Working Paper Article Version 1 This version is not peer-reviewed

Predicting Compression Index of Fine-Grained Soils Using Gene Expression Programming Model

Version 1 : Received: 23 March 2019 / Approved: 25 March 2019 / Online: 25 March 2019 (10:25:18 CET)

A peer-reviewed article of this Preprint also exists.

Mohammadzadeh S., D.; Kazemi, S.-F.; Mosavi, A.; Nasseralshariati, E.; Tah, J.H.M. Prediction of Compression Index of Fine-Grained Soils Using a Gene Expression Programming Model. Infrastructures 2019, 4, 26. Mohammadzadeh S., D.; Kazemi, S.-F.; Mosavi, A.; Nasseralshariati, E.; Tah, J.H.M. Prediction of Compression Index of Fine-Grained Soils Using a Gene Expression Programming Model. Infrastructures 2019, 4, 26.

Abstract

Appropriate estimation of soil settlement is of significant importance since it directly influences the performance of building and infrastructures that are built on soil. In particular, the settlement of fine-grained soils is critical because of low permeability and continuous settlement with time. Coefficient of consolidation (Cc) is a key parameter to estimate settlement of fine-grained soil layers. However, estimation of this parameter is time consuming, needs skilled technicians, and specific equipment. In this study, Cc was estimated using several soil parameters such as liquid limit (LL), plastic limit (PL), and initial void ratio (e0). Estimating such parameters in laboratory is straight forward and needs substantially less time and cost compared to conventional tests to estimate Cc such as Oedometer test. This study presents a novel prediction model for Cc of fine-grained soils using gene-expression programming (GEP). GEP is a biologically inspired technique capable of offering closed-form solution for the optimal solution. A database consisted 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 developed GEP-based model was evaluated through coefficient of determination (R2), root mean squared error (RMSE), and mean average error (MAE). High R2 and low error values indicated the descent performance of the model. Furthermore, the model was evaluated using the additional performance measures and met all the suggested criteria. Furthermore, the model had a better performance in terms of R2, RMSE, and MAE compared to most of existing models. It is expected that the developed model will decrease the time and cost associate with determining Cc of fine-grained soils.

Keywords

evolutionary model, gene-expression programming (GEP), prediction, soil compression index, estimation, soil engineering, soil informatics, civil engineering, machine learning, data science, big data, soft computing, deep learning, forecasting, subject classification codes, construction informatics, computational intelligence (CI), artificial intelligence (AI), estimation

Subject

Engineering, Civil Engineering

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.

Leave a public comment
Send a private comment to the author(s)
* All users must log in before leaving a comment
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.