Designing new hybrid artificial intelligence model for CFST beam flexural performance prediction

Hanoon, Ammar N.; Zand, Ahmed W. Al; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher

doi:10.1007/s00366-021-01325-7

Cited by 16 publications

(11 citation statements)

References 70 publications

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“…The motions of the search particles are therefore performed according to the cases that have a tendency to the global optimum and have the best condition. In this way, property (a) (attraction) is satisfied by these five cases [20].…”

Section: Computational Parameters Of Mceomentioning

confidence: 84%

“…Taking the previously mentioned extensive matrix sorting mechanism into consideration, an appropriate solution is provided to improve the problemsolving speed and prevent the trapping of the local optima of the new algorithm. One of the most important achievement of this strategy is high-accuracy and low-speed search where the optimum point probability is high and low-accuracy and high-speed search where the optimum point probability is low [20].…”

Section: Computational Parameters Of Mceomentioning

confidence: 99%

“…There has been a lot of research in the field of connection optimization, the most important of which are mentioned. Zhang et al [20] performed seismic optimization analysis of vertical stiffener connection to CFST column in a metamodel-based study. In this study 14 models in two series were designed and numerically analyzed based on the results of quasi-static tests of vertical stiffener connections to L-CFST columns.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimization of through-bolt steel beam connection to CFST column reinforced with rib plates using RSM method and MCEO algorithm

Soumeh

Sani

2021

NMCE

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Today, one of the most important engineering requirements is to ensure optimal design with best possible seismic performance of structures. To this end, the present paper aims to apply the optimization process for the design of the through-bolt steel beam connection to the concretefilled steel tube (CFST) column reinforced with rib plates. This study employs a multi-level cross-entropy optimizer (MCEO) algorithm along with response surface method (RSM) and finite element method (FEM) to establish the objective functions and constraints. The variables considered are the rib plate geometry and the steel and concrete strength parameters. In order to overcome problems, optimization is performed to increase the load-bearing capacity of the connection and to satisfy the constraints. Adopting this smart solution eliminates the need to connect finite elements for loop optimization and provides an explicit function for system performance. The results show that a very accurate analytical model can be developed to describe system performance using this process. This solution can optimize the performance of several systems that require a large amount of analysis and solve a wide range of structural optimization problems.

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Section: Computational Parameters Of Mceomentioning

confidence: 84%

Section: Computational Parameters Of Mceomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimization of through-bolt steel beam connection to CFST column reinforced with rib plates using RSM method and MCEO algorithm

Soumeh

Sani

2021

NMCE

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“…It is simpler to build than other optimization methods since it makes use of probabilistic transition rules and does not start with a seed solution. the number of [1,34]. When combined with other optimization tools, it can build hybrid tools that have several advantages over other optimization methods.…”

Section: Pso Algorithmmentioning

confidence: 99%

“…With the low water/cementitious materials ratio for high strength concrete, concrete durability can be improved. Compressive strength, on the other hand, causes concrete to become more brittle [1]. Steel fibers can improve section behavior [2,3], since they provide a bridging action across matrix microcracks and improve crack opening resistance [4].…”

Section: Introductionmentioning

confidence: 99%

The Behavior of Hybrid Fiber-Reinforced Concrete Elements: A New Stress-Strain Model Using an Evolutionary Approach

et al. 2022

Self Cite

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Several stress-strain models were used to predict the strengths of steel fiber reinforced concrete, which are distinctive of the material. However, insufficient research has been done on the influence of hybrid fiber combinations (comprising two or more distinct fibers) on the characteristics of concrete. For this reason, the researchers conducted an experimental program to determine the stress-strain relationship of 30 concrete samples reinforced with two distinct fibers (a hybrid of polyvinyl alcohol and steel fibers), with compressive strengths ranging from 40 to 120 MPa. A total of 80 % of the experimental results were used to develop a new empirical stress-strain model, which was accomplished through the application of the particle swarm optimization (PSO) tech-nique. It was discovered in this investigation that the new stress-strain model predictions are consistent with the remaining 20% of the experimental stress-strain curves obtained. Case studies of hybrid–fiber–reinforced concrete constructions were investigated in order to better understand the behavior of such elements. The data revealed that the proposed model has the highest absolute relative error (ARE) frequencies (ARE10 % and the lowest absolute relative error (ARE > 15 %) frequencies (ARE > 15 %).

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Fusion of experimental and synthetic data for reliable prediction of steel connection behaviour using machine learning

Cabrera

Ninić

Tizani

2023

Engineering with Computers

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The development of robust prediction tools based on machine learning (ML) techniques requires the availability of complete, consistent, accurate, and numerous datasets. The application of ML in structural engineering has been limited since, although real size experiments provide complete and accurate data, they are time-consuming and expensive. On the other hand, validated finite element (FE) models provide consistent and numerous synthetic data. Depending on the complexity of the problem, they might require large computational time and cost, and could be subjected to uncertainties and limitation in prediction capability given they are approximations of real-world problems. Hybrid approaches to combine experimental and synthetic datasets have emerged as an alternative to improve the reliability of ML model predictions. In this paper, we explore two hybrid methods to propose a robust approach for the prediction of the extended hollo-bolt (EHB) connection strength, stiffness, and column face displacement: (1) supervised ML methods with data fusion (DF) where learning is optimized with particle swarm optimization (PSO), and (2) artificial neural networks (ANN) based method with model fusion (MF). Based on the analysis of a dataset that combines 22 tensile experimental results with 2000 synthetic datapoints based on FE models, we concluded that using the first method (ML with DF and PSO) is the most suitable method for the prediction of the connection behavior. The ANN-based method with MF shows to be a promising method for the characterization of the EHB connection, however, more extensive experimental data is required for its implementation. Finally, a graphical user interface application was developed and shared in a public repository for the implementation of the proposed hybrid model.

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Designing new hybrid artificial intelligence model for CFST beam flexural performance prediction

Cited by 16 publications

References 70 publications

Optimization of through-bolt steel beam connection to CFST column reinforced with rib plates using RSM method and MCEO algorithm

Optimization of through-bolt steel beam connection to CFST column reinforced with rib plates using RSM method and MCEO algorithm

The Behavior of Hybrid Fiber-Reinforced Concrete Elements: A New Stress-Strain Model Using an Evolutionary Approach

Fusion of experimental and synthetic data for reliable prediction of steel connection behaviour using machine learning

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