Name
Title | ||
|---|---|---|
Artificial neural network methodology for soil liquefaction evaluation using CPT values |
Abstract | ||
|---|---|---|
With the 413 soil liquefaction records with cone penetration testing values collected after strong earthquakes, the Bayesian Regularization Back Propagation Neural Networks (BRBPNN) method was presented to evaluate the soil liquefaction potential in this paper. Cone resistance (qc), equivalent dynamic shear stress (τ / σ′0), mean grain size (D50), total stress (σ0), the effective stress (σ′0), earthquake magnitude (M) and the normalized acceleration horizontal at ground surface (a / g) are used as input parameters for networks. Four networks are constructed for different source of input data. The model M7 seems more efficient for the given data, since it only contain 109 records. The model M5 contains 413 samples, and the correct ratio for training data and testing data are 88.5% and 90% respectively. By compared with the square of the weight of the input layer for each network, the importance order of the input parameters should be qc,M,σ′0,σ0,a / g,τ / σ′0 and D50. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11816157_36 | ICIC (1) |
Keywords | Field | DocType |
input data,soil liquefaction evaluation,equivalent dynamic shear stress,model m5,training data,total stress,cone resistance,effective stress,cone penetration testing,input layer,input parameter,cpt value,artificial neural network methodology,bayesian regularization,grain size,shear stress,artificial neural network,cone penetration test | Soil liquefaction,Cone penetration test,Soil science,Effective stress,Pattern recognition,Standard penetration test,Shear stress,Computer science,Test data,Artificial intelligence,Backpropagation,Artificial neural network | Conference |
Volume | ISSN | ISBN |
4113 | 0302-9743 | 3-540-37271-7 |
Citations | PageRank | References |
1 | 0.35 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ben-yu Liu | 1 | 1 | 1.02 |
| Liao-yuan Ye | 2 | 7 | 1.97 |
| Mei-ling Xiao | 3 | 1 | 1.02 |
| Sheng Miao | 4 | 7 | 1.74 |