Reinforcing bar development length modeling using integrative support vector regression model with response surface method: New approach

Behrooz Keshtegar; Zaher Mundher Yaseen

doi:10.1016/j.isatra.2021.10.010

Reinforcing bar development length modeling using integrative support vector regression model with response surface method: New approach

ISA Trans. 2022 Sep;128(Pt A):423-434. doi: 10.1016/j.isatra.2021.10.010. Epub 2021 Oct 20.

Authors

Behrooz Keshtegar¹, Zaher Mundher Yaseen²

Affiliations

¹ Department of Civil Engineering, Faculty of Engineering, University of Zabol, P.B. 9861335- 856, Zabol, Iran.
² Department of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University, 76, Lenin Prospect, 454080 Chelyabinsk, Russia; New era and development in civil engineering research group, Scientific Research Center, Al- Ayen University, Thi-Qar, 64001, Iraq; College of Creative Design, Asia University, Taichung City, Taiwan. Electronic address: zaheryaseen88@gmail.com.

PMID: 34753602
DOI: 10.1016/j.isatra.2021.10.010

Abstract

Due to the non-uniformity of bond stress distribution, the full bar development length should be tested to validate the development length of the reinforcing bar embedded in concretes. The current study proposed the design of a hybrid artificial intelligence (AI) model using integration of support vector regression (SVR) coupled with response surface method (RSM) for prediction of reinforcement bar development length. Two nonlinear calibrating processes are conducted, RSM is used to connect the input data on the hardening dataset in first stage while SVR is used for determining the nonlinear relation between the hardening dataset and the output development bar stress. 534 pull-out test observations on short unit bar length are used for the modeling. Several physical dimensional properties are incorporated as input attributes for the hybrid predictive model. Stand-alone AI models, empirical formulations, and design codes are used for validation. Parametric analysis is performed to investigate the sensitivity of RSM-SVR model toward as input variables. Results evidenced the capability of the proposed RSM-RVM model to approximate the non-linear relations between physical information and bar development length. The RSM-SVR model proved to be a reliable intelligence model for bar materials design. Additionally, the model was highly consistent in the calibration of the existing design codes for more reliability. In quantitative terms, RSM-SVR model attained minimum root mean square error (RMSE = 25.60 MPa), compared with for example ACI 318-14 modeling design (306.56 MPa) or stand-alone SVR model (90.95 MPa), over the testing phase. The bar development length formulation using RSM-SVR model showed the nonlinear relationship with respect to the influence of the input variables such as transvers bars in development region, reinforcing yield stress, and concrete compressive strength in normal domain.

Keywords: Bar development length prediction; Bond strength; Computer aid model; Parametric analysis; Reinforced material.

MeSH terms

Artificial Intelligence*
Reproducibility of Results