Intelligent computing for predicting axial capacity of drilled shafts
MetadataShow full item record
This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil Engineers.
In the last few decades, numerous methods have been developed for predicting the axial capacity of drilled shafts. Among the available methods, the cone penetration test (CPT) based models have been shown to give better predictions in many situations. This can be attributed to the fact that CPT-based methods have been developed in accordance with the results of the CPT tests, which have been found to yield more reliable soil properties, hence, more accurate axial capacity predictions of drilled shafts. In this paper, one of the most commonly used artificial intelligence techniques, i.e. artificial neural networks (ANNs), was utilized in an attempt to obtain more accurate axial capacity predictions for drilled shafts. The ANN model was developed using data collected from the literature that comprise CPT results and drilled shaft load tests of 94 case records. The predictions from the ANN model were compared with those obtained from three commonly used available CPT-based methods. The results indicate that the ANN-based model provides more accurate axial capacity predictions of drilled shafts and outperforms the available conventional methods.
Showing items related by title, author, creator and subject.
Shahin, Mohamed (2010)In the last few decades, numerous methods have been developed for predicting the axial capacity of pile foundations. Among the available methods, the cone penetration test (CPT)-based models have been shown to give better ...
Load-settlement modelling of axially loaded drilled shafts using CPT-based recurrent neural networksShahin, Mohamed (2014)The design of pile foundations requires good estimation of the pile load-carrying capacity and settlement. Design for bearing capacity and design for settlement have been traditionally carried out separately. However, ...
Modelling pile capacity and load-settlement behaviour of piles embedded in sand & mixed soils using artificial intelligenceAlkroosh, Iyad Salim Jabor (2011)This thesis presents the development of numerical models which are intended to be used to predict the bearing capacity and the load-settlement behaviour of pile foundations embedded in sand and mixed soils. Two artificial ...