Prediction of permeability and unconfined compressive strength of pervious concrete using evolved support vector regression

By Junbo Sun, Junfei Zhang, Yunfan Gu, Yimiao Huang, Yuantian Sun, Guowei Ma. Construction and Building Materials, 2019

165 citations recorded by the source corpus.

Abstract Pervious concrete is a widely used construction material thanks to its good drainage characteristics. Before application, its most important properties, i.e. the permeability coefficient (PC) and 28-day unconfined compressive strength (UCS) are required to be tested. However, conducting PC and UCS tests with multiple influencing variables is time-consuming and costly. To address this issue, this paper proposed, for the first time, an evolved support vector regression (ESVR) tuned by beetle antennae search (BAS) to accurately and effectively predict the PC and UCS of pervious concrete. To prepare the dataset of the ESVR model, 270 specimens in total were prepared and casted in a controlled environment in the laboratory. The water-to-cement (w/c) ratio, aggregate-to-cement (a/c) ratio, and aggregate size were selected as the crucial influencing variables for the inputs, while PC and UCS were the outputs of this model. The results indicate that both the PC and UCS firstly increased and then decreased with increasing w/c ratio. As the a/c ratio increased, PC increased, while UCS decreased. Moreover, BAS is more reliable and efficient than random hyper-parameter selection for hyper-parameter tuning. A low root-mean-square error (RMSE) and high correlation coefficient (R) indicate a relatively high predictive capability of the proposed ESVR model. The sensitivity analysis (SA) suggests the a/c ratio and aggregate size were the most sensitive variables for UCS and PC, respectively. This pioneering work provides a simple and convenient method for evaluating PC and UCS of pervious concrete.

Subfields: Civil and Structural Engineering, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/7e7c0ec774a93a11d1422ac3b9571e5c9cb2ff4f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. Yuantian Sun
2. Guichen Li
3. Deyu Qian
4. Junfei Zhang
5. Amir Mosavi
6. Alireza Ashrafian

In Environmental Engineering

1. Amir Mosavi
2. Alireza Ashrafian

Citations in the corpus

• Prediction of the Strength of Rubberized Concrete by an Evolved Random Forest Model (2019, methodology): “Furthermore, to minimalize the bias, a 10-fold crossvalidation method was introduced [35].”
• Classification-Based Regression Models for Prediction of the Mechanical Properties of Roller-Compacted Concrete Pavement (2020, methodology): “proposed extended SVM model to estimate permeability coefficient and unconfined compressive strength [18].”