The potential of nonparametric model in foundation bearing capacity prediction

Jabbar, Saadya Fahad; Hamed, Raed I.; Alwan, Asmaa Hussein

doi:10.1007/s00521-017-2916-9

Cited by 11 publications

(2 citation statements)

References 36 publications

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“…The penetration depth is not only related to the soil engineering properties, but also limited by the thrust and pulling force of the sounding equipment. Generally, the penetration depth of 200 kN CPT equipment in soft soil can exceed 70 m, and the penetration depth in dense sand layer can exceed 30 m [8][9][10].…”

Section: Static Penetration Test Calculates Foundation Bearingmentioning

confidence: 99%

Study on the Influence of Water Immersion in Loess Stratum on the Bearing Capacity of Foundation Bearing Layer of Pipe Gallery

Pan

Hui³

et al. 2022

Geofluids

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Aiming at the influence of water immersion in the loess stratum on the bearing capacity of the pipe gallery foundation, taking the prefabricated pipe gallery demonstration project in Xiongan New District as the research object, the static penetration test and indoor triaxial test of the foundation bearing layer of the pipe gallery before and after water immersion were carried out. In the experiment, the loss law of loess foundation bearing capacity before and after water immersion was analyzed by using the obtained soil physical index changes. The research results show that the foundation bearing capacity calculated using the static penetration test data before and after immersion are in good agreement with the field plate load test results, and the eigenvalues of the foundation bearing capacity calculated using the shear strength index are higher than the field measured results. According to the results of the static penetration test, the bearing capacity of the foundation after immersion will decrease, but the reduction range is within 4%, and there is a certain loss; the bearing capacity of the foundation before and after immersion calculated according to the shear strength index is reduced by 2.5%. It shows that the bearing capacity of the foundation will be lost to a certain extent after immersion in water, but the loss is small. It is recommended to strengthen drainage measures in the later stage to prevent the foundation from being soaked by rainwater.

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Section: Static Penetration Test Calculates Foundation Bearingmentioning

confidence: 99%

Study on the Influence of Water Immersion in Loess Stratum on the Bearing Capacity of Foundation Bearing Layer of Pipe Gallery

Pan

Hui³

et al. 2022

Geofluids

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“…Consequently, efforts have been devoted to the development of efficient optimization algorithms for the evaluation of several functions. Several novel approaches have been proposed in recent times with some satisfactory performances with fewer function evaluations [28], [34]- [38].…”

Section: Overviewmentioning

confidence: 99%

A classification model on tumor cancer disease based mutual information and firefly algorithm

Jabar¹

2019

PEN

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Cancer is a globally recognized cause of death. A proper cancer analysis demands the classification of several types of tumor. Investigations into microarray gene expressions seem to be a successful platform for revising genetic diseases. Although the standard machine learning (ML) approaches have been efficient in the realization of significant genes and in the classification of new types of cancer cases, their medical and logical application has faced several drawbacks such as DNA microarray data analysis limitation, which includes an incredible number of features and the relatively small size of an instance. To achieve a reasonable and efficient DNA microarray dataset information, there is a need to extend the level of interpretability and forecast approach while maintaining a great level of precision. In this work, a novel way of cancer classification based on based gene expression profiles is presented. This method is a combination of both Firefly algorithm and Mutual Information Method. First, the features are used to select the features before using the Firefly algorithm for feature reduction. Finally, the Support Vector Machine is used to classify cancer into types. The performance of the proposed system was evaluated by using it to classify datasets from colon cancer; the results of the evaluation were compared with some recent approaches.

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