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Fatigue Performance Prediction of RC Beams Based on Optimized Machine Learning Technology.
Li Song1,2, Lian Wang1, Hongshuo Sun1
1School of Civil Engineering, Central South University, Changsha 410082, China.
Materials (Basel, Switzerland)
|September 23, 2022
Summary
This study introduces a Particle Swarm Optimization-optimized Deep Belief Network (PSO-DBN) model for predicting reinforced concrete (RC) beam fatigue performance. The PSO-DBN model demonstrates superior accuracy and efficiency in forecasting fatigue damage compared to traditional methods.
Area of Science:
- Civil Engineering
- Structural Engineering
- Materials Science
Background:
- Fatigue damage in reinforced concrete (RC) beams is influenced by complex interactions between repetitive loads and material properties.
- Predicting the fatigue performance of RC beams is crucial for ensuring structural integrity and safety.
Purpose of the Study:
- To develop and evaluate an optimized deep belief network (DBN) model for predicting the fatigue performance of RC beams.
- To compare the predictive accuracy and efficiency of the proposed PSO-DBN model against single DBN and backpropagation (BP) models.
Main Methods:
- An original database of fatigue loading tests on RC beams was established.
- A fatigue performance prediction model for RC beams was developed using a deep belief network (DBN) optimized by particle swarm optimization (PSO).
- The model predicted mid-span deflection, reinforcement strain, and concrete strain during fatigue loading.
Main Results:
- The PSO-DBN model demonstrated higher accuracy and efficiency in predicting the fatigue performance of RC beams.
- Performance evaluation metrics including R-squared, mean absolute percentage error, mean absolute error, and root mean square error confirmed the superiority of the PSO-DBN model.
- The proposed model accurately predicted key fatigue indicators like deflection and strain.
Conclusions:
- The PSO-DBN model offers a more accurate and efficient approach for predicting the fatigue performance of reinforced concrete beams.
- This advanced model can aid in better structural health monitoring and maintenance planning for RC structures subjected to fatigue loading.
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