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A machine learning regression approach for predicting the bearing capacity of a strip footing on rock mass under inclined and eccentric load

Overview of attention for article published in Frontiers in Built Environment, September 2022
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1 X user

Citations

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18 Dimensions

Readers on

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16 Mendeley
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Title
A machine learning regression approach for predicting the bearing capacity of a strip footing on rock mass under inclined and eccentric load
Published in
Frontiers in Built Environment, September 2022
DOI 10.3389/fbuil.2022.962331
Authors

Van Qui Lai, Kongtawan Sangjinda, Suraparb Keawsawasvong, Alireza Eskandarinejad, Vinay Bhushan Chauhan, Worathep Sae-Long, Suchart Limkatanyu

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Other 2 13%
Lecturer 1 6%
Student > Bachelor 1 6%
Student > Ph. D. Student 1 6%
Student > Master 1 6%
Other 2 13%
Unknown 8 50%
Readers by discipline Count As %
Engineering 5 31%
Social Sciences 2 13%
Business, Management and Accounting 1 6%
Unknown 8 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 September 2022.
All research outputs
#18,900,960
of 23,419,482 outputs
Outputs from Frontiers in Built Environment
#570
of 1,108 outputs
Outputs of similar age
#305,348
of 438,858 outputs
Outputs of similar age from Frontiers in Built Environment
#41
of 111 outputs
Altmetric has tracked 23,419,482 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,108 research outputs from this source. They receive a mean Attention Score of 2.9. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 438,858 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 111 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.