Dataset on the long-term monitoring of foundation vertical deformations on medium-expansive soilMendeley Data
This paper presents a complete data set from the long-term field monitoring of vertical deformation in four footings resting on medium-expansive soil. The four key variables (vertical deformation, daily average air temperature, weekly cumulative rainfall, and soil water content at a depth of 60 cm)...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-04-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925001544 |
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| Summary: | This paper presents a complete data set from the long-term field monitoring of vertical deformation in four footings resting on medium-expansive soil. The four key variables (vertical deformation, daily average air temperature, weekly cumulative rainfall, and soil water content at a depth of 60 cm) were recorded over a period of 974 days. The vertical deformations were measured with high-precision dial gauges. At the same time, the advanced instruments, Bosch GLL 3-80 G Professional line laser and LEICA DNA 10 digital levels were cross-used for measurement to ensure the accuracy and reliability of the results. The data collection was designed to include the effects of expansive soil properties, such as swelling during wet seasons and shrinkage during dry seasons. This is necessary for understanding the soil-structure interaction under natural field conditions, which differs considerably from controlled laboratory studies. This dataset presents a great possibility of being reused by researchers to support further studies on soil-structure interaction, develop predictive models for expansive soils, and analyze long-term structural stability. It is particularly useful in developing machine learning algorithms that can be used to predict foundation behavior in response to different environmental conditions, optimize foundation designs on expansive soils, and specifically predict foundation heave. The availability of this dataset provides an invaluable resource in advancing geotechnical engineering research. |
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| ISSN: | 2352-3409 |