دوره 16، شماره 2 - ( 1-1405 )
جلد 16 شماره 2 صفحات 299-285 |
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Talebi M, Ghodrati Amiri G. PREDICTION OF BRIDGE ACCELERATION RESPONSES USING AN ADAPTIVE AWAN FRAMEWORK. IJOCE 2026; 16 (2) :285-299
URL: http://ijoce.iust.ac.ir/article-1-678-fa.html
URL: http://ijoce.iust.ac.ir/article-1-678-fa.html
PREDICTION OF BRIDGE ACCELERATION RESPONSES USING AN ADAPTIVE AWAN FRAMEWORK. عنوان نشریه. 1405; 16 (2) :285-299
چکیده: (138 مشاهده)
Bridge Health Monitoring (BHM) plays a vital role in ensuring the safety, reliability, and long-term performance of bridge infrastructure. This study proposes an ARMA–Wavelet–Artificial Neural Network (AWAN) framework for predicting unmeasured bridge deck acceleration responses from limited sensor measurements. The proposed methodology integrates Auto-Regressive Moving Average (ARMA) modeling for temporal feature extraction, Continuous Wavelet Transform (CWT) for signal denoising, and a feed-forward Artificial Neural Network (ANN) for nonlinear response prediction. The combined framework exploits both spatial and short-term temporal correlations to achieve accurate response reconstruction while maintaining computational efficiency. The proposed framework was validated using three bridge models, including a simply supported beam, a two-span steel grid benchmark, and a scaled single-plane cable-stayed bridge. Prediction performance was evaluated using different statistical metrics under multiple loading scenarios. The results demonstrated excellent agreement between the predicted and measured acceleration responses, with higher prediction accuracy generally achieved at mid-span locations than near the supports, reflecting differences in local structural dynamics. In addition, the framework maintained stable performance under moderate temperature variation, demonstrating its robustness for practical bridge health monitoring applications. The proposed AWAN framework provides an efficient and reliable approach for reconstructing unmeasured structural responses while reducing sensor requirements. Its combination of prediction accuracy, computational efficiency, and robustness makes it a promising tool for data-driven bridge health monitoring and response reconstruction.
نوع مطالعه: پژوهشي |
موضوع مقاله:
Applications
دریافت: 1404/12/28 | پذیرش: 1405/3/1 | انتشار: 1405/3/2
دریافت: 1404/12/28 | پذیرش: 1405/3/1 | انتشار: 1405/3/2
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