سیویلیکا را در شبکه های اجتماعی دنبال نمایید.

Predictive Model of Bond Strength in Reinforced Concrete Structures: A Hybrid Metaheuristic-optimized Neural Network Approach

Publish Year: 1404
Type: Journal paper
Language: English
View: 45

This Paper With 23 Page And PDF Format Ready To Download

Export:

Link to this Paper:

Document National Code:

JR_IJE-38-5_019

Index date: 26 January 2025

Predictive Model of Bond Strength in Reinforced Concrete Structures: A Hybrid Metaheuristic-optimized Neural Network Approach abstract

Accurate estimation of bond strength between concrete and deformed reinforcing bars is essential for the stability of reinforced concrete structures, especially in critical regions subjected to heavy loads and environmental stresses. Despite intensive experimental studies revealing the complexity of factors influencing bond strength, existing predictive models, often reliant on artificial neural networks, have limitations in accuracy due to constrained datasets and inadequate representation of real-world stress fields. In response, this study pioneers a novel hybrid metaheuristic-optimized neural network model to swiftly and precisely predict bond strength under tensile load. Utilizing a comprehensive dataset comprising 558 valid experimental outcomes, seven metaheuristic algorithms are employed to optimize the ANN architecture. These metaheuristic algorithms include the Weighted Mean of Vectors, Grey Wolf Optimizer, Energy Valley Optimizer, Circle Search Algorithm, Artificial Ecosystem-Augmented Optimization, War Strategy Optimization, and Brown-Bear Optimization Algorithm. Results demonstrate that the developed hybrid models, particularly the artificial neural networks optimized by the Weighted Mean of Vectors algorithm, exhibit superior predictive performance. This model also demonstrated the lowest miscalibration value, followed by Circle Search Algorithm and Energy Valley Optimizer, indicating a high level of reliability. Moreover, comparison with common analytical and empirical formulations revealed significant performance improvements of the proposed model, achieving a 25% reduction in MSE during the testing phase. Additionally, the Shapley Additive explanations and Sobol sensitivity analysis framework was used to interpret the proposed predictive model, highlighting key predictors such as cross-sectional area, development length or splice, reinforcing bar diameter, and concrete compressive strength.

Predictive Model of Bond Strength in Reinforced Concrete Structures: A Hybrid Metaheuristic-optimized Neural Network Approach Keywords:

Predictive Model of Bond Strength in Reinforced Concrete Structures: A Hybrid Metaheuristic-optimized Neural Network Approach authors

N. Safaeian Hamzehkolaei

Department of Civil Engineering, Bozorgmehr University of Qaenat, Qaen, Iran

S. Ghavaminejad

Faculty of Engineering, Pardis Science and Technology Branch, Islamic Azad University, Pardis, Iran

M. S. Barkhordari

Department of Civil Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

مراجع و منابع این Paper:

لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :
Adnan A, Parung H, Tjaronge M, Djamaluddin R. Bond between ...
Soledispa CE, Pizarro PN, Massone LM. Optimizing reinforced concrete walls ...
Malozyomov BV, Martyushev NV, Babyr NV, Pogrebnoy AV, Efremenkov EA, ...
Barkhordari MS, Jawdhari A. Machine learning based prediction model for ...
Darwin D, Graham EK. Effect of deformation height and spacing ...
Morris GJ. Experimental evaluation of local bond behaviour of deformed ...
Ogura N, Bolander JE, Ichinose T. Analysis of bond splitting ...
نمایش کامل مراجع