A fusion of neural, genetic and ensemble machine learning approaches for enhancing the engineering predictive capabilities of lightweight foamed reinforced concrete beam

Chen, Yang; Zeng, Jie; Jia, Jianping; Jabli, Mahjoub; Abdullah, Nermeen; Elattar, Samia; Khadimallah, Mohamed Amine; Marzouki, Riadh; Hashmi, Ahmed; Assilzadeh, Hamid

doi:10.1016/j.powtec.2024.119680

Cited by 5 publications

(1 citation statement)

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“…Bayane et al [14] integrated the three factors of strain, acceleration, and environmental change, proposing a real-time bridge damage detection method based on a machine learning algorithm. Chen et al [15], in their effort to predict and evaluate the performance of lightweight foamed concrete, constructed three machine learning models for comparative analysis. The findings revealed that the amalgamation of these three machine learning models yielded the highest accuracy.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Prediction and Evaluation for Structural Damage Comfort of Suspension Footbridge

Zhao,

Tang,

2024

Buildings

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To investigate the impact of structural damages on the comfort level of suspension footbridges under human-induced vibrations, this study addresses the limitations of traditional manual testing, which often entails significant manpower and material resources. The aim is to achieve rapid estimation and health monitoring of comfort levels during bridge operation. To accomplish this, the study combines finite-element simulation results to establish a data-driven library and introduces three distinct machine learning algorithms. Through comparative analysis, a machine learning-based method is proposed for quick evaluation of bridge comfort levels. Focusing on the Yangjiadong Suspension Bridge, the study evaluates and researches the comfort level of the structure under the influence of human-induced vibrations. The findings revealed a relatively low base frequency and high flexibility. Additionally, when considering the mass of individuals, peak acceleration decreased. The predictive performance of the Artificial Neural Network (ANN) model was found to be superior when accounting for multi-parameter damages, yielding root mean square error (RMSE), mean absolute percentage error (MAPE), and R-squared (R2) values of 0.03, 0.02, and 0.98, respectively. Moreover, the error ratio of the generalization performance analysis was below 5%. Furthermore, the study identified a damage coefficient of 0.13 for the bridge’s main cable, hanger, and steel longitudinal beam. Under a crowd density of 0.5 people per square meter, the predicted peak acceleration was 1.098 m/s2, with a model error of less than 10% compared to the observed value of 1.004 m/s2. These results underscore the model’s effectiveness in swiftly evaluating bridge comfort levels, thereby offering valuable insights for the health monitoring of bridge comfort levels.

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Section: Introductionmentioning

confidence: 99%