Simulation of steel–concrete composite floor system behavior at elevated temperatures via multi-hybrid metaheuristic framework

Morasaei, Armin; Ghabussi, Aria; Aghlmand, Soheila; Yazdani, Maziar; Baharom, Shahrizan; Assilzadeh, Hamid

doi:10.1007/s00366-020-01228-z

Cited by 27 publications

(7 citation statements)

References 117 publications

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“…Operation Research is a branch of study that use sophisticated analytical techniques to comprehend complicated systems and make appropriate decisions [ 29 , 30 , 31 ]. OR assists firms and organisations in a variety of methods, including simulation approaches [ 32 , 33 , 34 ], mathematical optimisation [ 35 , 36 ], and decision analysis [ 37 , 38 ]. OR, with its focus on boosting efficiency [ 39 , 40 ], cost-effectiveness [ 41 , 42 ], and decision making [ 43 , 44 ], is especially useful for analysing complex health sectors challenges.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Hybrid Parametric and Non-Parametric Optimisation Model for Average Technical Efficiency Assessment in Public Hospitals during and Post-COVID-19 Pandemic

et al. 2021

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The COVID-19 pandemic has had a significant impact on hospitals and healthcare systems around the world. The cost of business disruption combined with lingering COVID-19 costs has placed many public hospitals on a course to insolvency. To quickly return to financial stability, hospitals should implement efficiency measure. An average technical efficiency (ATE) model made up of data envelopment analysis (DEA) and stochastic frontier analysis (SFA) for assessing efficiency in public hospitals during and after the COVID-19 pandemic is offered. The DEA method is a non-parametric method that requires no information other than the input and output quantities. SFA is a parametric method that considers stochastic noise in data and allows statistical testing of hypotheses about production structure and degree of inefficiency. The rationale for using these two competing approaches is to balance each method’s strengths, weaknesses and introduce a novel integrated approach. To show the applicability and efficacy of the proposed hybrid VRS-CRS-SFA (VCS) model, a case study is presented.

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Section: Literature Reviewmentioning

confidence: 99%

A Novel Hybrid Parametric and Non-Parametric Optimisation Model for Average Technical Efficiency Assessment in Public Hospitals during and Post-COVID-19 Pandemic

et al. 2021

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“…Artificial intelligence (AI) has been widely used in concrete technology in recent years to estimate the compressive strength of concrete. [17][18][19][20] One of the most common of these methods is known as Artificial neural network (ANN). [21][22][23][24] However, methods such as ANN are black-box methods, and although they can predict the response value with high accuracy, they are insufficient in terms of the model's explainability.…”

Section: Introductionmentioning

confidence: 99%

“…The general purpose of these methods is to determine the amount of components that can reach the targeted concrete compressive strength. Artificial intelligence (AI) has been widely used in concrete technology in recent years to estimate the compressive strength of concrete 17–20 . One of the most common of these methods is known as Artificial neural network (ANN) 21–24 .…”

Section: Introductionmentioning

confidence: 99%

A new intelligent sunflower optimization based explainable artificial intelligence approach for early‐age concrete compressive strength classification and mixture design of RAC

Ulucan

Yıldırım

Alataş

et al. 2023

Structural Concrete

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This study aims to develop a new artificial intelligence model that can produce explainable rules to predict the mix design and early‐age concrete compressive strength classes of recycled aggregate concrete (RAC). Unlike other black‐box machine learning methods and rule‐based algorithms, the study relies on a metaheuristic mechanism for explainability. This metaheuristic mechanism is not used for a traditional parameter optimization, but to automatically extract interpretable and interpretable rules from the experimental data. In the study, 30 series of RACs are produced, and the samples’ 1‐ and 3‐day early‐age concrete compressive strength values are determined. Concretes produced using these strength values are classified. The labels defined for each concrete class are Class A (C 8/10), Class B (C 12/15), Class C (C 16/20), and Class D (C 20/25). The proposed intelligent classification model that consists of rule set automatically produces interpretable and comprehensible rules from data to determine the early‐age concrete compressive strength class and RAC mix amounts. In addition, the proposed method eliminates the black‐box disadvantages of classical machine learning methods with its explainability and interpretability feature. The sunflower optimization algorithm is adapted as the metaheuristic mechanism and a special fitness function and representative solution form are developed for automatic extraction of high‐quality comprehensible rules by simultaneously optimizing many different metrics. This paper is the first interpretable and comprehensible artificial intelligence model attempt used for early‐age compressive strength classification and mixture design of recycled aggregate concrete by balancing and optimizing both the accuracy and explainability. Proposed explainable intelligent classification model is tested against both well‐known state‐of‐the‐art machine learning algorithms and standard rule‐based methods on the produced real data. Promising results in terms of accuracy, precision, recall are obtained along with the explainability feature.

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“…The performance of the steel‐concrete composite system depends on the behavior of shear connectors. Shear connectors are elements that create composite action, which significantly increases the load‐carrying capacity of the bridge and prevents the concrete slab from slipping against steel girders 2 . Shear connectors work as mechanical tools at the interface of materials to control the shear force at the steel‐concrete interface and provide a path to transfer the shear force 3–5 .…”

Section: Introductionmentioning

confidence: 99%

Predicting fatigue life of shear connectors in steel‐concrete composite bridges using artificial intelligence techniques

Roshanfar,

Ghiami Azad,

Forouzanfar

2023

Fatigue Fract Eng Mat Struct

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Fatigue limit states often govern the design of shear connectors in steel‐concrete composite bridges. AASHTO LRFD bridge design specifications provides a linear equation in a semi‐logarithmic S‐N curve for predicting the fatigue life of shear connectors. However, this equation can be too conservative in some cases, as supported by the available experimental data. In this paper, artificial intelligence (AI) was incorporated into the prediction of the fatigue life of shear connectors. Six different machine learning (ML) algorithms were considered for this purpose. The predictions of ML algorithms were compared both with the available experimental data and the equation provided by AASHTO. The results showed that the fatigue life predicted by ML methods is more accurate than that predicted by the current equation of AASHTO. The results of this study showed that AI can be a proper alternative to the existing methods for predicting the fatigue life of shear connectors.

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Simulation of steel–concrete composite floor system behavior at elevated temperatures via multi-hybrid metaheuristic framework

Cited by 27 publications

References 117 publications

A Novel Hybrid Parametric and Non-Parametric Optimisation Model for Average Technical Efficiency Assessment in Public Hospitals during and Post-COVID-19 Pandemic

A Novel Hybrid Parametric and Non-Parametric Optimisation Model for Average Technical Efficiency Assessment in Public Hospitals during and Post-COVID-19 Pandemic

A new intelligent sunflower optimization based explainable artificial intelligence approach for early‐age concrete compressive strength classification and mixture design of RAC

Predicting fatigue life of shear connectors in steel‐concrete composite bridges using artificial intelligence techniques

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