نوع مقاله : پژوهشی
نویسندگان
1 دانشکدهی فنی، دانشگاه گیلان
2 دانشکدهی مهندسی عمران، دانشگاه صنعتی امیرکبیر
چکیده
کلیدواژهها
عنوان مقاله [English]
نویسندگان [English]
Piles have been used for many years as a type of structural foundation. The design of pile foundations and estimation of static pile bearing capacities, based on measured soil properties, have been improved considerably over the years. However, due to inherent soil uncertainities and disturbances, there is always an element of uncertainty about the design capacity. Therefore, most theoretical approaches have been mainly based on simplifications and assumptions. The cone penetration test (CPT) is considered as one of the most useful in situ tests for the characterization of soil. Due to the similarity between the cone and the pile, determination of the pile capacity from CPT data is among its earliest applications. The measured cone resistance $(q_{c})$ and
sleeve friction $(f_{s})$ usually are employed for estimation of the pile Tip and shaft resistances, respectively. Over the last few years or so, the use of artificial neural networks (ANNs) has increased in many areas of engineering.
In particular, ANNs have been applied to many geotechnical engineering problems and have demonstrated some degree of success. Group method of data handling (GMDH) type neural networks optimized using genetic algorithms (GAs), are used to model the effects of effective cone Tip resistance $(q_{E})$ and cone sleeve
friction $(f_{s})$ as input parameters on pile Tip resistance, by applying some experimentally obtained training and test data. 29 pile case histories have been compiled, including static and dynamic loading tests, performed at sites, including CPT sounding. The pile embedment lengths range from 9 m through 31 m.
The pile Tip resistances range from 0.4 MPa through 29.4 MPa. A sensitivity analysis of the obtained model has been carried out to study the influence of input parameters on model output. According to the sensitivity analysis results, the pile Tip resistance $(r_{t})$ is considerably influenced by the effective cone Tip resistance $(q_{E})$, and the value rises by increasing qE values. Also, for a constant value of effective cone Tip resistance
$(q_{E})$, by decreasing the cone sleeve friction $(f_{s})$, the pile Tip resistance increases. Pile toe capacities calculated by the proposed method are compared Tip capacities calculated by five other direct methods. The proposed method gives values that are more consistent and closer to measured ones than current methods. The results demonstrate that the proposed method gives values that are consistent and close to the measured ones.
کلیدواژهها [English]
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