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Article

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Title

Prediction of reinforced concrete walls shear strength based on soft computing-based techniques

Authors

[ 1 ] Instytut Budownictwa, Wydział Inżynierii Lądowej i Transportu, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.7] Civil engineering, geodesy and transport

Year of publication

2023

Published in

Soft Computing

Journal year: 2023 | Journal volume: in press

Article type

scientific article

Publication language

english

Keywords
EN
  • Soft computing techniques
  • ANFIS
  • RF
  • PSO
  • Reinforced concrete wall
  • Shear strength
Abstract

EN The precise estimation of the shear strength of reinforced concrete walls is critical for structural engineers. This projection, nevertheless, is exceedingly complicated because of the varied structural geometries, plethora of load cases, and highly nonlinear relationships between the design requirements and the shear strength. Recent related design code regulations mostly depend on experimental formulations, which have a variety of constraints and establish low prediction accuracy. Hence, different soft computing techniques are used in this study to evaluate the shear capacity of reinforced concrete walls. In particular, developed models for estimating the shear capacity of concrete walls have been investigated, based on experimental test data accessible in the relevant literature. Adaptive neuro-fuzzy inference system, the integrated genetic algorithms, and the integrated particle swarm optimization methods were used to optimize the fuzzy model’s membership function range and the results were compared to the outcomes of random forests (RF) model. To determine the accuracy of the models, the results were assessed using several indices. Outliers in the anticipated data were identified and replaced with appropriate values to ensure prediction accuracy. The comparison of the resulting findings with the relevant experimental data demonstrates the potential of hybrid models to determine the shear capacity of reinforced concrete walls reliably and effectively. The findings revealed that the RF model with RMSE = 151.89, MAE = 111.52, and R2 = 0.9351 has the best prediction accuracy. Integrated GAFIS and PSOFIS performed virtually identically and had fewer errors than ANFIS. The sensitivity analysis shows that the thickness of the wall (bw) and concrete compressive strength (fc) have the most and the least effects on shear strength, respectively.

Date of online publication

20.07.2023

DOI

10.1007/s00500-023-08974-4

URL

https://link.springer.com/article/10.1007/s00500-023-08974-4

License type

CC BY (attribution alone)

Open Access Mode

czasopismo hybrydowe

Open Access Text Version

final published version

Date of Open Access to the publication

in press

Ministry points / journal

70

Impact Factor

3,1

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