Efficient Swell Risk Prediction for Building Design Using a Domain-Guided Machine Learning Model
Expansive clays damage the foundations, slabs, and utilities of low- and mid-rise buildings, threatening daily operations and incurring billions of dollars in costs globally. This study pioneers a domain-informed machine learning framework, coupled with a collinearity-aware feature selection strateg...
Saved in:
| Main Author: | Hani S. Alharbi |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/15/14/2530 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Empirical Correlations for the Swelling Pressure of Expansive Clays in the City of Barranquilla, Colombia
by: Victor Cantillo, et al.
Published: (2017-01-01) -
Optimization of glass debris in the treatment of swelling clay soils using recycled materials for sustainable road engineering
by: Noureddine Ouslimane, et al.
Published: (2025-07-01) -
Predicting Clay Swelling Pressure: A Comparative Analysis of Advanced Symbolic Regression Techniques
by: Esteban Díaz, et al.
Published: (2025-05-01) -
Evaluation of Temperature Variation Effects on the Swelling Characteristics of Fine-graded Soils improvement with Sodium Alginate in Constant Volume condition
by: M. Behzadipour, et al.
Published: (2024-09-01) -
Predictive Modeling of Heart Failure Outcomes Using ECG Monitoring Indicators and Machine Learning
by: Jia Liu, et al.
Published: (2025-07-01)