New design equations for estimation of ultimate bearing capacity of shallow foundations resting on rock masses
Rock masses are commonly used as the underlying layer of important structures such as bridges, dams and transportation constructions. The success of a foundation design for such structures mainly depends on the accuracy of estimating the bearing capacity of rock beneath them. Several traditional num...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2016-01-01
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| Series: | Geoscience Frontiers |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1674987114001625 |
| _version_ | 1797766180983799808 |
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| author | Amir H. Alavi Ehsan Sadrossadat |
| author_facet | Amir H. Alavi Ehsan Sadrossadat |
| author_sort | Amir H. Alavi |
| collection | DOAJ |
| description | Rock masses are commonly used as the underlying layer of important structures such as bridges, dams and transportation constructions. The success of a foundation design for such structures mainly depends on the accuracy of estimating the bearing capacity of rock beneath them. Several traditional numerical approaches are proposed for the estimation of the bearing capacity of foundations resting on rock masses to avoid performing elaborate and expensive experimental studies. Despite this fact, there still exists a serious need to develop more robust predictive models. This paper proposes new nonlinear prediction models for the ultimate bearing capacity of shallow foundations resting on non-fractured rock masses using a novel evolutionary computational approach, called linear genetic programming. A comprehensive set of rock socket, centrifuge rock socket, plate load and large-scaled footing load test results is used to develop the models. In order to verify the validity of the models, the sensitivity analysis is conducted and discussed. The results indicate that the proposed models accurately characterize the bearing capacity of shallow foundations. The correlation coefficients between the experimental and predicted bearing capacity values are equal to 0.95 and 0.96 for the best LGP models. Moreover, the derived models reach a notably better prediction performance than the traditional equations. |
| first_indexed | 2024-03-12T20:21:31Z |
| format | Article |
| id | doaj.art-8bbd3b8e72ba48738a6226a028bb64b2 |
| institution | Directory Open Access Journal |
| issn | 1674-9871 |
| language | English |
| last_indexed | 2024-03-12T20:21:31Z |
| publishDate | 2016-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Geoscience Frontiers |
| spelling | doaj.art-8bbd3b8e72ba48738a6226a028bb64b22023-08-02T00:55:56ZengElsevierGeoscience Frontiers1674-98712016-01-0171919910.1016/j.gsf.2014.12.005New design equations for estimation of ultimate bearing capacity of shallow foundations resting on rock massesAmir H. Alavi0Ehsan Sadrossadat1Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI 48823, USADepartment of Civil Engineering, Mashhad Branch, Islamic Azad University, Mashhad, IranRock masses are commonly used as the underlying layer of important structures such as bridges, dams and transportation constructions. The success of a foundation design for such structures mainly depends on the accuracy of estimating the bearing capacity of rock beneath them. Several traditional numerical approaches are proposed for the estimation of the bearing capacity of foundations resting on rock masses to avoid performing elaborate and expensive experimental studies. Despite this fact, there still exists a serious need to develop more robust predictive models. This paper proposes new nonlinear prediction models for the ultimate bearing capacity of shallow foundations resting on non-fractured rock masses using a novel evolutionary computational approach, called linear genetic programming. A comprehensive set of rock socket, centrifuge rock socket, plate load and large-scaled footing load test results is used to develop the models. In order to verify the validity of the models, the sensitivity analysis is conducted and discussed. The results indicate that the proposed models accurately characterize the bearing capacity of shallow foundations. The correlation coefficients between the experimental and predicted bearing capacity values are equal to 0.95 and 0.96 for the best LGP models. Moreover, the derived models reach a notably better prediction performance than the traditional equations.http://www.sciencedirect.com/science/article/pii/S1674987114001625Rock mass propertiesUltimate bearing capacityShallow foundationPredictionEvolutionary computationLinear genetic programming |
| spellingShingle | Amir H. Alavi Ehsan Sadrossadat New design equations for estimation of ultimate bearing capacity of shallow foundations resting on rock masses Geoscience Frontiers Rock mass properties Ultimate bearing capacity Shallow foundation Prediction Evolutionary computation Linear genetic programming |
| title | New design equations for estimation of ultimate bearing capacity of shallow foundations resting on rock masses |
| title_full | New design equations for estimation of ultimate bearing capacity of shallow foundations resting on rock masses |
| title_fullStr | New design equations for estimation of ultimate bearing capacity of shallow foundations resting on rock masses |
| title_full_unstemmed | New design equations for estimation of ultimate bearing capacity of shallow foundations resting on rock masses |
| title_short | New design equations for estimation of ultimate bearing capacity of shallow foundations resting on rock masses |
| title_sort | new design equations for estimation of ultimate bearing capacity of shallow foundations resting on rock masses |
| topic | Rock mass properties Ultimate bearing capacity Shallow foundation Prediction Evolutionary computation Linear genetic programming |
| url | http://www.sciencedirect.com/science/article/pii/S1674987114001625 |
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