Sensitivity analysis and genetic algorithm-based shear capacity model for basalt FRC one-way slabs reinforced with BFRP bars

Al-Hamrani, Abathar; Wakjira, Tadesse G.; Alnahhal, Wael; Ebead, Usama

doi:10.1016/j.compstruct.2022.116473

Cited by 15 publications

(3 citation statements)

References 58 publications

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“…This study [7] compares different methods for optimal sensor placement in building foundation health monitoring systems. It evaluates approaches such as genetic algorithms, particle swarm optimization, and simulated annealing, considering factors such as coverage, redundancy, and cost-effectiveness.…”

Section: Literature Reviewmentioning

confidence: 99%

Identification and Repair of Structural Damage of Building Foundations Based on Genetic Algorithm

Yachun Hu

2024

jes

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The identification and repair of structural damage in building foundations are critical tasks in ensuring the safety and integrity of civil infrastructure. This research paper proposes a method based on genetic algorithms for the identification and repair of structural damage in building foundations. The proposed approach begins with the identification of structural damage by analysing sensor data, including vibration measurements and strain gauges, obtained from the building's foundation. A genetic algorithm is then employed to optimize the identification process by iteratively searching for the most likely damage scenarios based on the collected sensor data. Once the damage is identified, the algorithm proceeds to develop an optimal repair strategy. It considers various parameters, such as available repair materials, budget constraints, and desired structural performance, to generate a repair plan that minimizes cost and maximizes the restoration of structural integrity. The repair plan is optimized using the genetic algorithm to find the best combination of repair techniques and materials. To evaluate the effectiveness of the proposed method, extensive simulations and case studies are conducted on different types of building foundations. The performance of the algorithm is assessed in terms of its accuracy in identifying structural damage and the efficiency of the repair strategy.

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Section: Literature Reviewmentioning

confidence: 99%

Identification and Repair of Structural Damage of Building Foundations Based on Genetic Algorithm

Yachun Hu

2024

jes

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“…Given the prevalence, accessibility, and cost-effectiveness of steel tendons compared to FRP tendons, a majority of earlier research delved into Reinforced Concrete (RC) prestressed with steel tendons [ 42 , 43 ]. In contrast, studies on FRP tendons predominantly concentrated on scrutinizing their behavior and properties in slabs, beams, and girders [ [44] , [45] , [46] , [47] , [48] , [49] , [50] , [51] , [52] , [53] ].…”

Section: Introductionmentioning

confidence: 99%

Performances and properties of steel and composite prestressed tendons – A review

Rafieizonooz,

Jang,

Kim

et al. 2024

Heliyon

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“…The current investigations have demonstrated that the data-driven ML approaches could provide strength predictions with significantly high accuracy compared with the design provisions. However, limited research has been carried out, focusing on the retrofitting of RC structures with FRP [35][36][37][38][39][40][41][42][43][44]. In addition, normal ML approaches only rely on the initial input, but the shear prediction of FRP-reinforced concrete is a complex question which requires a deep understanding of the mechanism.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Shear Strength Prediction of FRP-Reinforced Concrete Beams without Stirrups Based on Machine Learning Methods

Yang

Liu

2023

Buildings

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Due to the intrinsic complexity, there has been no widely accepted mechanics-based estimation model of the shear performance of Fiber-Reinforced Polymer (FRP)-reinforced concrete beams. Capitalizing on a large amount of previous experimental data, data-driven machine learning (ML) models could be potentially suitable for addressing this problem. In this paper, four existing shear design provisions are reviewed and four typical ML models are analyzed. The accuracy of codified methods and ML models are compared and analyzed based on our established extensive database of FRP-reinforced concrete beams with rectangular cross sections. A series of artificially selected features considering the shear-carrying mechanisms of FRP-reinforced beams are incorporated into the proposed ML models to show their influence on the model validity. Bayesian optimization is utilized to automatically tune the hyperparameters of different ML models. Compared to the most satisfying codified predictions from CSA S806, the best ML model, XGBoost, can provide more accurate and consistent predictions for the database, with R2 enhanced by 15% and the MAE and RMSE reduced by 59% and 52%, respectively. With the selected features based on domain knowledge, the performance of ML models is further enhanced, shown by the most important features being the added ones. With outstanding performance on a large database and singular test, the ML approaches have great potential in guiding the shear design of FRP-reinforced concrete.

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Sensitivity analysis and genetic algorithm-based shear capacity model for basalt FRC one-way slabs reinforced with BFRP bars

Cited by 15 publications

References 58 publications

Identification and Repair of Structural Damage of Building Foundations Based on Genetic Algorithm

Identification and Repair of Structural Damage of Building Foundations Based on Genetic Algorithm

Performances and properties of steel and composite prestressed tendons – A review

Data-Driven Shear Strength Prediction of FRP-Reinforced Concrete Beams without Stirrups Based on Machine Learning Methods

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