Evaluation of liquefaction induced lateral displacements using genetic programming

Javadi, Akbar A.; Rezania, Mohammad; Nezhad, Mohaddeseh Mousavi

doi:10.1016/j.compgeo.2006.05.001

Cited by 141 publications

(52 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, the GP has been found successful in solving several problems in the field of geotechnical engineering (e.g. Javadi et al 2006;Rezania and Javadi 2007).…”

Section: Introductionmentioning

confidence: 99%

Correlation of Pile Axial Capacity and CPT Data Using Gene Expression Programming

Alkroosh

Nikraz

2011

Geotech Geol Eng

View full text Add to dashboard Cite

Numerous methods have been proposed to assess the axial capacity of pile foundations. Most of the methods have limitations and therefore cannot provide consistent and accurate evaluation of pile capacity. However, in many situations, the methods that correlate cone penetration test (CPT) data and pile capacity have shown to provide better results, because the CPT results provide more reliable soil properties. In an attempt to obtain more accurate correlation of CPT data with axial pile capacity, gene expression programming (GEP) technique is used in this study. The GEP is a relatively new artificial intelligent computational technique that has been recently used with success in the field of engineering. Three GEP models have been developed, one for bored piles and two other models for driven piles (a model for each of concrete and steel piles). The data used for developing the GEP models are collected from the literature and comprise a total of 50 bored pile load tests and 58 driven pile load tests (28 concrete pile load tests and 30 steel pile load tests) as well as CPT data. For each GEP model, the data are divided into a training set for model calibration and an independent validation set for model verification. The performances of the GEP models are evaluated by comparing their results with experimental data and the robustness of each model is investigated via sensitivity analyses. The performances of the GEP models are evaluated further by comparing their results with the results of number of currently used CPT-based methods. Statistical analyses are used for the comparison. The results indicate that the GEP models are robust and perform well.

show abstract

“…Recently, the GP has been found successful in solving several problems in the field of geotechnical engineering (e.g. Javadi et al 2006;Rezania and Javadi 2007).…”

Section: Introductionmentioning

confidence: 99%

Correlation of Pile Axial Capacity and CPT Data Using Gene Expression Programming

Alkroosh

Nikraz

2011

Geotech Geol Eng

View full text Add to dashboard Cite

show abstract

“…Several researchers (e.g., [34,50,[101][102][103][104]) have recently used the GP technique as an alterative to ANNs in order to obtain greatly simplified formulae for some geotechnical engineering problems. GP is a computing method that attempts to mimic the biological evolution of living organisms.…”

Section: Model Transparency and Knowledge Extractionmentioning

confidence: 99%

“…Although the liquefaction mechanism is well known, the prediction of liquefaction potential is very complex [47]. This fact has attracted many researchers to investigate the applicability of ANNs for predicting liquefaction [47][48][49][50][51][52][53][54][55].…”

Section: Introductionmentioning

confidence: 99%

Recent Advances and Future Challenges for Artificial Neural Systems in Geotechnical Engineering Applications

Shahin

Jaksa

Maier

2009

Advances in Artificial Neural Systems

120

View full text Add to dashboard Cite

Artificial neural networks (ANNs) are a form of artificial intelligence that has proved to provide a high level of competency in solving many complex engineering problems that are beyond the computational capability of classical mathematics and traditional procedures. In particular, ANNs have been applied successfully to almost all aspects of geotechnical engineering problems. Despite the increasing number and diversity of ANN applications in geotechnical engineering, the contents of reported applications indicate that the progress in ANN development and procedures is marginal and not moving forward since the mid-1990s. This paper presents a brief overview of ANN applications in geotechnical engineering, briefly provides an overview of the operation of ANN modeling, investigates the current research directions of ANNs in geotechnical engineering, and discusses some ANN modeling issues that need further attention in the future, including model robustness; transparency and knowledge extraction; extrapolation; uncertainty.

show abstract

“…another group of AI techniques, are based on statistical learning theory [30,31]; they are found to be better than the ANN model for some geotechnical engineering problems [32]. In the recent past, Genetic Programming (GP), which is a biologically inspired AI method, has been used as an AI technique to model di cult geotechnical engineering problems [33][34][35][36][37][38]. Alavi et al [39] found that a hybrid computational model (coupling of GP and simulated annealing) provided a better prediction performance than GP did, by predicting the uplift capacity of suction caisson using the above database (Rahman et al [26]).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Uplift Capacity of Suction Caisson in Clay Using Functional Network and Multivariate Adaptive Regression Spline

2017

View full text Add to dashboard Cite

Abstract. Suction caissons are extensively used as anchors for o shore foundation structures. The uplift capacity of suction caisson is an important factor with respect to e ective design. In this paper, two recently developed AI techniques, i.e. Functional Network (FN) and Multivariate Adaptive Regression Spline (MARS), have been used to predict the uplift capacity of suction caisson in clay. The performances of the developed models are compared with those of other AI techniques: arti cial neural network, support vector machine, relevance vector machine, genetic programming, extreme learning machine, and Group Method of Data Handling with Harmony Search (GMDH-HS). The model's inputs include the aspect ratio of the caisson, undrained shear strength of soil at the depth of the caisson tip, relative depth of the lug to which the caisson force is applied, load inclination angle, and load rate parameter. The results of the above AI techniques are comparatively analysed via di erent statistical performance criteria: correlation coe cient (R), root mean square error, Nash-Sutcli e coe cient of e ciency, and log-normal distribution of ratio of the predicted load capacity to observed load capacity, with a ranking system to determine the best predictive model. The FN and MARS models are found to be comparably e cient which can outperform other AI techniques.

show abstract

Evaluation of liquefaction induced lateral displacements using genetic programming

Cited by 141 publications

References 14 publications

Correlation of Pile Axial Capacity and CPT Data Using Gene Expression Programming

Correlation of Pile Axial Capacity and CPT Data Using Gene Expression Programming

Recent Advances and Future Challenges for Artificial Neural Systems in Geotechnical Engineering Applications

Prediction of Uplift Capacity of Suction Caisson in Clay Using Functional Network and Multivariate Adaptive Regression Spline

Contact Info

Product

Resources

About