Enhancing Marshall stability of asphalt concrete using a hybrid deep neural network and ensemble learning
Accurate prediction of Marshall Stability (MS) is vital for asphalt concrete mix design and performance evaluation, yet traditional laboratory methods are resource-intensive. This study proposes and evaluates hybrid machine learning models, specifically integrating a deep neural network (DNN) base l...
Saved in:
| Main Authors: | Henok Desalegn Shikur, Ming-Der Yang, Yared Bitew Kebede |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-12-01
|
| Series: | Case Studies in Construction Materials |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S221450952500960X |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Landslide Susceptibility Mapping Based on Ensemble Learning in the Jiuzhaigou Region, Sichuan, China
by: Bangsheng An, et al.
Published: (2024-11-01) -
Explainable machine learning model for predicting compressive strength of CO2-cured concrete
by: Jia Chu, et al.
Published: (2025-07-01) -
Explainable artificial intelligence (XAI) for interpreting predictive models and key variables in flood susceptibility
by: Bahram Choubin, et al.
Published: (2025-09-01) -
Prediction of splitting tensile strength of fiber-reinforced recycled aggregate concrete utilizing machine learning models with SHAP analysis
by: Md Al Adnan, et al.
Published: (2025-12-01) -
Predicting grip strength-related frailty in middle-aged and older Chinese adults using interpretable machine learning models: a prospective cohort study
by: Lisheng Yu, et al.
Published: (2024-12-01)