-
Prediction of Discharge Capacity of Labyrinth Weir with Gene Expression Programming
This paper proposes a model based on gene expression programming for pre...
read it
-
Network Pruning for Low-Rank Binary Indexing
Pruning is an efficient model compression technique to remove redundancy...
read it
-
A fine-grained policy model for Provenance-based Access Control and Policy Algebras.pdf
A fine-grained provenance-based access control policy model is proposed ...
read it
-
Using neural networks to predict icephobic performance
Icephobic surfaces inspired by superhydrophobic surfaces offer a passive...
read it
-
Practical Fine-grained Privilege Separation in Multithreaded Applications
An inherent security limitation with the classic multithreaded programmi...
read it
-
Towards More Fine-grained and Reliable NLP Performance Prediction
Performance prediction, the task of estimating a system's performance wi...
read it
-
Performance optimization and modeling of fine-grained irregular communication in UPC
The UPC programming language offers parallelism via logically partitione...
read it
Prediction of Compression Index of Fine-Grained Soils Using a Gene Expression Programming Model
In construction projects, estimation of the settlement of fine-grained soils is of critical importance, and yet is a challenging task. The coefficient of consolidation for the compression index (Cc) is a key parameter in modeling the settlement of fine-grained soil layers. However, the estimation of this parameter is costly, time-consuming, and requires skilled technicians. To overcome these drawbacks, we aimed to predict Cc through other soil parameters, i.e., the liquid limit (LL), plastic limit (PL), and initial void ratio (e0). Using these parameters is more convenient and requires substantially less time and cost compared to the conventional tests to estimate Cc. This study presents a novel prediction model for the Cc of fine-grained soils using gene expression programming (GEP). A database consisting of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate Cc based on LL, PL, and e0. The performance of the developed GEP-based model was evaluated through the coefficient of determination (R2), the root mean squared error (RMSE), and the mean average error (MAE). The proposed model performed better in terms of R2, RMSE, and MAE compared to the other models.
READ FULL TEXT
Comments
There are no comments yet.