Prediction of dynamic increase factor for steel fibre reinforced concrete using a hybrid artificial intelligence model

Lei Yang, Chongchong Qi, Xiaoshan Lin, Junwei Li, Xiangjian Dong

Research output: Contribution to journalArticle

Abstract

Steel fibre reinforced concrete (SFRC) has been increasingly used in the engineering structures subjected to intense dynamic loads. In structural design and analysis, a dynamic increase factor (DIF) has been usually used to characterize strain-rate effect on the dynamic mechanical behaviour of SFRC. At present, several analytical equations that contain one or two variables have been utilised to predict the DIF values for material strengths of SFRC. However, this may lead to unsatisfactory results as the rate sensitivity of SFRC is influenced by multiple variables. In this study, a hybrid model, integrating random forest (RF) technique and firefly algorithm (FA), is proposed for predicting DIF values for SFRC. RF is utilized to discover the non-linear relationship between the influencing variables and DIF, while FA optimizes the hyper-parameters of RF. A total of 193 and 314 DIF data samples for compressive and tensile strengths of SFRC are retrieved from the reported studies to train and verify the proposed model. The input variables for the predictive model include strain rate, matrix strength, fibre dosage, and fibre properties (i.e. fibre shape, fibre aspect ratio and fibre tensile strength). The predicted results denote that the developed model is an efficient and accurate method to predict the DIF values for SFRC. Additionally, the relative importance of each input variable is investigated. It is found that the DIF values of SFRC are most sensitive to the matrix strength.

Original languageEnglish
Pages (from-to)309-318
Number of pages10
JournalEngineering Structures
Volume189
DOIs
Publication statusPublished - 15 Jun 2019

Fingerprint

Steel fibers
Artificial intelligence
Reinforced concrete
Fibers
Strain rate
Tensile strength
Dynamic loads
Structural design
Structural analysis
Compressive strength
Strength of materials
Aspect ratio

Cite this

@article{f342082402ec4767bd9a7c5274d6f1b9,
title = "Prediction of dynamic increase factor for steel fibre reinforced concrete using a hybrid artificial intelligence model",
abstract = "Steel fibre reinforced concrete (SFRC) has been increasingly used in the engineering structures subjected to intense dynamic loads. In structural design and analysis, a dynamic increase factor (DIF) has been usually used to characterize strain-rate effect on the dynamic mechanical behaviour of SFRC. At present, several analytical equations that contain one or two variables have been utilised to predict the DIF values for material strengths of SFRC. However, this may lead to unsatisfactory results as the rate sensitivity of SFRC is influenced by multiple variables. In this study, a hybrid model, integrating random forest (RF) technique and firefly algorithm (FA), is proposed for predicting DIF values for SFRC. RF is utilized to discover the non-linear relationship between the influencing variables and DIF, while FA optimizes the hyper-parameters of RF. A total of 193 and 314 DIF data samples for compressive and tensile strengths of SFRC are retrieved from the reported studies to train and verify the proposed model. The input variables for the predictive model include strain rate, matrix strength, fibre dosage, and fibre properties (i.e. fibre shape, fibre aspect ratio and fibre tensile strength). The predicted results denote that the developed model is an efficient and accurate method to predict the DIF values for SFRC. Additionally, the relative importance of each input variable is investigated. It is found that the DIF values of SFRC are most sensitive to the matrix strength.",
keywords = "Dynamic increase factor, Firefly algorithm, Random forest, Steel fibre reinforced concrete, Variable importance",
author = "Lei Yang and Chongchong Qi and Xiaoshan Lin and Junwei Li and Xiangjian Dong",
year = "2019",
month = "6",
day = "15",
doi = "10.1016/j.engstruct.2019.03.105",
language = "English",
volume = "189",
pages = "309--318",
journal = "Engineering Structures",
issn = "0141-0296",
publisher = "Elsevier",

}

Prediction of dynamic increase factor for steel fibre reinforced concrete using a hybrid artificial intelligence model. / Yang, Lei; Qi, Chongchong; Lin, Xiaoshan; Li, Junwei; Dong, Xiangjian.

In: Engineering Structures, Vol. 189, 15.06.2019, p. 309-318.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Prediction of dynamic increase factor for steel fibre reinforced concrete using a hybrid artificial intelligence model

AU - Yang, Lei

AU - Qi, Chongchong

AU - Lin, Xiaoshan

AU - Li, Junwei

AU - Dong, Xiangjian

PY - 2019/6/15

Y1 - 2019/6/15

N2 - Steel fibre reinforced concrete (SFRC) has been increasingly used in the engineering structures subjected to intense dynamic loads. In structural design and analysis, a dynamic increase factor (DIF) has been usually used to characterize strain-rate effect on the dynamic mechanical behaviour of SFRC. At present, several analytical equations that contain one or two variables have been utilised to predict the DIF values for material strengths of SFRC. However, this may lead to unsatisfactory results as the rate sensitivity of SFRC is influenced by multiple variables. In this study, a hybrid model, integrating random forest (RF) technique and firefly algorithm (FA), is proposed for predicting DIF values for SFRC. RF is utilized to discover the non-linear relationship between the influencing variables and DIF, while FA optimizes the hyper-parameters of RF. A total of 193 and 314 DIF data samples for compressive and tensile strengths of SFRC are retrieved from the reported studies to train and verify the proposed model. The input variables for the predictive model include strain rate, matrix strength, fibre dosage, and fibre properties (i.e. fibre shape, fibre aspect ratio and fibre tensile strength). The predicted results denote that the developed model is an efficient and accurate method to predict the DIF values for SFRC. Additionally, the relative importance of each input variable is investigated. It is found that the DIF values of SFRC are most sensitive to the matrix strength.

AB - Steel fibre reinforced concrete (SFRC) has been increasingly used in the engineering structures subjected to intense dynamic loads. In structural design and analysis, a dynamic increase factor (DIF) has been usually used to characterize strain-rate effect on the dynamic mechanical behaviour of SFRC. At present, several analytical equations that contain one or two variables have been utilised to predict the DIF values for material strengths of SFRC. However, this may lead to unsatisfactory results as the rate sensitivity of SFRC is influenced by multiple variables. In this study, a hybrid model, integrating random forest (RF) technique and firefly algorithm (FA), is proposed for predicting DIF values for SFRC. RF is utilized to discover the non-linear relationship between the influencing variables and DIF, while FA optimizes the hyper-parameters of RF. A total of 193 and 314 DIF data samples for compressive and tensile strengths of SFRC are retrieved from the reported studies to train and verify the proposed model. The input variables for the predictive model include strain rate, matrix strength, fibre dosage, and fibre properties (i.e. fibre shape, fibre aspect ratio and fibre tensile strength). The predicted results denote that the developed model is an efficient and accurate method to predict the DIF values for SFRC. Additionally, the relative importance of each input variable is investigated. It is found that the DIF values of SFRC are most sensitive to the matrix strength.

KW - Dynamic increase factor

KW - Firefly algorithm

KW - Random forest

KW - Steel fibre reinforced concrete

KW - Variable importance

UR - http://www.scopus.com/inward/record.url?scp=85063478373&partnerID=8YFLogxK

U2 - 10.1016/j.engstruct.2019.03.105

DO - 10.1016/j.engstruct.2019.03.105

M3 - Article

VL - 189

SP - 309

EP - 318

JO - Engineering Structures

JF - Engineering Structures

SN - 0141-0296

ER -