a value in the areas of pile foundation subjected to vertical and lateral load particularly for structure such as transmission line tower and jetty.

show abstract

Simulating pile load-settlement behavior from CPT data using intelligent computing

Cited by 13 publications

References 14 publications

Peak Shear Strength of Discrete Fiber-Reinforced Soils Computed by Machine Learning and Metaensemble Methods

Peak Shear Strength of Discrete Fiber-Reinforced Soils Computed by Machine Learning and Metaensemble Methods

Performance analysis of an evolutionary LM algorithm to model the load-settlement response of steel piles embedded in sandy soil

Field and numerical analysis on effect of axial load on the lateral response of a pile

Contact Info

Product

Resources

About