Hybrid intelligence modeling for estimating shear strength of FRP reinforced concrete members

Alam, Shah; Sultana, Nahid; Hossain, S. M. Zakir; Islam, Mohammad S.

doi:10.1007/s00521-021-06791-0

Cited by 6 publications

(1 citation statement)

References 51 publications

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“…The results revealed that utilized method gained more accurate results as comparing with that used in literatures. Alam et al 48 presented a hybrid ML called ANN-BOA for V s prediction of reinforced concrete elements with FRP reinforcement without stirrups. The study indicated that the presented model showed better results than traditional ANN and empirical equations.…”

Section: Introductionmentioning

confidence: 99%

Machine learning models development for shear strength prediction of reinforced concrete beam: a comparative study

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

2023

Sci Rep

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Fiber reinforced polymer (FPR) bars have been widely used as a substitutional material of steel reinforcement in reinforced concrete elements in corrosion areas. Shear resistance of FRP reinforced concrete element can be affected by concrete properties and transverse FRP stirrups. Hence, studying the shear strength (Vs) mechanism is one of the highly essential for pre-design procedure for reinforced concrete elements. This research examines the ability of three machine learning (ML) models called M5-Tree (M5), extreme learning machine (ELM), and random forest (RF) in predicting Vs of 112 shear tests of FRP reinforced concrete beam with transverse reinforcement. For generating the prediction matrix of the developed ML models, statistical correlation analysis was conducted to generate the suitable inputs models for Vs prediction. Statistical evaluation and graphical approaches were used to evaluate the efficiency of the proposed models. The results revealed that all the proposed models performed in general well for all the input combinations. However, ELM-M1 and M5-Tree-M5 models exhibited less accuracy performance in comparison with the other developed models. The study showed that the best prediction performance was revealed by M5 tree model using nine input parameters, with coefficient of determination (R2) and root mean square error (RMSE) equal to 0.9313 and 35.5083 KN, respectively. The comparison results also indicated that ELM and RF were performed significant results with a less slight performance than M5 model. The study outcome contributes to basic knowledge of investigating the impact of stirrups on Vs of FRP reinforced concrete beam with the potential of applying different computer aid models.

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Section: Introductionmentioning

confidence: 99%

Machine learning models development for shear strength prediction of reinforced concrete beam: a comparative study

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

2023

Sci Rep

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Artificial intelligence techniques in advanced concrete technology: A comprehensive survey on 10 years research trend

Kazemi

2023

Engineering Reports

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Advanced concrete technology is the science of efficient, cost‐effective, and safe design in civil engineering projects. Engineers and concrete designers are generally faced with the slightest change in the conditions or objectives of the project, which makes it challenging to choose the optimal design among several ones. Besides, the experimental examination of all of them requires time and high costs. Hence, an efficient approach is to utilize artificial intelligence (AI) techniques to predict and optimize real‐world problems in concrete technology. Despite the large body of publications in this field, there are few comprehensive surveys that conduct scientometric analysis. This paper provides a state‐of‐the‐art review that lists, summarizes, and categorizes the most widely used machine learning methods, meta‐heuristic algorithms, and hybrid approaches to concrete issues. To this end, 457 publications are considered during the recent decade with a scientometric approach to highlight the annual trend/active journals/top researchers/co‐occurrence of key title words/countries' participation/research hotspots. In addition, AI techniques are classified into distinct clusters using VOSviewer clustering visualization to identify the application scope and their relationship through the link strength. The findings can be a beacon to help researchers utilize AI techniques in future research on advanced concrete technology.

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Assessment of shear capacity of a FRP-reinforced concrete beam without stirrup

Kumar,

Arora,

Kumar

2024

Artificial Intelligence Applications for Sustainable Construction

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Hybrid intelligence modeling for estimating shear strength of FRP reinforced concrete members

Cited by 6 publications

References 51 publications

Machine learning models development for shear strength prediction of reinforced concrete beam: a comparative study

Machine learning models development for shear strength prediction of reinforced concrete beam: a comparative study

Artificial intelligence techniques in advanced concrete technology: A comprehensive survey on 10 years research trend

Assessment of shear capacity of a FRP-reinforced concrete beam without stirrup

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